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Diffstat (limited to '')
19 files changed, 2509 insertions, 766 deletions
diff --git a/notebooks/FIR_mehrere_V2.ipynb b/notebooks/FIR_mehrere_V2.ipynb new file mode 100644 index 0000000..6d07670 --- /dev/null +++ b/notebooks/FIR_mehrere_V2.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d828ae1c", + "metadata": {}, + "source": [ + "# FIR Filter Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9aed9f51", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "from numpy.fft import fft,ifft,fftshift\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9a16a6d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Anfangs werte noch variabel machen \n", + "delays = [3,5,2]\n", + "ampl = [0.2,0.5,0.8]" + ] + }, + { + "cell_type": "markdown", + "id": "5ad0edf9", + "metadata": {}, + "source": [ + "# Dealy Window\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bf92d73a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "d =0\n", + "k = np.linspace(-5,10,1000)\n", + "h_ideal = np.sinc(k-d)\n", + "plt.plot(k,h_ideal)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dd93e444", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n = 4.5\n", + "window_sample= np.arange(np.round(d-(n-1)/2),np.round(d+(n+1)/2))\n", + "window = []\n", + "for ws in window_sample: \n", + " idx = (np.abs(k-ws)).argmin()\n", + " window.append(idx)\n", + " \n", + "plt.plot(k,h_ideal)\n", + "plt.plot(window_sample,h_ideal[window],\"-*\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "12b0c2d2", + "metadata": {}, + "source": [ + "# FIR" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "efc93b69", + "metadata": {}, + "outputs": [], + "source": [ + "delay = 3\n", + "amplitude = 1\n", + "\n", + "d_int = int(np.floor(delay))\n", + "#d_frac = delay - d_int\n", + "\n", + "h_int = np.concatenate([np.zeros(d_int-1), [amplitude], np.zeros(3)])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "43372541", + "metadata": {}, + "outputs": [], + "source": [ + "#H_int = fft(h_int)\n", + "#h = ifft(H_int)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a32cb580", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<StemContainer object of 3 artists>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "test = np.random.randint(0, 10, size = 20)# Signal\n", + "\n", + "y = np.convolve(test.real, h_int)#Faltung\n", + "\n", + "plt.stem(test, linefmt=\"C0-\")\n", + "plt.stem(y, linefmt='C1-')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "39fb3489", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 5, 5, 9, 0, 3, 6, 7, 7, 9, 5, 9, 4, 9, 5, 9, 6, 2, 2, 5])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7fd06347", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 1., 0., 0., 0.])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_int" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a9612999", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 2., 5., 5., 9., 0., 3., 6., 7., 7., 9., 5., 9., 4., 9., 5.,\n", + " 9., 6., 2., 2., 5., 0., 0., 0.])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "markdown", + "id": "706a51b1", + "metadata": {}, + "source": [ + "# FIR mit Delay \n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "79e18131", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tap with amplitude=1, delay=5\n", + "Creating filter of order N=11.0\n", + "[ 1.00000000e+00 -3.89817183e-17 3.89817183e-17 -3.89817183e-17\n", + " 3.89817183e-17 1.00000000e+00 3.89817183e-17 -3.89817183e-17\n", + " 3.89817183e-17 -3.89817183e-17 3.89817183e-17 -3.89817183e-17]\n" + ] + }, + { + "data": { + "image/png": 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NfKiqrga+w5D+k32mzrnkjcAq4HLg6Ul+a7BV6WyGLdCbuxl1tySXMB3mH6+qHYOup49eAfxKkv9g+jTZK5P81WBL6qtJYLKqTv2L6hNMB3wLXg18o6qmquoJYAfwcwOuaTH8V5LLADrfHxlwPedk2AL9BzesTnIp02/O7BxwTX3RuS/rR4CHquovBl1PP1XVHVW1oqpWMv07+1xVNXOUV1X/CRxJcur27a8CHhxgSf10GHhZkqd19tFX0cgbvjPsBN7Uefwm4FMDrOWc9XRP0aeKqjqZ5DZgN/9/w+r9Ay6rX14BvAHYl+Qrnb53du7nqqe+twEf7xxoHAJuHnA9fVFVX07yCeABpq/E2suQf0w+yb3AtcDyJJPAu4D3An+T5C1M/xH79cFVeO786L8kNWLYTrlIks7CQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmN+D9RkrLuTrwbkAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "delay = 5\n", + "ampl = 1\n", + "print(f\"Tap with amplitude={ampl}, delay={delay}\")\n", + "\n", + " \n", + "order = 2 * np.floor(delay) + 1 #N\n", + "print(f\"Creating filter of order N={order}\")\n", + "\n", + "\n", + "skip = np.floor(delay) - (order - 1) / 2 #M\n", + "assert skip >= 0\n", + "\n", + "samples = np.arange(0, order+1)\n", + "\n", + "\n", + "h = ampl*(np.sinc(samples-delay)) #sinc\n", + "\n", + "\n", + "h[0] = 1\n", + "\n", + "print(h)\n", + "\n", + "plt.stem(samples, h)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c89c83ae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f5b33445e50>]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = np.arange(200)\n", + "\n", + "signal = np.sin(2 * np.pi * t * 0.05)\n", + "\n", + "signal_shifted = np.convolve(h, signal, mode='full')\n", + "\n", + "plt.plot(t, signal)\n", + "plt.grid(True)\n", + "plt.show()\n", + "plt.plot(signal_shifted)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d51d8a62", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "delay = [5,6.2]\n", + "ampl = [4,1]\n", + "print(f\"Tap with amplitude={ampl}, delay={delay}\")\n", + "\n", + "max_order = 2 * np.floor(np.max(delay)) + 1\n", + "max_samples = np.arange(0, max_order +1) \n", + "max_len = len(max_samples)\n", + "print(max_len)\n", + "\n", + "sum_x = np.zeros(int(max_len))\n", + "\n", + "for (a,d) in zip(ampl,delay):\n", + " \n", + " order = 2 * np.floor(d) + 1\n", + " \n", + " skip = np.floor(d) - (order - 1) / 2 #M sollte immer 0 sein \n", + " assert skip >= 0\n", + "\n", + " samples = np.arange(0, order+1 ) \n", + "\n", + " h = a*(np.sinc(samples-d)) #sinc\n", + " h_len = np.concatenate([h, np.zeros(max_len-len(h))])\n", + " print(h_len)\n", + " sum_x += h_len\n", + " \n", + "\n", + "sum_x[0] = 1\n", + "print(sum_x)\n", + "\n", + "\n", + "\n", + "plt.stem(max_samples, sum_x)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81b4ff57", + "metadata": {}, + "outputs": [], + "source": [ + "delay = [5,6]\n", + "np.max(delay)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "330915ff", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/FrameSynchronization.ipynb b/notebooks/FrameSynchronization.ipynb index e0187b2..911ddd6 100644 --- a/notebooks/FrameSynchronization.ipynb +++ b/notebooks/FrameSynchronization.ipynb @@ -8,10 +8,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "from numpy_ringbuffer import RingBuffer\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.image as mpimg" + "import matplotlib.pyplot as plt" ] }, { @@ -23,39 +20,143 @@ ] }, { + "cell_type": "markdown", + "id": "fc118de2-937b-4157-a5a1-9b7f152bf59b", + "metadata": {}, + "source": [ + "First we need to create the access code, a barker sequence which has a very good autocorrelation." + ] + }, + { "cell_type": "code", - "execution_count": 27, - "id": "025c6919", - "metadata": { - "scrolled": false - }, + "execution_count": 2, + "id": "20dcd173-2889-4e21-9746-3d0a0ab060e8", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Header (N=16): [1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1]\n", - "Stream (N=80): [0 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1\n", - " 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 1 0\n", - " 0 1 1 1 0 1]\n", - "Correlation peak value: 16 at i=47\n" + "Access code: 13 bit pattern [1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1] = left padded bytes [31, 53]\n" ] - }, + } + ], + "source": [ + "ac = [ 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, ]\n", + "\n", + "ac_pad = [0] * int(8 * np.ceil(len(ac) / 8) - len(ac)) + ac\n", + "ac_bytes = list(np.packbits(ac_pad))\n", + "\n", + "print(f\"Access code: {len(ac)} bit pattern {ac} = left padded bytes {ac_bytes}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5cfd3c9a-d697-4462-bc26-26e82d25cbfd", + "metadata": {}, + "source": [ + "To correlate with the access code we need its symbols, thus the functions to modulate the access code." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "515891a9-dbd2-4088-9a41-b792d878f0fc", + "metadata": {}, + "outputs": [], + "source": [ + "def modulate_qpsk(m):\n", + " ampl = np.sqrt(2)\n", + " sym = {\n", + " 0: ampl * (-1 -1j),\n", + " 1: ampl * ( 1 -1j),\n", + " 2: ampl * (-1 +1j),\n", + " 3: ampl * ( 1 +1j)\n", + " }\n", + "\n", + " return map(lambda k: sym[k], m)" + ] + }, + { + "cell_type": "markdown", + "id": "d328e765-e977-4de9-9694-35012193a0aa", + "metadata": {}, + "source": [ + "### Symbols for QPSK" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3a4c834f-7012-4e22-b9f3-865527d09c02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modulate chunks [0, 1, 3, 3, 0, 3, 1, 1] with QPSK into 8 symbols:\n", + "[(-1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (-1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j)]\n", + "\n", + "Reversed complex conjugate list for FIR filter:\n", + "[(1.4142135623730951+1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (-1.4142135623730951+1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (-1.4142135623730951+1.4142135623730951j)]\n" + ] + } + ], + "source": [ + "# convert into chunks of 2 bits for QPSK\n", + "chunks = list(np.matmul(np.array(ac_pad).reshape((-1,2)), np.array([2, 1])))\n", + "syms = list(modulate_qpsk(chunks))\n", + "print(f\"Modulate chunks {chunks} with QPSK into {len(syms)} symbols:\\n{syms}\\n\")\n", + "\n", + "fir_syms = list(np.conj(syms[::-1]))\n", + "print(f\"Reversed complex conjugate list for FIR filter:\\n{fir_syms}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6155b2e-9405-4d20-8c05-72af45575a04", + "metadata": {}, + "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "<StemContainer object of 3 artists>" + "<Figure size 1440x288 with 2 Axes>" ] }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (left, right) = plt.subplots(1, 2, figsize = (20, 4))\n", + "\n", + "left.plot(np.real(syms), \".-\")\n", + "left.plot(np.imag(syms), \".-\")\n", + "left.set_title(\"Access Code\")\n", + "\n", + "right.plot(np.real(fir_syms), \".-\")\n", + "right.plot(np.imag(fir_syms), \".-\")\n", + "right.set_title(\"FIR Filter\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8163c2e0-0652-43c5-ad32-eaf0e94a638e", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "<Figure size 1800x360 with 1 Axes>" + "<Figure size 1440x288 with 2 Axes>" ] }, "metadata": { @@ -65,47 +166,57 @@ } ], "source": [ - "# Create test data\n", - "seq = np.unpackbits(np.array([0xbe, 0xef], dtype=np.dtype(\"uint8\")))\n", - "stream = np.concatenate([\n", - " np.random.randint(low=0, high=2, size=32), seq, np.random.randint(low=0, high=2, size=32)\n", - "])\n", - "\n", - "print(f\"Header (N={len(seq)}): {seq}\")\n", - "print(f\"Stream (N={len(stream)}): {stream}\")\n", - "\n", - "# Create buffers for cross correlation\n", - "fifo = RingBuffer(len(seq), dtype=np.dtype(\"uint8\"))\n", - "xcorr = RingBuffer(len(stream) + len(seq), dtype=np.dtype(\"uint8\"))\n", + "fig, (left, right) = plt.subplots(1, 2, figsize = (20, 4))\n", "\n", - "## fill FIFO with zeros\n", - "fifo.extend(np.zeros(fifo.maxlen))\n", + "left.plot(np.real(syms), np.imag(syms))\n", + "left.set_title(\"AC Constellation Diagram\")\n", "\n", - "def correlation(v):\n", - " n = len(seq)\n", - " d = np.logical_xor(v, seq) # or bitwise_xor, no difference in this case\n", - " return n - sum(d)\n", - " \n", - "for i in range(len(stream) + len(seq) + 1):\n", - " xcorr.append(correlation(np.array(fifo)))\n", - " \n", - " # append stream data\n", - " # if the stream is finished use zeros\n", - " fifo.append(stream[i] if i < len(stream) else 0)\n", + "xc = np.convolve(fir_syms, syms)\n", + "right.plot(np.abs(xc))\n", + "right.set_title(\"AC Autocorrelation\")\n", "\n", - "# unwrap values\n", - "xc = np.array(xcorr)\n", - "# print(f\"Cross correlation: {xc}\")\n", - "print(f\"Correlation peak value: {np.amax(xc)} at i={np.argmax(xc)}\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bc633da5-61b7-4569-adaf-3d019e0f1b17", + "metadata": {}, + "source": [ + "## Symbols for 16-QAM" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b56d782d-fcea-4fb9-b0c1-9c3933d5ce6c", + "metadata": {}, + "outputs": [], + "source": [ + "def modulate_16qam(m):\n", + " sym = {}\n", + " return map(lambda k: sym[k] if k in sym.keys() else None, m)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2fb687aa-0061-4626-bf6b-3ec7d628d739", + "metadata": {}, + "outputs": [], + "source": [ + "# convert into chunks of 4 bits for QPSK\n", + "# chunks = list(np.matmul(np.array(ac_pad).reshape((-1,4)), np.array([4, 3, 2, 1])))\n", + "# syms = list(modulate_16qam(chunks))\n", "\n", - "plt.figure(figsize = (25, 5))\n", - "plt.stem(xc)" + "# print(chunks)\n", + "# print(syms)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -119,7 +230,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.2" + "version": "3.8.11" } }, "nbformat": 4, diff --git a/notebooks/FIRDelay.ipynb b/notebooks/Test/FIRDelay.ipynb index 4576f88..4576f88 100644 --- a/notebooks/FIRDelay.ipynb +++ b/notebooks/Test/FIRDelay.ipynb diff --git a/notebooks/FIR_mehrere.ipynb b/notebooks/Test/FIR_mehrere.ipynb index cdd7e25..e7f2240 100644 --- a/notebooks/FIR_mehrere.ipynb +++ b/notebooks/Test/FIR_mehrere.ipynb @@ -361,16 +361,16 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "70acad87", + "execution_count": 28, + "id": "79e18131", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Tap with amplitude=1, delay=1.25\n", - "Creating filter of order N=3.0\n" + "Tap with amplitude=1, delay=7.5\n", + "Creating filter of order N=15.0\n" ] }, { @@ -379,13 +379,13 @@ "<StemContainer object of 3 artists>" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] @@ -398,7 +398,7 @@ ], "source": [ "\n", - "delay = 1.25\n", + "delay = 7.5\n", "ampl = 1\n", "print(f\"Tap with amplitude={ampl}, delay={delay}\")\n", "\n", @@ -540,7 +540,7 @@ }, { "cell_type": "markdown", - "id": "6fb85387", + "id": "892b32f7", "metadata": {}, "source": [ "# Test 2" @@ -610,7 +610,7 @@ { "cell_type": "code", "execution_count": null, - "id": "87e52ab7", + "id": "6ba475b6", "metadata": {}, "outputs": [], "source": [ @@ -620,7 +620,7 @@ { "cell_type": "code", "execution_count": null, - "id": "97f54131", + "id": "b1cfddba", "metadata": {}, "outputs": [], "source": [] @@ -628,7 +628,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cb6163b1", + "id": "5b4c5781", "metadata": {}, "outputs": [], "source": [] diff --git a/notebooks/Untitled.ipynb b/notebooks/Test/Untitled.ipynb index 0a1a81c..0a1a81c 100644 --- a/notebooks/Untitled.ipynb +++ b/notebooks/Test/Untitled.ipynb diff --git a/notebooks/Untitled1.ipynb b/notebooks/Untitled1.ipynb deleted file mode 100644 index 652e4fe..0000000 --- a/notebooks/Untitled1.ipynb +++ /dev/null @@ -1,33 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "f7f019cc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/Untitled2.ipynb b/notebooks/Untitled2.ipynb deleted file mode 100644 index 4515e16..0000000 --- a/notebooks/Untitled2.ipynb +++ /dev/null @@ -1,182 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "c9db320e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#!python\n", - "\n", - "from numpy import cos, sin, pi, absolute, arange\n", - "from scipy.signal import kaiserord, lfilter, firwin, freqz\n", - "from pylab import figure, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show\n", - "\n", - "\n", - "#------------------------------------------------\n", - "# Create a signal for demonstration.\n", - "#------------------------------------------------\n", - "\n", - "sample_rate = 100.0\n", - "nsamples = 400\n", - "t = arange(nsamples) / sample_rate\n", - "x = cos(2*pi*0.5*t) + 0.2*sin(2*pi*2.5*t+0.1) + \\\n", - " 0.2*sin(2*pi*15.3*t) + 0.1*sin(2*pi*16.7*t + 0.1) + \\\n", - " 0.1*sin(2*pi*23.45*t+.8)\n", - "\n", - "\n", - "#------------------------------------------------\n", - "# Create a FIR filter and apply it to x.\n", - "#------------------------------------------------\n", - "\n", - "# The Nyquist rate of the signal.\n", - "nyq_rate = sample_rate / 2.0\n", - "\n", - "# The desired width of the transition from pass to stop,\n", - "# relative to the Nyquist rate. We'll design the filter\n", - "# with a 5 Hz transition width.\n", - "width = 5.0/nyq_rate\n", - "\n", - "# The desired attenuation in the stop band, in dB.\n", - "ripple_db = 60.0\n", - "\n", - "# Compute the order and Kaiser parameter for the FIR filter.\n", - "N, beta = kaiserord(ripple_db, width)\n", - "\n", - "# The cutoff frequency of the filter.\n", - "cutoff_hz = 10.0\n", - "\n", - "# Use firwin with a Kaiser window to create a lowpass FIR filter.\n", - "taps = firwin(N, cutoff_hz/nyq_rate, window=('kaiser', beta))\n", - "\n", - "# Use lfilter to filter x with the FIR filter.\n", - "filtered_x = lfilter(taps, 1.0, x)\n", - "\n", - "#------------------------------------------------\n", - "# Plot the FIR filter coefficients.\n", - "#------------------------------------------------\n", - "\n", - "figure(1)\n", - "plot(taps, 'bo-', linewidth=2)\n", - "title('Filter Coefficients (%d taps)' % N)\n", - "grid(True)\n", - "\n", - "#------------------------------------------------\n", - "# Plot the magnitude response of the filter.\n", - "#------------------------------------------------\n", - "\n", - "figure(2)\n", - "clf()\n", - "w, h = freqz(taps, worN=8000)\n", - "plot((w/pi)*nyq_rate, absolute(h), linewidth=2)\n", - "xlabel('Frequency (Hz)')\n", - "ylabel('Gain')\n", - "title('Frequency Response')\n", - "ylim(-0.05, 1.05)\n", - "grid(True)\n", - "\n", - "# Upper inset plot.\n", - "ax1 = axes([0.42, 0.6, .45, .25])\n", - "plot((w/pi)*nyq_rate, absolute(h), linewidth=2)\n", - "xlim(0,8.0)\n", - "ylim(0.9985, 1.001)\n", - "grid(True)\n", - "\n", - "# Lower inset plot\n", - "ax2 = axes([0.42, 0.25, .45, .25])\n", - "plot((w/pi)*nyq_rate, absolute(h), linewidth=2)\n", - "xlim(12.0, 20.0)\n", - "ylim(0.0, 0.0025)\n", - "grid(True)\n", - "\n", - "#------------------------------------------------\n", - "# Plot the original and filtered signals.\n", - "#------------------------------------------------\n", - "\n", - "# The phase delay of the filtered signal.\n", - "delay = 0.5 * (N-1) / sample_rate\n", - "\n", - "figure(3)\n", - "# Plot the original signal.\n", - "plot(t, x)\n", - "# Plot the filtered signal, shifted to compensate for the phase delay.\n", - "plot(t-delay, filtered_x, 'r-')\n", - "# Plot just the \"good\" part of the filtered signal. The first N-1\n", - "# samples are \"corrupted\" by the initial conditions.\n", - "plot(t[N-1:]-delay, filtered_x[N-1:], 'g', linewidth=4)\n", - "\n", - "xlabel('t')\n", - "grid(True)\n", - "\n", - "show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9513b663", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/simulation/QAM_Fading/qam_fading_V2_eigerner_block.grc b/simulation/QAM_Fading/qam_fading_V2_eigerner_block.grc index 1ba7d1c..23cb577 100644 --- a/simulation/QAM_Fading/qam_fading_V2_eigerner_block.grc +++ b/simulation/QAM_Fading/qam_fading_V2_eigerner_block.grc @@ -808,9 +808,10 @@ blocks: parameters: affinity: '' alias: '' - amplitudes: '[0.2,0.2]' + amplitudes: '[0.2, 0.2]' comment: '' - delays: '[sps+1,sps+1]' + delays: '[4,4]' + los: 'True' maxoutbuf: '0' minoutbuf: '0' states: diff --git a/simulation/QAM_Fading/qam_fading_block.py b/simulation/QAM_Fading/qam_fading_block.py index 8186175..5622e69 100755 --- a/simulation/QAM_Fading/qam_fading_block.py +++ b/simulation/QAM_Fading/qam_fading_block.py @@ -491,7 +491,7 @@ class qam_fading_block(gr.top_block, Qt.QWidget): self.plots_grid_layout_0.setRowStretch(r, 1) for c in range(0, 1): self.plots_grid_layout_0.setColumnStretch(c, 1) - self.fadingui_multipath_fading_0 = fadingui.multipath_fading(amplitudes=[0.2,0.2], delays=[sps+1,sps+1]) + self.fadingui_multipath_fading_0 = fadingui.multipath_fading(amplitudes=[0.2, 0.2], delays=[4,4]) self._fading_1_range = Range(1, 30, 1, 2, 200) self._fading_1_win = RangeWidget(self._fading_1_range, self.set_fading_1, 'Fading', "counter_slider", int) self.params_grid_layout_2.addWidget(self._fading_1_win, 1, 0, 1, 1) diff --git a/src/gr-fadingui/grc/fadingui_multipath_fading.block.yml b/src/gr-fadingui/grc/fadingui_multipath_fading.block.yml index e116467..1d374ac 100644 --- a/src/gr-fadingui/grc/fadingui_multipath_fading.block.yml +++ b/src/gr-fadingui/grc/fadingui_multipath_fading.block.yml @@ -12,12 +12,19 @@ templates: # * label (label shown in the GUI) # * dtype (e.g. int, float, complex, byte, short, xxx_vector, ...) parameters: -- id: amplitudes - label: Amplitudes - dtype: raw - id: delays label: Delays + dtype: complex_vector +- id: amplitudes + label: Amplitudes dtype: raw +- id: los + label: LOS/NLOS + options: ['True', 'False'] + option_labels: ['LOS', 'NLOS'] + #default: 'False' + dtype: bool + #hide: ${ 'none' if los == 'False' else 'part' } # Make one 'inputs' list entry per input and one 'outputs' list entry per output. # Keys include: diff --git a/src/gr-fadingui/python/multipath_fading.py b/src/gr-fadingui/python/multipath_fading.py index 01f2dbe..02b3bb4 100644 --- a/src/gr-fadingui/python/multipath_fading.py +++ b/src/gr-fadingui/python/multipath_fading.py @@ -44,45 +44,74 @@ class multipath_fading(gr.sync_block): self.amplitudes = amplitudes self.delays = delays self.temp = [0] - # if los: - # self.amplitudes.append(1) - # self.delays.append(0) - self.los= 1 - #self.fir = + log.debug(los) #TO DO: True False unterscheidung + if los == True: + self.los = 1 + log.debug("Los True") + else: + self.los = 0 + log.debug("Los False") + + + + def work(self, input_items, output_items): """example: multiply with constant""" inp = input_items[0] oup = output_items[0] - if len(self.amplitudes) != len(self.delays): + if len(self.amplitudes) != len(self.delays): # Test: Es muss gleich viele Werte für Delays und Amplituden haben. raise Exception("Amplitudes and Delay length dont match") + + #TO DO negativ check + + + # raise Exception("Delay length can't be one") #if np.min(self.delays)<=1: # raise Exception("Delay length can't be one") - max_len = np.max(self.delays) - sum_x = np.zeros(max_len) - for(a,d) in zip(self.amplitudes,self.delays): + + #max_len = np.max(self.delays) #Max Werte herausfinden für länge + #sum_x = np.zeros(max_len) + + max_order = 2 * np.floor(np.max(self.delays)) + 1 + max_samples = np.arange(0, max_order +1) + max_len = len(max_samples) #Für Filter + + sum_x = np.zeros(int(max_len)) + + for (a,d) in zip(self.amplitudes,self.delays): # if d-1 <= 0: # x = np.concatenate([[a], np.zeros(max_len-1)]) - # else: - x = np.concatenate([np.zeros(d-1), [a], np.zeros(max_len-d)]) - sum_x += x + # else: + order = 2 * np.floor(d) + 1 + + skip = np.floor(d) - (order - 1) / 2 #M sollte immer 0 sein + assert skip >= 0 + + samples = np.arange(0, order +1) + + h = a*(np.sinc(samples-d)) #sinc + h_len = np.concatenate([h, np.zeros(max_len-len(h))]) + + sum_x += h_len + + #x = np.concatenate([np.zeros(d-1), [a], np.zeros(max_len-d)]) + #sum_x += x sum_x[0] = self.los - log.debug(sum_x) - - #H_int = fft(sum_x) - - #h = ifft(H_int) + #log.debug(sum_x) - #h[0]=1 y = np.convolve(inp, sum_x) + # signal_shifted = np.convolve(h, inp, mode='full') + # y = signal_shifted + + #y+=np.concatenate([self.temp,np.zeros(len(y)-len(self.temp))]) y+=np.concatenate([self.temp,np.zeros(len(y)-len(self.temp))]) - oup[:] = y[:len(inp)] self.temp = y[len(inp):] diff --git a/src/gui.py b/src/gui/gui.py index b2cbebb..b2cbebb 100755 --- a/src/gui.py +++ b/src/gui/gui.py diff --git a/src/net.py b/src/gui/net.py index 2c91bb8..2c91bb8 100644 --- a/src/net.py +++ b/src/gui/net.py diff --git a/tests/correlator/acgen.dat b/tests/correlator/acgen.dat Binary files differdeleted file mode 100644 index 537dcc5..0000000 --- a/tests/correlator/acgen.dat +++ /dev/null diff --git a/tests/correlator/acgen.grc b/tests/correlator/acgen.grc deleted file mode 100644 index ec7c1e8..0000000 --- a/tests/correlator/acgen.grc +++ /dev/null @@ -1,188 +0,0 @@ -options: - parameters: - author: Naoki Pross - catch_exceptions: 'True' - category: '[GRC Hier Blocks]' - cmake_opt: '' - comment: '' - copyright: '' - description: '' - gen_cmake: 'On' - gen_linking: dynamic - generate_options: no_gui - hier_block_src_path: '.:' - id: acgen - max_nouts: '0' - output_language: python - placement: (0,0) - qt_qss_theme: '' - realtime_scheduling: '' - run: 'True' - run_command: '{python} -u {filename}' - run_options: run - sizing_mode: fixed - thread_safe_setters: '' - title: Access Code Symbols Generator - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [8, 8] - rotation: 0 - state: enabled - -blocks: -- name: access_code - id: variable - parameters: - comment: '' - value: '[ 0xaa, 0xff, 0x0a ]' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [40, 292.0] - rotation: 0 - state: true -- name: const - id: variable_constellation - parameters: - comment: '' - const_points: '[-1-1j, -1+1j, 1+1j, 1-1j]' - dims: '1' - normalization: digital.constellation.AMPLITUDE_NORMALIZATION - precision: '8' - rot_sym: '4' - soft_dec_lut: None - sym_map: '[0, 1, 3, 2]' - type: qpsk - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [464, 124.0] - rotation: 0 - state: true -- name: excess_bw - id: variable - parameters: - comment: '' - value: '1' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [464, 308.0] - rotation: 0 - state: true -- name: samp_rate - id: variable - parameters: - comment: '' - value: '32000' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [200, 44.0] - rotation: 0 - state: enabled -- name: sps - id: variable - parameters: - comment: '' - value: '4' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [312, 44.0] - rotation: 0 - state: true -- name: blocks_file_sink_0 - id: blocks_file_sink - parameters: - affinity: '' - alias: '' - append: 'False' - comment: '' - file: acgen.dat - type: complex - unbuffered: 'False' - vlen: '1' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [752, 204.0] - rotation: 0 - state: true -- name: blocks_throttle_0 - id: blocks_throttle - parameters: - affinity: '' - alias: '' - comment: '' - ignoretag: 'True' - maxoutbuf: '0' - minoutbuf: '0' - samples_per_second: samp_rate - type: byte - vlen: '1' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [264, 220.0] - rotation: 0 - state: true -- name: blocks_vector_source_x_0 - id: blocks_vector_source_x - parameters: - affinity: '' - alias: '' - comment: '' - maxoutbuf: '0' - minoutbuf: '0' - repeat: 'False' - tags: '[]' - type: byte - vector: '[0x00] * 10 + access_code * 10' - vlen: '1' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [40, 204.0] - rotation: 0 - state: true -- name: digital_constellation_modulator_0 - id: digital_constellation_modulator - parameters: - affinity: '' - alias: '' - comment: '' - constellation: const - differential: 'False' - excess_bw: excess_bw - log: 'False' - maxoutbuf: '0' - minoutbuf: '0' - samples_per_symbol: sps - truncate: 'False' - verbose: 'False' - states: - bus_sink: false - bus_source: false - bus_structure: null - coordinate: [464, 196.0] - rotation: 0 - state: true - -connections: -- [blocks_throttle_0, '0', digital_constellation_modulator_0, '0'] -- [blocks_vector_source_x_0, '0', blocks_throttle_0, '0'] -- [digital_constellation_modulator_0, '0', blocks_file_sink_0, '0'] - -metadata: - file_format: 1 diff --git a/tests/correlator/acgen.py b/tests/correlator/acgen.py deleted file mode 100755 index 5fbdbb4..0000000 --- a/tests/correlator/acgen.py +++ /dev/null @@ -1,120 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -# -# SPDX-License-Identifier: GPL-3.0 -# -# GNU Radio Python Flow Graph -# Title: Access Code Symbols Generator -# Author: Naoki Pross -# GNU Radio version: 3.9.2.0 - -from gnuradio import blocks -from gnuradio import digital -from gnuradio import gr -from gnuradio.filter import firdes -from gnuradio.fft import window -import sys -import signal -from argparse import ArgumentParser -from gnuradio.eng_arg import eng_float, intx -from gnuradio import eng_notation - - - - -class acgen(gr.top_block): - - def __init__(self): - gr.top_block.__init__(self, "Access Code Symbols Generator", catch_exceptions=True) - - ################################################## - # Variables - ################################################## - self.sps = sps = 4 - self.samp_rate = samp_rate = 32000 - self.excess_bw = excess_bw = 1 - self.const = const = digital.constellation_qpsk().base() - self.access_code = access_code = [ 0xaa, 0xff, 0x0a ] - - ################################################## - # Blocks - ################################################## - self.digital_constellation_modulator_0 = digital.generic_mod( - constellation=const, - differential=False, - samples_per_symbol=sps, - pre_diff_code=True, - excess_bw=excess_bw, - verbose=False, - log=False, - truncate=False) - self.blocks_vector_source_x_0 = blocks.vector_source_b([0x00] * 10 + access_code * 10, False, 1, []) - self.blocks_throttle_0 = blocks.throttle(gr.sizeof_char*1, samp_rate,True) - self.blocks_file_sink_0 = blocks.file_sink(gr.sizeof_gr_complex*1, 'acgen.dat', False) - self.blocks_file_sink_0.set_unbuffered(False) - - - - ################################################## - # Connections - ################################################## - self.connect((self.blocks_throttle_0, 0), (self.digital_constellation_modulator_0, 0)) - self.connect((self.blocks_vector_source_x_0, 0), (self.blocks_throttle_0, 0)) - self.connect((self.digital_constellation_modulator_0, 0), (self.blocks_file_sink_0, 0)) - - - def get_sps(self): - return self.sps - - def set_sps(self, sps): - self.sps = sps - - def get_samp_rate(self): - return self.samp_rate - - def set_samp_rate(self, samp_rate): - self.samp_rate = samp_rate - self.blocks_throttle_0.set_sample_rate(self.samp_rate) - - def get_excess_bw(self): - return self.excess_bw - - def set_excess_bw(self, excess_bw): - self.excess_bw = excess_bw - - def get_const(self): - return self.const - - def set_const(self, const): - self.const = const - - def get_access_code(self): - return self.access_code - - def set_access_code(self, access_code): - self.access_code = access_code - self.blocks_vector_source_x_0.set_data([0x00] * 10 + self.access_code * 10, []) - - - - -def main(top_block_cls=acgen, options=None): - tb = top_block_cls() - - def sig_handler(sig=None, frame=None): - tb.stop() - tb.wait() - - sys.exit(0) - - signal.signal(signal.SIGINT, sig_handler) - signal.signal(signal.SIGTERM, sig_handler) - - tb.start() - - tb.wait() - - -if __name__ == '__main__': - main() diff --git a/tests/correlator/acproc.py b/tests/correlator/acproc.py deleted file mode 100755 index e119520..0000000 --- a/tests/correlator/acproc.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 - -import numpy as np -import matplotlib.pyplot as plt -import acgen - -# Parameters -# samples per symbol -sps = 4 -# number of initial bytes (to ignore) -nzeros = 10 -# length of the access code in bytes -aclen = 2 - -# Create samples -print("Modulating symbols") -acgen.main() - -# Extract one sequence -print("Extracting symbol sequence") - -# raw data -data = np.fromfile("acgen.dat", dtype=np.complex64) -plt.plot(data.real) -plt.plot(data.imag) -plt.title("Raw Data (time domain)") -plt.show() - -# take only symbols -symbols = data[1::sps] -plt.plot(symbols.real, symbols.imag) -plt.title("Symbols only (constellation)") -plt.show() - -# where ac symbols start, in symbols -ac_start = nzeros * 8 -ac_end = ac_start + aclen * 8 - -ac = symbols[ac_start:ac_end] - -fig, (ax1, ax2) = plt.subplots(2, 1) -fig.tight_layout() - -ax1.plot(ac.real, ac.imag) -ax1.set_title("Symbols of Access Code (constellation)") - -ax2.plot(ac.real, ".-") -ax2.plot(ac.imag, ".-") -ax2.set_title("Symbols of Access Code (time)") -plt.show() - -fir = list(np.conj(ac[::-1])) - -# print the symbols -print(f"Generated {len(ac)} symbols from a {aclen} byte sequence") -print("Reversed symbols (for FIR filter):") -print(fir) diff --git a/tests/correlator/correlator.grc b/tests/correlator/correlator.grc index cc57e18..9deec54 100644 --- a/tests/correlator/correlator.grc +++ b/tests/correlator/correlator.grc @@ -32,6 +32,40 @@ options: state: enabled blocks: +- name: access_code_symbols + id: variable + parameters: + comment: '' + value: '[(-1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), + (1.4142135623730951+1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), + (-1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), + (1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j)]' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [776, 1140.0] + rotation: 0 + state: enabled +- name: access_code_symbols_sps + id: variable + parameters: + comment: '' + value: '[(1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), + (1.4142197+1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), + (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), + (1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), + (-1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), + (1.4142197-1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), + (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), + (1.4142197+1.4142197j)]' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [224, 132.0] + rotation: 0 + state: enabled - name: const id: variable_constellation parameters: @@ -48,9 +82,9 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [432, 164.0] + coordinate: [592, 484.0] rotation: 0 - state: true + state: enabled - name: excess_bw id: variable parameters: @@ -60,9 +94,9 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [432, 340.0] + coordinate: [496, 484.0] rotation: 0 - state: true + state: enabled - name: nfilts id: variable parameters: @@ -72,9 +106,24 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [696, 356.0] + coordinate: [224, 988.0] rotation: 0 - state: true + state: enabled +- name: revconj_access_code_symbols + id: variable + parameters: + comment: '' + value: '[(1.4142135623730951+1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), + (1.4142135623730951-1.4142135623730951j), (-1.4142135623730951+1.4142135623730951j), + (1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), + (1.4142135623730951+1.4142135623730951j), (-1.4142135623730951+1.4142135623730951j)]' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [48, 564.0] + rotation: 0 + state: enabled - name: rrc_taps id: variable parameters: @@ -84,9 +133,9 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [768, 356.0] + coordinate: [304, 988.0] rotation: 0 - state: true + state: enabled - name: samp_rate id: variable parameters: @@ -96,7 +145,7 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [256, 52.0] + coordinate: [8, 116.0] rotation: 0 state: enabled - name: sps @@ -108,9 +157,21 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [352, 52.0] + coordinate: [104, 116.0] rotation: 0 - state: true + state: enabled +- name: testvec + id: variable + parameters: + comment: '' + value: '[31, 53] + [0x12, 0xe3, 0x9b, 0xee, 0x84, 0x23, 0x41, 0xf3]' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [48, 492.0] + rotation: 0 + state: enabled - name: timing_loop_bw id: variable parameters: @@ -120,11 +181,31 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [696, 420.0] + coordinate: [224, 1068.0] rotation: 0 - state: true -- name: blocks_complex_to_mag_0 - id: blocks_complex_to_mag + state: enabled +- name: blocks_burst_tagger_0 + id: blocks_burst_tagger + parameters: + affinity: '' + alias: '' + comment: '' + false_key: nothing + false_value: '0' + maxoutbuf: '0' + minoutbuf: '0' + true_key: peak + true_value: 'True' + type: float + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [944, 1580.0] + rotation: 0 + state: disabled +- name: blocks_char_to_short_0 + id: blocks_char_to_short parameters: affinity: '' alias: '' @@ -136,9 +217,93 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [1584, 80.0] + coordinate: [752, 1624.0] rotation: 0 - state: true + state: disabled +- name: blocks_complex_to_magphase_0 + id: blocks_complex_to_magphase + parameters: + affinity: '' + alias: '' + comment: '' + maxoutbuf: '0' + minoutbuf: '0' + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1048, 696.0] + rotation: 0 + state: enabled +- name: blocks_complex_to_magphase_0_0 + id: blocks_complex_to_magphase + parameters: + affinity: '' + alias: '' + comment: '' + maxoutbuf: '0' + minoutbuf: '0' + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1048, 1104.0] + rotation: 0 + state: enabled +- name: blocks_complex_to_magphase_0_1 + id: blocks_complex_to_magphase + parameters: + affinity: '' + alias: '' + comment: '' + maxoutbuf: '0' + minoutbuf: '0' + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [256, 1592.0] + rotation: 0 + state: disabled +- name: blocks_multiply_const_vxx_0 + id: blocks_multiply_const_vxx + parameters: + affinity: '' + alias: '' + comment: '' + const: 180 / 3.141592653589793 + maxoutbuf: '0' + minoutbuf: '0' + type: float + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1288, 724.0] + rotation: 0 + state: enabled +- name: blocks_multiply_const_vxx_0_0 + id: blocks_multiply_const_vxx + parameters: + affinity: '' + alias: '' + comment: '' + const: 180 / 3.141592653589793 + maxoutbuf: '0' + minoutbuf: '0' + type: float + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1336, 1180.0] + rotation: 0 + state: disabled - name: blocks_null_sink_0 id: blocks_null_sink parameters: @@ -153,9 +318,118 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [1528, 216.0] + coordinate: [1504, 1696.0] + rotation: 0 + state: disabled +- name: blocks_null_sink_2 + id: blocks_null_sink + parameters: + affinity: '' + alias: '' + bus_structure_sink: '[[0,],]' + comment: '' + num_inputs: '1' + type: float + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [528, 1720.0] + rotation: 0 + state: disabled +- name: blocks_null_sink_3 + id: blocks_null_sink + parameters: + affinity: '' + alias: '' + bus_structure_sink: '[[0,],]' + comment: '' + num_inputs: '1' + type: float + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1336, 1232.0] rotation: 0 state: true +- name: blocks_null_source_0 + id: blocks_null_source + parameters: + affinity: '' + alias: '' + bus_structure_source: '[[0,],]' + comment: '' + maxoutbuf: '0' + minoutbuf: '0' + num_outputs: '1' + type: byte + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [96, 344.0] + rotation: 0 + state: enabled +- name: blocks_peak_detector2_fb_0 + id: blocks_peak_detector2_fb + parameters: + affinity: '' + alias: '' + alpha: '0.001' + comment: '' + look_ahead: '1000' + maxoutbuf: '0' + minoutbuf: '0' + threshold_factor_rise: '7' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [528, 1620.0] + rotation: 0 + state: disabled +- name: blocks_stream_mux_0 + id: blocks_stream_mux + parameters: + affinity: '' + alias: '' + comment: '' + lengths: '[10, len(testvec)]' + maxoutbuf: '0' + minoutbuf: '0' + num_inputs: '2' + type: byte + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [288, 392.0] + rotation: 0 + state: enabled +- name: blocks_stream_mux_1 + id: blocks_stream_mux + parameters: + affinity: '' + alias: '' + comment: '' + lengths: '[len(access_code_symbols_sps), sps * (len(testvec) + 15)]' + maxoutbuf: '0' + minoutbuf: '0' + num_inputs: '2' + type: complex + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [776, 280.0] + rotation: 0 + state: disabled - name: blocks_throttle_0 id: blocks_throttle parameters: @@ -166,15 +440,15 @@ blocks: maxoutbuf: '0' minoutbuf: '0' samples_per_second: samp_rate - type: byte + type: complex vlen: '1' states: bus_sink: false bus_source: false bus_structure: null - coordinate: [248, 260.0] + coordinate: [1272, 404.0] rotation: 0 - state: true + state: enabled - name: blocks_vector_source_x_0 id: blocks_vector_source_x parameters: @@ -186,15 +460,56 @@ blocks: repeat: 'False' tags: '[]' type: byte - vector: ([0x00] * 10 + [0xaa, 0xff, 0x0a] + [0x00] * 10) * 20 + vector: testvec * 1600 vlen: '1' states: bus_sink: false bus_source: false bus_structure: null - coordinate: [16, 244.0] + coordinate: [48, 404.0] rotation: 0 - state: true + state: enabled +- name: blocks_vector_source_x_1 + id: blocks_vector_source_x + parameters: + affinity: '' + alias: '' + comment: '' + maxoutbuf: '0' + minoutbuf: '0' + repeat: 'True' + tags: '[]' + type: complex + vector: access_code_symbols_sps + vlen: '1' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [224, 204.0] + rotation: 0 + state: disabled +- name: channels_channel_model_0 + id: channels_channel_model + parameters: + affinity: '' + alias: '' + block_tags: 'False' + comment: '' + epsilon: '1.0' + freq_offset: '0.000001' + maxoutbuf: '0' + minoutbuf: '0' + noise_voltage: '0.2' + seed: '243' + taps: -1.4 + .4j + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [992, 364.0] + rotation: 0 + state: enabled - name: digital_cma_equalizer_cc_0 id: digital_cma_equalizer_cc parameters: @@ -211,7 +526,7 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [992, 188.0] + coordinate: [528, 812.0] rotation: 0 state: enabled - name: digital_constellation_decoder_cb_0 @@ -227,9 +542,9 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [1264, 212.0] + coordinate: [1232, 1692.0] rotation: 0 - state: true + state: disabled - name: digital_constellation_modulator_0 id: digital_constellation_modulator parameters: @@ -249,9 +564,29 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [432, 236.0] + coordinate: [496, 380.0] rotation: 0 - state: true + state: enabled +- name: digital_corr_est_cc_0 + id: digital_corr_est_cc + parameters: + affinity: '' + alias: '' + comment: '' + mark_delay: '0' + maxoutbuf: '0' + minoutbuf: '0' + sps: '1' + symbols: access_code_symbols + threshold: '.8' + threshold_method: digital.THRESHOLD_DYNAMIC + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [776, 1020.0] + rotation: 0 + state: enabled - name: digital_pfb_clock_sync_xxx_0 id: digital_pfb_clock_sync_xxx parameters: @@ -272,9 +607,9 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [696, 212.0] + coordinate: [224, 836.0] rotation: 0 - state: true + state: enabled - name: fir_filter_xxx_1 id: fir_filter_xxx parameters: @@ -285,19 +620,339 @@ blocks: maxoutbuf: '0' minoutbuf: '0' samp_delay: '0' - taps: '[(-1.4142197+1.4142197j), (-1.4142197+1.4142197j), (1.4142197-1.4142197j), - (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (-1.4142197-1.4142197j), - (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), - (-1.4142197-1.4142197j), (-1.4142197+1.4142197j), (-1.4142197+1.4142197j), (1.4142197-1.4142197j), - (1.4142197-1.4142197j)]' + taps: revconj_access_code_symbols type: ccc states: bus_sink: false bus_source: false bus_structure: null - coordinate: [1328, 68.0] + coordinate: [776, 828.0] + rotation: 0 + state: enabled +- name: high_pass_filter_0 + id: high_pass_filter + parameters: + affinity: '' + alias: '' + beta: '6.76' + comment: '' + cutoff_freq: 5e3 + decim: '1' + gain: '1' + interp: '1' + maxoutbuf: '0' + minoutbuf: '0' + samp_rate: samp_rate + type: fir_filter_fff + width: '.7' + win: window.WIN_HAMMING + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [528, 1420.0] + rotation: 0 + state: disabled +- name: low_pass_filter_0 + id: low_pass_filter + parameters: + affinity: '' + alias: '' + beta: '6.76' + comment: '' + cutoff_freq: 7e3 + decim: '1' + gain: '1' + interp: '1' + maxoutbuf: '0' + minoutbuf: '0' + samp_rate: samp_rate + type: fir_filter_fff + width: '.8' + win: window.WIN_HAMMING + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [528, 1252.0] + rotation: 0 + state: disabled +- name: qtgui_const_sink_x_0 + id: qtgui_const_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'False' + axislabels: 'True' + color1: '"blue"' + color10: '"red"' + color2: '"red"' + color3: '"red"' + color4: '"red"' + color5: '"red"' + color6: '"red"' + color7: '"red"' + color8: '"red"' + color9: '"red"' + comment: '' + grid: 'False' + gui_hint: 0,1,2,1 + label1: '' + label10: '' + label2: '' + label3: '' + label4: '' + label5: '' + label6: '' + label7: '' + label8: '' + label9: '' + legend: 'True' + marker1: '0' + marker10: '0' + marker2: '0' + marker3: '0' + marker4: '0' + marker5: '0' + marker6: '0' + marker7: '0' + marker8: '0' + marker9: '0' + name: '"Equalized Signal"' + nconnections: '1' + size: '1024' + style1: '0' + style10: '0' + style2: '0' + style3: '0' + style4: '0' + style5: '0' + style6: '0' + style7: '0' + style8: '0' + style9: '0' + tr_chan: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: complex + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + xmax: '2' + xmin: '-2' + ymax: '2' + ymin: '-2' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [776, 716.0] + rotation: 0 + state: enabled +- name: qtgui_const_sink_x_1 + id: qtgui_const_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '.5' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: '"blue"' + color10: '"red"' + color2: '"red"' + color3: '"red"' + color4: '"red"' + color5: '"red"' + color6: '"red"' + color7: '"red"' + color8: '"red"' + color9: '"red"' + comment: '' + grid: 'True' + gui_hint: 2,1,2,1 + label1: '' + label10: '' + label2: '' + label3: '' + label4: '' + label5: '' + label6: '' + label7: '' + label8: '' + label9: '' + legend: 'True' + marker1: '9' + marker10: '0' + marker2: '0' + marker3: '0' + marker4: '0' + marker5: '0' + marker6: '0' + marker7: '0' + marker8: '0' + marker9: '0' + name: '"Cross Correlation"' + nconnections: '1' + size: '1024' + style1: '2' + style10: '0' + style2: '0' + style3: '0' + style4: '0' + style5: '0' + style6: '0' + style7: '0' + style8: '0' + style9: '0' + tr_chan: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: complex + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + xmax: '2' + xmin: '-2' + ymax: '2' + ymin: '-2' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1048, 612.0] rotation: 0 state: enabled +- name: qtgui_const_sink_x_2 + id: qtgui_const_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'False' + axislabels: 'True' + color1: '"blue"' + color10: '"red"' + color2: '"red"' + color3: '"red"' + color4: '"red"' + color5: '"red"' + color6: '"red"' + color7: '"red"' + color8: '"red"' + color9: '"red"' + comment: '' + grid: 'False' + gui_hint: '2,1,2,1 ' + label1: '' + label10: '' + label2: '' + label3: '' + label4: '' + label5: '' + label6: '' + label7: '' + label8: '' + label9: '' + legend: 'True' + marker1: '0' + marker10: '0' + marker2: '0' + marker3: '0' + marker4: '0' + marker5: '0' + marker6: '0' + marker7: '0' + marker8: '0' + marker9: '0' + name: '"Phase Correction"' + nconnections: '1' + size: '1024' + style1: '0' + style10: '0' + style2: '0' + style3: '0' + style4: '0' + style5: '0' + style6: '0' + style7: '0' + style8: '0' + style9: '0' + tr_chan: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: complex + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + xmax: '2' + xmin: '-2' + ymax: '2' + ymin: '-2' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1248, 1588.0] + rotation: 0 + state: disabled - name: qtgui_time_sink_x_0 id: qtgui_time_sink_x parameters: @@ -313,7 +968,7 @@ blocks: alpha7: '1.0' alpha8: '1.0' alpha9: '1.0' - autoscale: 'False' + autoscale: 'True' axislabels: 'True' color1: blue color10: dark blue @@ -326,7 +981,104 @@ blocks: color8: dark red color9: dark green comment: '' - ctrlpanel: 'True' + ctrlpanel: 'False' + entags: 'True' + grid: 'False' + gui_hint: 2,0,1,1 + label1: Signal 1 + label10: Signal 10 + label2: Signal 2 + label3: Signal 3 + label4: Signal 4 + label5: Signal 5 + label6: Signal 6 + label7: Signal 7 + label8: Signal 8 + label9: Signal 9 + legend: 'True' + marker1: '-1' + marker10: '-1' + marker2: '-1' + marker3: '-1' + marker4: '-1' + marker5: '-1' + marker6: '-1' + marker7: '-1' + marker8: '-1' + marker9: '-1' + name: '""' + nconnections: '1' + size: '1024' + srate: samp_rate + stemplot: 'False' + style1: '1' + style10: '1' + style2: '1' + style3: '1' + style4: '1' + style5: '1' + style6: '1' + style7: '1' + style8: '1' + style9: '1' + tr_chan: '0' + tr_delay: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: float + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + ylabel: XC Magnitude + ymax: '20' + ymin: '0' + yunit: '""' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1320, 604.0] + rotation: 0 + state: enabled +- name: qtgui_time_sink_x_0_0 + id: qtgui_time_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: blue + color10: dark blue + color2: red + color3: green + color4: black + color5: cyan + color6: magenta + color7: yellow + color8: dark red + color9: dark green + comment: '' + ctrlpanel: 'False' entags: 'True' grid: 'False' gui_hint: '' @@ -384,17 +1136,114 @@ blocks: width7: '1' width8: '1' width9: '1' - ylabel: Amplitude - ymax: '50' + ylabel: XC Magnitude + ymax: '20' ymin: '0' yunit: '""' states: bus_sink: false bus_source: false bus_structure: null - coordinate: [1792, 60.0] + coordinate: [1336, 1084.0] rotation: 0 - state: true + state: enabled +- name: qtgui_time_sink_x_0_0_0 + id: qtgui_time_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: blue + color10: dark blue + color2: red + color3: green + color4: black + color5: cyan + color6: magenta + color7: yellow + color8: dark red + color9: dark green + comment: '' + ctrlpanel: 'False' + entags: 'True' + grid: 'False' + gui_hint: '' + label1: Signal 1 + label10: Signal 10 + label2: Signal 2 + label3: Signal 3 + label4: Signal 4 + label5: Signal 5 + label6: Signal 6 + label7: Signal 7 + label8: Signal 8 + label9: Signal 9 + legend: 'True' + marker1: '-1' + marker10: '-1' + marker2: '-1' + marker3: '-1' + marker4: '-1' + marker5: '-1' + marker6: '-1' + marker7: '-1' + marker8: '-1' + marker9: '-1' + name: '""' + nconnections: '1' + size: '1024' + srate: samp_rate + stemplot: 'False' + style1: '1' + style10: '1' + style2: '1' + style3: '1' + style4: '1' + style5: '1' + style6: '1' + style7: '1' + style8: '1' + style9: '1' + tr_chan: '0' + tr_delay: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: complex + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + ylabel: XC Magnitude + ymax: '2' + ymin: '-2' + yunit: '""' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1048, 1020.0] + rotation: 0 + state: enabled - name: qtgui_time_sink_x_1 id: qtgui_time_sink_x parameters: @@ -423,7 +1272,7 @@ blocks: color8: dark red color9: dark green comment: '' - ctrlpanel: 'True' + ctrlpanel: 'False' entags: 'True' grid: 'False' gui_hint: '' @@ -469,7 +1318,7 @@ blocks: tr_mode: qtgui.TRIG_MODE_FREE tr_slope: qtgui.TRIG_SLOPE_POS tr_tag: '""' - type: complex + type: float update_time: '0.10' width1: '1' width10: '1' @@ -482,6 +1331,200 @@ blocks: width8: '1' width9: '1' ylabel: Amplitude + ymax: '20' + ymin: '-5' + yunit: '""' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [784, 1452.0] + rotation: 0 + state: disabled +- name: qtgui_time_sink_x_1_0 + id: qtgui_time_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: blue + color10: dark blue + color2: red + color3: green + color4: black + color5: cyan + color6: magenta + color7: yellow + color8: dark red + color9: dark green + comment: '' + ctrlpanel: 'False' + entags: 'True' + grid: 'False' + gui_hint: 0,0,1,1 + label1: Signal 1 + label10: Signal 10 + label2: Signal 2 + label3: Signal 3 + label4: Signal 4 + label5: Signal 5 + label6: Signal 6 + label7: Signal 7 + label8: Signal 8 + label9: Signal 9 + legend: 'True' + marker1: '-1' + marker10: '-1' + marker2: '-1' + marker3: '-1' + marker4: '-1' + marker5: '-1' + marker6: '-1' + marker7: '-1' + marker8: '-1' + marker9: '-1' + name: '""' + nconnections: '1' + size: '1024' + srate: samp_rate + stemplot: 'False' + style1: '1' + style10: '1' + style2: '1' + style3: '1' + style4: '1' + style5: '1' + style6: '1' + style7: '1' + style8: '1' + style9: '1' + tr_chan: '0' + tr_delay: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: complex + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + ylabel: Modulated + ymax: '2' + ymin: '-2' + yunit: '""' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [808, 452.0] + rotation: 0 + state: enabled +- name: qtgui_time_sink_x_1_1 + id: qtgui_time_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: blue + color10: dark blue + color2: red + color3: green + color4: black + color5: cyan + color6: magenta + color7: yellow + color8: dark red + color9: dark green + comment: '' + ctrlpanel: 'False' + entags: 'True' + grid: 'True' + gui_hint: 1,0,1,1 + label1: Signal 1 + label10: Signal 10 + label2: Signal 2 + label3: Signal 3 + label4: Signal 4 + label5: Signal 5 + label6: Signal 6 + label7: Signal 7 + label8: Signal 8 + label9: Signal 9 + legend: 'True' + marker1: '-1' + marker10: '-1' + marker2: '-1' + marker3: '-1' + marker4: '-1' + marker5: '-1' + marker6: '-1' + marker7: '-1' + marker8: '-1' + marker9: '-1' + name: '""' + nconnections: '1' + size: '1024' + srate: samp_rate + stemplot: 'False' + style1: '1' + style10: '1' + style2: '1' + style3: '1' + style4: '1' + style5: '1' + style6: '1' + style7: '1' + style8: '1' + style9: '1' + tr_chan: '0' + tr_delay: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: complex + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + ylabel: Equalized ymax: '2' ymin: '-2' yunit: '""' @@ -489,21 +1532,347 @@ blocks: bus_sink: false bus_source: false bus_structure: null - coordinate: [696, 108.0] + coordinate: [776, 612.0] + rotation: 0 + state: enabled +- name: qtgui_time_sink_x_2 + id: qtgui_time_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: blue + color10: dark blue + color2: red + color3: green + color4: black + color5: cyan + color6: magenta + color7: yellow + color8: dark red + color9: dark green + comment: '' + ctrlpanel: 'False' + entags: 'True' + grid: 'False' + gui_hint: 3,0,1,1 + label1: Signal 1 + label10: Signal 10 + label2: Signal 2 + label3: Signal 3 + label4: Signal 4 + label5: Signal 5 + label6: Signal 6 + label7: Signal 7 + label8: Signal 8 + label9: Signal 9 + legend: 'True' + marker1: '-1' + marker10: '-1' + marker2: '-1' + marker3: '-1' + marker4: '-1' + marker5: '-1' + marker6: '-1' + marker7: '-1' + marker8: '-1' + marker9: '-1' + name: '""' + nconnections: '1' + size: '1024' + srate: samp_rate + stemplot: 'False' + style1: '1' + style10: '1' + style2: '1' + style3: '1' + style4: '1' + style5: '1' + style6: '1' + style7: '1' + style8: '1' + style9: '1' + tr_chan: '0' + tr_delay: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: float + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + ylabel: XC Phase + ymax: '1' + ymin: '-1' + yunit: '""' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1480, 708.0] + rotation: 0 + state: enabled +- name: qtgui_time_sink_x_2_0 + id: qtgui_time_sink_x + parameters: + affinity: '' + alias: '' + alpha1: '1.0' + alpha10: '1.0' + alpha2: '1.0' + alpha3: '1.0' + alpha4: '1.0' + alpha5: '1.0' + alpha6: '1.0' + alpha7: '1.0' + alpha8: '1.0' + alpha9: '1.0' + autoscale: 'True' + axislabels: 'True' + color1: blue + color10: dark blue + color2: red + color3: green + color4: black + color5: cyan + color6: magenta + color7: yellow + color8: dark red + color9: dark green + comment: '' + ctrlpanel: 'False' + entags: 'True' + grid: 'False' + gui_hint: '' + label1: Signal 1 + label10: Signal 10 + label2: Signal 2 + label3: Signal 3 + label4: Signal 4 + label5: Signal 5 + label6: Signal 6 + label7: Signal 7 + label8: Signal 8 + label9: Signal 9 + legend: 'True' + marker1: '-1' + marker10: '-1' + marker2: '-1' + marker3: '-1' + marker4: '-1' + marker5: '-1' + marker6: '-1' + marker7: '-1' + marker8: '-1' + marker9: '-1' + name: '""' + nconnections: '1' + size: '1024' + srate: samp_rate + stemplot: 'False' + style1: '1' + style10: '1' + style2: '1' + style3: '1' + style4: '1' + style5: '1' + style6: '1' + style7: '1' + style8: '1' + style9: '1' + tr_chan: '0' + tr_delay: '0' + tr_level: '0.0' + tr_mode: qtgui.TRIG_MODE_FREE + tr_slope: qtgui.TRIG_SLOPE_POS + tr_tag: '""' + type: float + update_time: '0.10' + width1: '1' + width10: '1' + width2: '1' + width3: '1' + width4: '1' + width5: '1' + width6: '1' + width7: '1' + width8: '1' + width9: '1' + ylabel: XC Phase + ymax: '1' + ymin: '-1' + yunit: '""' + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1512, 1164.0] + rotation: 0 + state: disabled +- name: root_raised_cosine_filter_0 + id: root_raised_cosine_filter + parameters: + affinity: '' + alias: '' + alpha: excess_bw + comment: '' + decim: '1' + gain: '2' + interp: '1' + maxoutbuf: '0' + minoutbuf: '0' + ntaps: 11*samp_rate + samp_rate: samp_rate + sym_rate: sps * samp_rate + type: fir_filter_ccf + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [496, 180.0] + rotation: 0 + state: disabled +- name: virtual_sink_0 + id: virtual_sink + parameters: + alias: '' + comment: '' + stream_id: envelope + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1480, 404.0] + rotation: 0 + state: enabled +- name: virtual_sink_1 + id: virtual_sink + parameters: + alias: '' + comment: '' + stream_id: symbols + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [776, 916.0] rotation: 0 state: true +- name: virtual_sink_2 + id: virtual_sink + parameters: + alias: '' + comment: '' + stream_id: xcorrelation + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [1080, 836.0] + rotation: 0 + state: true +- name: virtual_source_0 + id: virtual_source + parameters: + alias: '' + comment: '' + stream_id: envelope + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [32, 884.0] + rotation: 0 + state: enabled +- name: virtual_source_2 + id: virtual_source + parameters: + alias: '' + comment: '' + stream_id: symbols + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [32, 1716.0] + rotation: 0 + state: disabled +- name: virtual_source_3 + id: virtual_source + parameters: + alias: '' + comment: '' + stream_id: xcorrelation + states: + bus_sink: false + bus_source: false + bus_structure: null + coordinate: [32, 1604.0] + rotation: 0 + state: disabled connections: -- [blocks_complex_to_mag_0, '0', qtgui_time_sink_x_0, '0'] -- [blocks_throttle_0, '0', digital_constellation_modulator_0, '0'] -- [blocks_vector_source_x_0, '0', blocks_throttle_0, '0'] -- [digital_cma_equalizer_cc_0, '0', digital_constellation_decoder_cb_0, '0'] +- [blocks_char_to_short_0, '0', blocks_burst_tagger_0, '1'] +- [blocks_complex_to_magphase_0, '0', qtgui_time_sink_x_0, '0'] +- [blocks_complex_to_magphase_0, '1', blocks_multiply_const_vxx_0, '0'] +- [blocks_complex_to_magphase_0_0, '0', qtgui_time_sink_x_0_0, '0'] +- [blocks_complex_to_magphase_0_0, '1', blocks_multiply_const_vxx_0_0, '0'] +- [blocks_complex_to_magphase_0_0, '1', blocks_null_sink_3, '0'] +- [blocks_complex_to_magphase_0_1, '0', blocks_burst_tagger_0, '0'] +- [blocks_complex_to_magphase_0_1, '0', blocks_peak_detector2_fb_0, '0'] +- [blocks_complex_to_magphase_0_1, '0', high_pass_filter_0, '0'] +- [blocks_complex_to_magphase_0_1, '0', low_pass_filter_0, '0'] +- [blocks_complex_to_magphase_0_1, '1', blocks_null_sink_2, '0'] +- [blocks_multiply_const_vxx_0, '0', qtgui_time_sink_x_2, '0'] +- [blocks_multiply_const_vxx_0_0, '0', qtgui_time_sink_x_2_0, '0'] +- [blocks_null_source_0, '0', blocks_stream_mux_0, '0'] +- [blocks_peak_detector2_fb_0, '0', blocks_char_to_short_0, '0'] +- [blocks_stream_mux_0, '0', digital_constellation_modulator_0, '0'] +- [blocks_stream_mux_1, '0', channels_channel_model_0, '0'] +- [blocks_throttle_0, '0', virtual_sink_0, '0'] +- [blocks_vector_source_x_0, '0', blocks_stream_mux_0, '1'] +- [blocks_vector_source_x_1, '0', root_raised_cosine_filter_0, '0'] +- [channels_channel_model_0, '0', blocks_throttle_0, '0'] +- [digital_cma_equalizer_cc_0, '0', digital_corr_est_cc_0, '0'] - [digital_cma_equalizer_cc_0, '0', fir_filter_xxx_1, '0'] +- [digital_cma_equalizer_cc_0, '0', qtgui_const_sink_x_0, '0'] +- [digital_cma_equalizer_cc_0, '0', qtgui_time_sink_x_1_1, '0'] +- [digital_cma_equalizer_cc_0, '0', virtual_sink_1, '0'] - [digital_constellation_decoder_cb_0, '0', blocks_null_sink_0, '0'] -- [digital_constellation_modulator_0, '0', digital_pfb_clock_sync_xxx_0, '0'] -- [digital_constellation_modulator_0, '0', qtgui_time_sink_x_1, '0'] +- [digital_constellation_modulator_0, '0', blocks_stream_mux_1, '1'] +- [digital_constellation_modulator_0, '0', channels_channel_model_0, '0'] +- [digital_constellation_modulator_0, '0', qtgui_time_sink_x_1_0, '0'] +- [digital_corr_est_cc_0, '0', qtgui_time_sink_x_0_0_0, '0'] +- [digital_corr_est_cc_0, '1', blocks_complex_to_magphase_0_0, '0'] - [digital_pfb_clock_sync_xxx_0, '0', digital_cma_equalizer_cc_0, '0'] -- [fir_filter_xxx_1, '0', blocks_complex_to_mag_0, '0'] +- [fir_filter_xxx_1, '0', blocks_complex_to_magphase_0, '0'] +- [fir_filter_xxx_1, '0', qtgui_const_sink_x_1, '0'] +- [fir_filter_xxx_1, '0', virtual_sink_2, '0'] +- [high_pass_filter_0, '0', qtgui_time_sink_x_1, '0'] +- [low_pass_filter_0, '0', qtgui_time_sink_x_1, '0'] +- [root_raised_cosine_filter_0, '0', blocks_stream_mux_1, '0'] +- [virtual_source_0, '0', digital_pfb_clock_sync_xxx_0, '0'] +- [virtual_source_3, '0', blocks_complex_to_magphase_0_1, '0'] metadata: file_format: 1 diff --git a/tests/correlator/correlator.py b/tests/correlator/correlator.py index 79fa3f8..376d061 100755 --- a/tests/correlator/correlator.py +++ b/tests/correlator/correlator.py @@ -26,6 +26,7 @@ from gnuradio import qtgui from gnuradio.filter import firdes import sip from gnuradio import blocks +from gnuradio import channels from gnuradio import digital from gnuradio import filter from gnuradio import gr @@ -80,32 +81,143 @@ class correlator(gr.top_block, Qt.QWidget): self.nfilts = nfilts = 32 self.excess_bw = excess_bw = .35 self.timing_loop_bw = timing_loop_bw = 2 * 3.141592653589793 / 100 + self.testvec = testvec = [31, 53] + [0x12, 0xe3, 0x9b, 0xee, 0x84, 0x23, 0x41, 0xf3] self.samp_rate = samp_rate = 32000 self.rrc_taps = rrc_taps = firdes.root_raised_cosine(nfilts, nfilts, 1.0/float(sps), excess_bw, 45*nfilts) + self.revconj_access_code_symbols = revconj_access_code_symbols = [(1.4142135623730951+1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (-1.4142135623730951+1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (-1.4142135623730951+1.4142135623730951j)] self.const = const = digital.constellation_qpsk().base() + self.access_code_symbols_sps = access_code_symbols_sps = [(1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j), (1.4142197+1.4142197j)] + self.access_code_symbols = access_code_symbols = [(-1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (-1.4142135623730951-1.4142135623730951j), (1.4142135623730951+1.4142135623730951j), (1.4142135623730951-1.4142135623730951j), (1.4142135623730951-1.4142135623730951j)] ################################################## # Blocks ################################################## - self.qtgui_time_sink_x_1 = qtgui.time_sink_c( + self.qtgui_time_sink_x_2 = qtgui.time_sink_f( 1024, #size samp_rate, #samp_rate "", #name 1, #number of inputs None # parent ) - self.qtgui_time_sink_x_1.set_update_time(0.10) - self.qtgui_time_sink_x_1.set_y_axis(-2, 2) + self.qtgui_time_sink_x_2.set_update_time(0.10) + self.qtgui_time_sink_x_2.set_y_axis(-1, 1) - self.qtgui_time_sink_x_1.set_y_label('Amplitude', "") + self.qtgui_time_sink_x_2.set_y_label('XC Phase', "") - self.qtgui_time_sink_x_1.enable_tags(True) - self.qtgui_time_sink_x_1.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") - self.qtgui_time_sink_x_1.enable_autoscale(False) - self.qtgui_time_sink_x_1.enable_grid(False) - self.qtgui_time_sink_x_1.enable_axis_labels(True) - self.qtgui_time_sink_x_1.enable_control_panel(True) - self.qtgui_time_sink_x_1.enable_stem_plot(False) + self.qtgui_time_sink_x_2.enable_tags(True) + self.qtgui_time_sink_x_2.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") + self.qtgui_time_sink_x_2.enable_autoscale(True) + self.qtgui_time_sink_x_2.enable_grid(False) + self.qtgui_time_sink_x_2.enable_axis_labels(True) + self.qtgui_time_sink_x_2.enable_control_panel(False) + self.qtgui_time_sink_x_2.enable_stem_plot(False) + + + labels = ['Signal 1', 'Signal 2', 'Signal 3', 'Signal 4', 'Signal 5', + 'Signal 6', 'Signal 7', 'Signal 8', 'Signal 9', 'Signal 10'] + widths = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + colors = ['blue', 'red', 'green', 'black', 'cyan', + 'magenta', 'yellow', 'dark red', 'dark green', 'dark blue'] + alphas = [1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0] + styles = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + markers = [-1, -1, -1, -1, -1, + -1, -1, -1, -1, -1] + + + for i in range(1): + if len(labels[i]) == 0: + self.qtgui_time_sink_x_2.set_line_label(i, "Data {0}".format(i)) + else: + self.qtgui_time_sink_x_2.set_line_label(i, labels[i]) + self.qtgui_time_sink_x_2.set_line_width(i, widths[i]) + self.qtgui_time_sink_x_2.set_line_color(i, colors[i]) + self.qtgui_time_sink_x_2.set_line_style(i, styles[i]) + self.qtgui_time_sink_x_2.set_line_marker(i, markers[i]) + self.qtgui_time_sink_x_2.set_line_alpha(i, alphas[i]) + + self._qtgui_time_sink_x_2_win = sip.wrapinstance(self.qtgui_time_sink_x_2.pyqwidget(), Qt.QWidget) + self.top_grid_layout.addWidget(self._qtgui_time_sink_x_2_win, 3, 0, 1, 1) + for r in range(3, 4): + self.top_grid_layout.setRowStretch(r, 1) + for c in range(0, 1): + self.top_grid_layout.setColumnStretch(c, 1) + self.qtgui_time_sink_x_1_1 = qtgui.time_sink_c( + 1024, #size + samp_rate, #samp_rate + "", #name + 1, #number of inputs + None # parent + ) + self.qtgui_time_sink_x_1_1.set_update_time(0.10) + self.qtgui_time_sink_x_1_1.set_y_axis(-2, 2) + + self.qtgui_time_sink_x_1_1.set_y_label('Equalized', "") + + self.qtgui_time_sink_x_1_1.enable_tags(True) + self.qtgui_time_sink_x_1_1.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") + self.qtgui_time_sink_x_1_1.enable_autoscale(True) + self.qtgui_time_sink_x_1_1.enable_grid(True) + self.qtgui_time_sink_x_1_1.enable_axis_labels(True) + self.qtgui_time_sink_x_1_1.enable_control_panel(False) + self.qtgui_time_sink_x_1_1.enable_stem_plot(False) + + + labels = ['Signal 1', 'Signal 2', 'Signal 3', 'Signal 4', 'Signal 5', + 'Signal 6', 'Signal 7', 'Signal 8', 'Signal 9', 'Signal 10'] + widths = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + colors = ['blue', 'red', 'green', 'black', 'cyan', + 'magenta', 'yellow', 'dark red', 'dark green', 'dark blue'] + alphas = [1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0] + styles = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + markers = [-1, -1, -1, -1, -1, + -1, -1, -1, -1, -1] + + + for i in range(2): + if len(labels[i]) == 0: + if (i % 2 == 0): + self.qtgui_time_sink_x_1_1.set_line_label(i, "Re{{Data {0}}}".format(i/2)) + else: + self.qtgui_time_sink_x_1_1.set_line_label(i, "Im{{Data {0}}}".format(i/2)) + else: + self.qtgui_time_sink_x_1_1.set_line_label(i, labels[i]) + self.qtgui_time_sink_x_1_1.set_line_width(i, widths[i]) + self.qtgui_time_sink_x_1_1.set_line_color(i, colors[i]) + self.qtgui_time_sink_x_1_1.set_line_style(i, styles[i]) + self.qtgui_time_sink_x_1_1.set_line_marker(i, markers[i]) + self.qtgui_time_sink_x_1_1.set_line_alpha(i, alphas[i]) + + self._qtgui_time_sink_x_1_1_win = sip.wrapinstance(self.qtgui_time_sink_x_1_1.pyqwidget(), Qt.QWidget) + self.top_grid_layout.addWidget(self._qtgui_time_sink_x_1_1_win, 1, 0, 1, 1) + for r in range(1, 2): + self.top_grid_layout.setRowStretch(r, 1) + for c in range(0, 1): + self.top_grid_layout.setColumnStretch(c, 1) + self.qtgui_time_sink_x_1_0 = qtgui.time_sink_c( + 1024, #size + samp_rate, #samp_rate + "", #name + 1, #number of inputs + None # parent + ) + self.qtgui_time_sink_x_1_0.set_update_time(0.10) + self.qtgui_time_sink_x_1_0.set_y_axis(-2, 2) + + self.qtgui_time_sink_x_1_0.set_y_label('Modulated', "") + + self.qtgui_time_sink_x_1_0.enable_tags(True) + self.qtgui_time_sink_x_1_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") + self.qtgui_time_sink_x_1_0.enable_autoscale(True) + self.qtgui_time_sink_x_1_0.enable_grid(False) + self.qtgui_time_sink_x_1_0.enable_axis_labels(True) + self.qtgui_time_sink_x_1_0.enable_control_panel(False) + self.qtgui_time_sink_x_1_0.enable_stem_plot(False) labels = ['Signal 1', 'Signal 2', 'Signal 3', 'Signal 4', 'Signal 5', @@ -125,19 +237,122 @@ class correlator(gr.top_block, Qt.QWidget): for i in range(2): if len(labels[i]) == 0: if (i % 2 == 0): - self.qtgui_time_sink_x_1.set_line_label(i, "Re{{Data {0}}}".format(i/2)) + self.qtgui_time_sink_x_1_0.set_line_label(i, "Re{{Data {0}}}".format(i/2)) else: - self.qtgui_time_sink_x_1.set_line_label(i, "Im{{Data {0}}}".format(i/2)) + self.qtgui_time_sink_x_1_0.set_line_label(i, "Im{{Data {0}}}".format(i/2)) else: - self.qtgui_time_sink_x_1.set_line_label(i, labels[i]) - self.qtgui_time_sink_x_1.set_line_width(i, widths[i]) - self.qtgui_time_sink_x_1.set_line_color(i, colors[i]) - self.qtgui_time_sink_x_1.set_line_style(i, styles[i]) - self.qtgui_time_sink_x_1.set_line_marker(i, markers[i]) - self.qtgui_time_sink_x_1.set_line_alpha(i, alphas[i]) - - self._qtgui_time_sink_x_1_win = sip.wrapinstance(self.qtgui_time_sink_x_1.pyqwidget(), Qt.QWidget) - self.top_layout.addWidget(self._qtgui_time_sink_x_1_win) + self.qtgui_time_sink_x_1_0.set_line_label(i, labels[i]) + self.qtgui_time_sink_x_1_0.set_line_width(i, widths[i]) + self.qtgui_time_sink_x_1_0.set_line_color(i, colors[i]) + self.qtgui_time_sink_x_1_0.set_line_style(i, styles[i]) + self.qtgui_time_sink_x_1_0.set_line_marker(i, markers[i]) + self.qtgui_time_sink_x_1_0.set_line_alpha(i, alphas[i]) + + self._qtgui_time_sink_x_1_0_win = sip.wrapinstance(self.qtgui_time_sink_x_1_0.pyqwidget(), Qt.QWidget) + self.top_grid_layout.addWidget(self._qtgui_time_sink_x_1_0_win, 0, 0, 1, 1) + for r in range(0, 1): + self.top_grid_layout.setRowStretch(r, 1) + for c in range(0, 1): + self.top_grid_layout.setColumnStretch(c, 1) + self.qtgui_time_sink_x_0_0_0 = qtgui.time_sink_c( + 1024, #size + samp_rate, #samp_rate + "", #name + 1, #number of inputs + None # parent + ) + self.qtgui_time_sink_x_0_0_0.set_update_time(0.10) + self.qtgui_time_sink_x_0_0_0.set_y_axis(-2, 2) + + self.qtgui_time_sink_x_0_0_0.set_y_label('XC Magnitude', "") + + self.qtgui_time_sink_x_0_0_0.enable_tags(True) + self.qtgui_time_sink_x_0_0_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") + self.qtgui_time_sink_x_0_0_0.enable_autoscale(True) + self.qtgui_time_sink_x_0_0_0.enable_grid(False) + self.qtgui_time_sink_x_0_0_0.enable_axis_labels(True) + self.qtgui_time_sink_x_0_0_0.enable_control_panel(False) + self.qtgui_time_sink_x_0_0_0.enable_stem_plot(False) + + + labels = ['Signal 1', 'Signal 2', 'Signal 3', 'Signal 4', 'Signal 5', + 'Signal 6', 'Signal 7', 'Signal 8', 'Signal 9', 'Signal 10'] + widths = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + colors = ['blue', 'red', 'green', 'black', 'cyan', + 'magenta', 'yellow', 'dark red', 'dark green', 'dark blue'] + alphas = [1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0] + styles = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + markers = [-1, -1, -1, -1, -1, + -1, -1, -1, -1, -1] + + + for i in range(2): + if len(labels[i]) == 0: + if (i % 2 == 0): + self.qtgui_time_sink_x_0_0_0.set_line_label(i, "Re{{Data {0}}}".format(i/2)) + else: + self.qtgui_time_sink_x_0_0_0.set_line_label(i, "Im{{Data {0}}}".format(i/2)) + else: + self.qtgui_time_sink_x_0_0_0.set_line_label(i, labels[i]) + self.qtgui_time_sink_x_0_0_0.set_line_width(i, widths[i]) + self.qtgui_time_sink_x_0_0_0.set_line_color(i, colors[i]) + self.qtgui_time_sink_x_0_0_0.set_line_style(i, styles[i]) + self.qtgui_time_sink_x_0_0_0.set_line_marker(i, markers[i]) + self.qtgui_time_sink_x_0_0_0.set_line_alpha(i, alphas[i]) + + self._qtgui_time_sink_x_0_0_0_win = sip.wrapinstance(self.qtgui_time_sink_x_0_0_0.pyqwidget(), Qt.QWidget) + self.top_layout.addWidget(self._qtgui_time_sink_x_0_0_0_win) + self.qtgui_time_sink_x_0_0 = qtgui.time_sink_f( + 1024, #size + samp_rate, #samp_rate + "", #name + 1, #number of inputs + None # parent + ) + self.qtgui_time_sink_x_0_0.set_update_time(0.10) + self.qtgui_time_sink_x_0_0.set_y_axis(0, 20) + + self.qtgui_time_sink_x_0_0.set_y_label('XC Magnitude', "") + + self.qtgui_time_sink_x_0_0.enable_tags(True) + self.qtgui_time_sink_x_0_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") + self.qtgui_time_sink_x_0_0.enable_autoscale(True) + self.qtgui_time_sink_x_0_0.enable_grid(False) + self.qtgui_time_sink_x_0_0.enable_axis_labels(True) + self.qtgui_time_sink_x_0_0.enable_control_panel(False) + self.qtgui_time_sink_x_0_0.enable_stem_plot(False) + + + labels = ['Signal 1', 'Signal 2', 'Signal 3', 'Signal 4', 'Signal 5', + 'Signal 6', 'Signal 7', 'Signal 8', 'Signal 9', 'Signal 10'] + widths = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + colors = ['blue', 'red', 'green', 'black', 'cyan', + 'magenta', 'yellow', 'dark red', 'dark green', 'dark blue'] + alphas = [1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0] + styles = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + markers = [-1, -1, -1, -1, -1, + -1, -1, -1, -1, -1] + + + for i in range(1): + if len(labels[i]) == 0: + self.qtgui_time_sink_x_0_0.set_line_label(i, "Data {0}".format(i)) + else: + self.qtgui_time_sink_x_0_0.set_line_label(i, labels[i]) + self.qtgui_time_sink_x_0_0.set_line_width(i, widths[i]) + self.qtgui_time_sink_x_0_0.set_line_color(i, colors[i]) + self.qtgui_time_sink_x_0_0.set_line_style(i, styles[i]) + self.qtgui_time_sink_x_0_0.set_line_marker(i, markers[i]) + self.qtgui_time_sink_x_0_0.set_line_alpha(i, alphas[i]) + + self._qtgui_time_sink_x_0_0_win = sip.wrapinstance(self.qtgui_time_sink_x_0_0.pyqwidget(), Qt.QWidget) + self.top_layout.addWidget(self._qtgui_time_sink_x_0_0_win) self.qtgui_time_sink_x_0 = qtgui.time_sink_f( 1024, #size samp_rate, #samp_rate @@ -146,16 +361,16 @@ class correlator(gr.top_block, Qt.QWidget): None # parent ) self.qtgui_time_sink_x_0.set_update_time(0.10) - self.qtgui_time_sink_x_0.set_y_axis(0, 50) + self.qtgui_time_sink_x_0.set_y_axis(0, 20) - self.qtgui_time_sink_x_0.set_y_label('Amplitude', "") + self.qtgui_time_sink_x_0.set_y_label('XC Magnitude', "") self.qtgui_time_sink_x_0.enable_tags(True) self.qtgui_time_sink_x_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, 0, "") - self.qtgui_time_sink_x_0.enable_autoscale(False) + self.qtgui_time_sink_x_0.enable_autoscale(True) self.qtgui_time_sink_x_0.enable_grid(False) self.qtgui_time_sink_x_0.enable_axis_labels(True) - self.qtgui_time_sink_x_0.enable_control_panel(True) + self.qtgui_time_sink_x_0.enable_control_panel(False) self.qtgui_time_sink_x_0.enable_stem_plot(False) @@ -185,10 +400,105 @@ class correlator(gr.top_block, Qt.QWidget): self.qtgui_time_sink_x_0.set_line_alpha(i, alphas[i]) self._qtgui_time_sink_x_0_win = sip.wrapinstance(self.qtgui_time_sink_x_0.pyqwidget(), Qt.QWidget) - self.top_layout.addWidget(self._qtgui_time_sink_x_0_win) - self.fir_filter_xxx_1 = filter.fir_filter_ccc(1, [(-1.4142197+1.4142197j), (-1.4142197+1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197-1.4142197j), (-1.4142197+1.4142197j), (-1.4142197+1.4142197j), (1.4142197-1.4142197j), (1.4142197-1.4142197j)]) + self.top_grid_layout.addWidget(self._qtgui_time_sink_x_0_win, 2, 0, 1, 1) + for r in range(2, 3): + self.top_grid_layout.setRowStretch(r, 1) + for c in range(0, 1): + self.top_grid_layout.setColumnStretch(c, 1) + self.qtgui_const_sink_x_1 = qtgui.const_sink_c( + 1024, #size + "Cross Correlation", #name + 1, #number of inputs + None # parent + ) + self.qtgui_const_sink_x_1.set_update_time(0.10) + self.qtgui_const_sink_x_1.set_y_axis(-2, 2) + self.qtgui_const_sink_x_1.set_x_axis(-2, 2) + self.qtgui_const_sink_x_1.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, "") + self.qtgui_const_sink_x_1.enable_autoscale(True) + self.qtgui_const_sink_x_1.enable_grid(True) + self.qtgui_const_sink_x_1.enable_axis_labels(True) + + + labels = ['', '', '', '', '', + '', '', '', '', ''] + widths = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + colors = ["blue", "red", "red", "red", "red", + "red", "red", "red", "red", "red"] + styles = [2, 0, 0, 0, 0, + 0, 0, 0, 0, 0] + markers = [9, 0, 0, 0, 0, + 0, 0, 0, 0, 0] + alphas = [.5, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0] + + for i in range(1): + if len(labels[i]) == 0: + self.qtgui_const_sink_x_1.set_line_label(i, "Data {0}".format(i)) + else: + self.qtgui_const_sink_x_1.set_line_label(i, labels[i]) + self.qtgui_const_sink_x_1.set_line_width(i, widths[i]) + self.qtgui_const_sink_x_1.set_line_color(i, colors[i]) + self.qtgui_const_sink_x_1.set_line_style(i, styles[i]) + self.qtgui_const_sink_x_1.set_line_marker(i, markers[i]) + self.qtgui_const_sink_x_1.set_line_alpha(i, alphas[i]) + + self._qtgui_const_sink_x_1_win = sip.wrapinstance(self.qtgui_const_sink_x_1.pyqwidget(), Qt.QWidget) + self.top_grid_layout.addWidget(self._qtgui_const_sink_x_1_win, 2, 1, 2, 1) + for r in range(2, 4): + self.top_grid_layout.setRowStretch(r, 1) + for c in range(1, 2): + self.top_grid_layout.setColumnStretch(c, 1) + self.qtgui_const_sink_x_0 = qtgui.const_sink_c( + 1024, #size + "Equalized Signal", #name + 1, #number of inputs + None # parent + ) + self.qtgui_const_sink_x_0.set_update_time(0.10) + self.qtgui_const_sink_x_0.set_y_axis(-2, 2) + self.qtgui_const_sink_x_0.set_x_axis(-2, 2) + self.qtgui_const_sink_x_0.set_trigger_mode(qtgui.TRIG_MODE_FREE, qtgui.TRIG_SLOPE_POS, 0.0, 0, "") + self.qtgui_const_sink_x_0.enable_autoscale(False) + self.qtgui_const_sink_x_0.enable_grid(False) + self.qtgui_const_sink_x_0.enable_axis_labels(True) + + + labels = ['', '', '', '', '', + '', '', '', '', ''] + widths = [1, 1, 1, 1, 1, + 1, 1, 1, 1, 1] + colors = ["blue", "red", "red", "red", "red", + "red", "red", "red", "red", "red"] + styles = [0, 0, 0, 0, 0, + 0, 0, 0, 0, 0] + markers = [0, 0, 0, 0, 0, + 0, 0, 0, 0, 0] + alphas = [1.0, 1.0, 1.0, 1.0, 1.0, + 1.0, 1.0, 1.0, 1.0, 1.0] + + for i in range(1): + if len(labels[i]) == 0: + self.qtgui_const_sink_x_0.set_line_label(i, "Data {0}".format(i)) + else: + self.qtgui_const_sink_x_0.set_line_label(i, labels[i]) + self.qtgui_const_sink_x_0.set_line_width(i, widths[i]) + self.qtgui_const_sink_x_0.set_line_color(i, colors[i]) + self.qtgui_const_sink_x_0.set_line_style(i, styles[i]) + self.qtgui_const_sink_x_0.set_line_marker(i, markers[i]) + self.qtgui_const_sink_x_0.set_line_alpha(i, alphas[i]) + + self._qtgui_const_sink_x_0_win = sip.wrapinstance(self.qtgui_const_sink_x_0.pyqwidget(), Qt.QWidget) + self.top_grid_layout.addWidget(self._qtgui_const_sink_x_0_win, 0, 1, 2, 1) + for r in range(0, 2): + self.top_grid_layout.setRowStretch(r, 1) + for c in range(1, 2): + self.top_grid_layout.setColumnStretch(c, 1) + self.fir_filter_xxx_1 = filter.fir_filter_ccc(1, revconj_access_code_symbols) self.fir_filter_xxx_1.declare_sample_delay(0) self.digital_pfb_clock_sync_xxx_0 = digital.pfb_clock_sync_ccf(sps, timing_loop_bw, rrc_taps, nfilts, 16, 1.5, 1) + self.digital_corr_est_cc_0 = digital.corr_est_cc(access_code_symbols, 1, 0, .8, digital.THRESHOLD_DYNAMIC) self.digital_constellation_modulator_0 = digital.generic_mod( constellation=const, differential=False, @@ -198,28 +508,49 @@ class correlator(gr.top_block, Qt.QWidget): verbose=False, log=False, truncate=False) - self.digital_constellation_decoder_cb_0 = digital.constellation_decoder_cb(const) self.digital_cma_equalizer_cc_0 = digital.cma_equalizer_cc(15, 1, .002, 1) - self.blocks_vector_source_x_0 = blocks.vector_source_b(([0x00] * 10 + [0xaa, 0xff, 0x0a] + [0x00] * 10) * 20, False, 1, []) - self.blocks_throttle_0 = blocks.throttle(gr.sizeof_char*1, samp_rate,True) - self.blocks_null_sink_0 = blocks.null_sink(gr.sizeof_char*1) - self.blocks_complex_to_mag_0 = blocks.complex_to_mag(1) + self.channels_channel_model_0 = channels.channel_model( + noise_voltage=0.2, + frequency_offset=0.000001, + epsilon=1.0, + taps=[-1.4 + .4j], + noise_seed=243, + block_tags=False) + self.blocks_vector_source_x_0 = blocks.vector_source_b(testvec * 1600, False, 1, []) + self.blocks_throttle_0 = blocks.throttle(gr.sizeof_gr_complex*1, samp_rate,True) + self.blocks_stream_mux_0 = blocks.stream_mux(gr.sizeof_char*1, [10, len(testvec)]) + self.blocks_null_source_0 = blocks.null_source(gr.sizeof_char*1) + self.blocks_null_sink_3 = blocks.null_sink(gr.sizeof_float*1) + self.blocks_multiply_const_vxx_0 = blocks.multiply_const_ff(180 / 3.141592653589793) + self.blocks_complex_to_magphase_0_0 = blocks.complex_to_magphase(1) + self.blocks_complex_to_magphase_0 = blocks.complex_to_magphase(1) ################################################## # Connections ################################################## - self.connect((self.blocks_complex_to_mag_0, 0), (self.qtgui_time_sink_x_0, 0)) - self.connect((self.blocks_throttle_0, 0), (self.digital_constellation_modulator_0, 0)) - self.connect((self.blocks_vector_source_x_0, 0), (self.blocks_throttle_0, 0)) - self.connect((self.digital_cma_equalizer_cc_0, 0), (self.digital_constellation_decoder_cb_0, 0)) + self.connect((self.blocks_complex_to_magphase_0, 1), (self.blocks_multiply_const_vxx_0, 0)) + self.connect((self.blocks_complex_to_magphase_0, 0), (self.qtgui_time_sink_x_0, 0)) + self.connect((self.blocks_complex_to_magphase_0_0, 1), (self.blocks_null_sink_3, 0)) + self.connect((self.blocks_complex_to_magphase_0_0, 0), (self.qtgui_time_sink_x_0_0, 0)) + self.connect((self.blocks_multiply_const_vxx_0, 0), (self.qtgui_time_sink_x_2, 0)) + self.connect((self.blocks_null_source_0, 0), (self.blocks_stream_mux_0, 0)) + self.connect((self.blocks_stream_mux_0, 0), (self.digital_constellation_modulator_0, 0)) + self.connect((self.blocks_throttle_0, 0), (self.digital_pfb_clock_sync_xxx_0, 0)) + self.connect((self.blocks_vector_source_x_0, 0), (self.blocks_stream_mux_0, 1)) + self.connect((self.channels_channel_model_0, 0), (self.blocks_throttle_0, 0)) + self.connect((self.digital_cma_equalizer_cc_0, 0), (self.digital_corr_est_cc_0, 0)) self.connect((self.digital_cma_equalizer_cc_0, 0), (self.fir_filter_xxx_1, 0)) - self.connect((self.digital_constellation_decoder_cb_0, 0), (self.blocks_null_sink_0, 0)) - self.connect((self.digital_constellation_modulator_0, 0), (self.digital_pfb_clock_sync_xxx_0, 0)) - self.connect((self.digital_constellation_modulator_0, 0), (self.qtgui_time_sink_x_1, 0)) + self.connect((self.digital_cma_equalizer_cc_0, 0), (self.qtgui_const_sink_x_0, 0)) + self.connect((self.digital_cma_equalizer_cc_0, 0), (self.qtgui_time_sink_x_1_1, 0)) + self.connect((self.digital_constellation_modulator_0, 0), (self.channels_channel_model_0, 0)) + self.connect((self.digital_constellation_modulator_0, 0), (self.qtgui_time_sink_x_1_0, 0)) + self.connect((self.digital_corr_est_cc_0, 1), (self.blocks_complex_to_magphase_0_0, 0)) + self.connect((self.digital_corr_est_cc_0, 0), (self.qtgui_time_sink_x_0_0_0, 0)) self.connect((self.digital_pfb_clock_sync_xxx_0, 0), (self.digital_cma_equalizer_cc_0, 0)) - self.connect((self.fir_filter_xxx_1, 0), (self.blocks_complex_to_mag_0, 0)) + self.connect((self.fir_filter_xxx_1, 0), (self.blocks_complex_to_magphase_0, 0)) + self.connect((self.fir_filter_xxx_1, 0), (self.qtgui_const_sink_x_1, 0)) def closeEvent(self, event): @@ -258,6 +589,13 @@ class correlator(gr.top_block, Qt.QWidget): self.timing_loop_bw = timing_loop_bw self.digital_pfb_clock_sync_xxx_0.set_loop_bandwidth(self.timing_loop_bw) + def get_testvec(self): + return self.testvec + + def set_testvec(self, testvec): + self.testvec = testvec + self.blocks_vector_source_x_0.set_data(self.testvec * 1600, []) + def get_samp_rate(self): return self.samp_rate @@ -265,7 +603,11 @@ class correlator(gr.top_block, Qt.QWidget): self.samp_rate = samp_rate self.blocks_throttle_0.set_sample_rate(self.samp_rate) self.qtgui_time_sink_x_0.set_samp_rate(self.samp_rate) - self.qtgui_time_sink_x_1.set_samp_rate(self.samp_rate) + self.qtgui_time_sink_x_0_0.set_samp_rate(self.samp_rate) + self.qtgui_time_sink_x_0_0_0.set_samp_rate(self.samp_rate) + self.qtgui_time_sink_x_1_0.set_samp_rate(self.samp_rate) + self.qtgui_time_sink_x_1_1.set_samp_rate(self.samp_rate) + self.qtgui_time_sink_x_2.set_samp_rate(self.samp_rate) def get_rrc_taps(self): return self.rrc_taps @@ -274,12 +616,31 @@ class correlator(gr.top_block, Qt.QWidget): self.rrc_taps = rrc_taps self.digital_pfb_clock_sync_xxx_0.update_taps(self.rrc_taps) + def get_revconj_access_code_symbols(self): + return self.revconj_access_code_symbols + + def set_revconj_access_code_symbols(self, revconj_access_code_symbols): + self.revconj_access_code_symbols = revconj_access_code_symbols + self.fir_filter_xxx_1.set_taps(self.revconj_access_code_symbols) + def get_const(self): return self.const def set_const(self, const): self.const = const + def get_access_code_symbols_sps(self): + return self.access_code_symbols_sps + + def set_access_code_symbols_sps(self, access_code_symbols_sps): + self.access_code_symbols_sps = access_code_symbols_sps + + def get_access_code_symbols(self): + return self.access_code_symbols + + def set_access_code_symbols(self, access_code_symbols): + self.access_code_symbols = access_code_symbols + |