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montecarlo_sc.py
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montecarlo_sc.py
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import math as mt
import numpy as np
import matplotlib.pyplot as plt
from scipy import special
def con(a,b):
l = []
for i in range(len(a)):
l.append(a[i] ^ b[i])
for i in range(len(b)):
l.append(b[i])
return l
def f(L1, L2):
l = []
n = len(L1)
for i in range(n):
l.append(np.sign(L1[i]) * np.sign(L2[i]) * min(np.abs(L1[i]), np.abs(L2[i])))
return l
def g(L1, L2, b):
G = []
for i in range(len(L1)):
G.append(L2[i] + (1 - 2*b[i]) * L1[i])
return G
def xor(left,right):
res=[]
for i in range(0,len(left)):
res.append((left[i] + right[i]) % 2)
for i in range(0,len(right)):
res.append(right[i])
return res
def decode (y, d, node,node_values):
if(d == n-1):
decision = []
if node not in F:
if(y[0] < 0):
node_values[node] = 1
decision.append(1)
else:
node_values[node] = 0
decision.append(0)
else:
node_values[node] = 0
decision.append(0)
return decision
else:
L1 = y[0: len(y)//2]
L2 = y[len(y)//2:]
left = f(L1, L2)
arr1 = decode(left, d+1, 2*(node),node_values)
right = g(L1, L2, arr1)
arr2 = decode(right, d + 1, 2*(node) + 1,node_values)
temp = xor(arr1, arr2)
return temp
rel_seq = [0, 1, 2, 4, 8, 16, 32, 3, 5, 64, 9, 6, 17, 10, 18, 128, 12, 33, 65, 20, 256, 34, 24, 36, 7, 129, 66,
512, 11, 40, 68, 130, 19, 13, 48, 14, 72, 257, 21, 132, 35, 258, 26, 513, 80, 37, 25, 22, 136, 260,
264, 38, 514, 96, 67, 41, 144, 28, 69, 42, 516, 49, 74, 272, 160, 520, 288, 528, 192, 544, 70, 44, 131,
81, 50, 73, 15, 320, 133, 52, 23, 134, 384, 76, 137, 82, 56, 27, 97, 39, 259, 84, 138, 145, 261, 29,
43, 98, 515, 88, 140, 30, 146, 71, 262, 265, 161, 576, 45, 100, 640, 51, 148, 46, 75, 266, 273, 517,
104, 162, 53, 193, 152, 77, 164, 768, 268, 274, 518, 54, 83, 57, 521, 112, 135, 78, 289, 194, 85,
276, 522, 58, 168, 139, 99, 86, 60, 280, 89, 290, 529, 524, 196, 141, 101, 147, 176, 142, 530, 321,
31, 200, 90, 545, 292, 322, 532, 263, 149, 102, 105, 304, 296, 163, 92, 47, 267, 385, 546, 324, 208,
386, 150, 153, 165, 106, 55, 328, 536, 577, 548, 113, 154, 79, 269, 108, 578, 224, 166, 519, 552,
195, 270, 641, 523, 275, 580, 291, 59, 169, 560, 114, 277, 156, 87, 197, 116, 170, 61, 531, 525,
642, 281, 278, 526, 177, 293, 388, 91, 584, 769, 198, 172, 120, 201, 336, 62, 282, 143, 103, 178,
294, 93, 644, 202, 592, 323, 392, 297, 770, 107, 180, 151, 209, 284, 648, 94, 204, 298, 400, 608,
352, 325, 533, 155, 210, 305, 547, 300, 109, 184, 534, 537, 115, 167, 225, 326, 306, 772, 157, 656,
329, 110, 117, 212, 171, 776, 330, 226, 549, 538, 387, 308, 216, 416, 271, 279, 158, 337, 550, 672,
118, 332, 579, 540, 389, 173, 121, 553, 199, 784, 179, 228, 338, 312, 704, 390, 174, 554, 581, 393,
283, 122, 448, 353, 561, 203, 63, 340, 394, 527, 582, 556, 181, 295, 285, 232, 124, 205, 182, 643,
562, 286, 585, 299, 354, 211, 401, 185, 396, 344, 586, 645, 593, 535, 240, 206, 95, 327, 564,
800, 402, 356, 307, 301, 417, 213, 568, 832, 588, 186, 646, 404, 227, 896, 594, 418, 302, 649, 771,
360, 539, 111, 331, 214, 309, 188, 449, 217, 408, 609, 596, 551, 650, 229, 159, 420, 310, 541, 773,
610, 657, 333, 119, 600, 339, 218, 368, 652, 230, 391, 313, 450, 542, 334, 233, 555, 774, 175, 123,
658, 612, 341, 777, 220, 314, 424, 395, 673, 583, 355, 287, 183, 234, 125, 557, 660, 616, 342, 316,
241, 778, 563, 345, 452, 397, 403, 207, 674, 558, 785, 432, 357, 187, 236, 664, 624, 587, 780, 705,
126, 242, 565, 398, 346, 456, 358, 405, 303, 569, 244, 595, 189, 566, 676, 361, 706, 589, 215, 786,
647, 348, 419, 406, 464, 680, 801, 362, 590, 409, 570, 788, 597, 572, 219, 311, 708, 598, 601, 651,
421, 792, 802, 611, 602, 410, 231, 688, 653, 248, 369, 190, 364, 654, 659, 335, 480, 315, 221, 370,
613, 422, 425, 451, 614, 543, 235, 412, 343, 372, 775, 317, 222, 426, 453, 237, 559, 833, 804, 712,
834, 661, 808, 779, 617, 604, 433, 720, 816, 836, 347, 897, 243, 662, 454, 318, 675, 618, 898, 781,
376, 428, 665, 736, 567, 840, 625, 238, 359, 457, 399, 787, 591, 678, 434, 677, 349, 245, 458, 666,
620, 363, 127, 191, 782, 407, 436, 626, 571, 465, 681, 246, 707, 350, 599, 668, 790, 460, 249, 682,
573, 411, 803, 789, 709, 365, 440, 628, 689, 374, 423, 466, 793, 250, 371, 481, 574, 413, 603, 366,
468, 655, 900, 805, 615, 684, 710, 429, 794, 252, 373, 605, 848, 690, 713, 632, 482, 806, 427, 904,
414, 223, 663, 692, 835, 619, 472, 455, 796, 809, 714, 721, 837, 716, 864, 810, 606, 912, 722, 696,
377, 435, 817, 319, 621, 812, 484, 430, 838, 667, 488, 239, 378, 459, 622, 627, 437, 380, 818, 461,
496, 669, 679, 724, 841, 629, 351, 467, 438, 737, 251, 462, 442, 441, 469, 247, 683, 842, 738, 899,
670, 783, 849, 820, 728, 928, 791, 367, 901, 630, 685, 844, 633, 711, 253, 691, 824, 902, 686, 740,
850, 375, 444, 470, 483, 415, 485, 905, 795, 473, 634, 744, 852, 960, 865, 693, 797, 906, 715, 807,
474, 636, 694, 254, 717, 575, 913, 798, 811, 379, 697, 431, 607, 489, 866, 723, 486, 908, 718, 813,
476, 856, 839, 725, 698, 914, 752, 868, 819, 814, 439, 929, 490, 623, 671, 739, 916, 463, 843, 381,
497, 930, 821, 726, 961, 872, 492, 631, 729, 700, 443, 741, 845, 920, 382, 822, 851, 730, 498, 880,
742, 445, 471, 635, 932, 687, 903, 825, 500, 846, 745, 826, 732, 446, 962, 936, 475, 853, 867, 637,
907, 487, 695, 746, 828, 753, 854, 857, 504, 799, 255, 964, 909, 719, 477, 915, 638, 748, 944, 869,
491, 699, 754, 858, 478, 968, 383, 910, 815, 976, 870, 917, 727, 493, 873, 701, 931, 756, 860, 499,
731, 823, 922, 874, 918, 502, 933, 743, 760, 881, 494, 702, 921, 501, 876, 847, 992, 447, 733, 827,
934, 882, 937, 963, 747, 505, 855, 924, 734, 829, 965, 938, 884, 506, 749, 945, 966, 755, 859, 940,
830, 911, 871, 639, 888, 479, 946, 750, 969, 508, 861, 757, 970, 919, 875, 862, 758, 948, 977, 923,
972, 761, 877, 952, 495, 703, 935, 978, 883, 762, 503, 925, 878, 735, 993, 885, 939, 994, 980, 926,
764, 941, 967, 886, 831, 947, 507, 889, 984, 751, 942, 996, 971, 890, 509, 949, 973, 1000, 892, 950,
863, 759, 1008, 510, 979, 953, 763, 974, 954, 879, 981, 982, 927, 995, 765, 956, 887, 985, 997, 986,
943, 891, 998, 766, 511, 988, 1001, 951, 1002, 893, 975, 894, 1009, 955, 1004, 1010, 957, 983, 958,
987, 1012, 999, 1016, 767, 989, 1003, 990, 1005, 959, 1011, 1013, 895, 1006, 1014, 1017, 1018, 991,
1020, 1007, 1015, 1019, 1021, 1022, 1023]
N = [2,4,8,16,32,64,128,256,512,1024] #Total bits
K = [1,2,4,8,16,32,64,128,256,512] #actual msg bits
Nsim = 10000 #Total number of simulation for a given EbN0 value
for z in range(len(N)):
n = int(mt.log2(N[z]))+1
Rate = K[z]/N[z]
EbN0dB = np.arange(0,10,0.5) # EbN0 value in db
EbN0 = 10**(EbN0dB/10) # EbN0 value
theoritical = 0.5 - 0.5*special.erf(np.sqrt(Rate*EbN0))
Ber_sc =[] #Bit Error Rate
blker_sc = []
success_sc =[]
Q = [i for i in rel_seq if i < N[z]]
F = Q[: N[z]-K[z]]
NF = Q[N[z]-K[z] :]
for vals in range(len(EbN0)):
sigma = np.sqrt(1/(Rate*2*EbN0[vals]))
total = 0
totalblkerror = 0
for times in range(0,Nsim):
msg = np.random.randint(2, size=K[z])
u = np.zeros(N[z], dtype=int)
u[NF] = msg
stored_msg = []
for i in range(len(u)):
stored_msg.append(u[i])
m=1
for d in range(n-1):
for i in range(0,N[z],2*m):
x = u[i:i+m]
y = u[i+m:i+2*m]
u[i:i+2*m] = con(x,y)
m = m*2
enc_msg = u
#BPSK modulation and AWGN channel
symbols = 1 - 2 * enc_msg
rec_symbols = symbols + sigma * np.random.randn(N[z])
node_values = [0] * N[z]
ans = decode(rec_symbols,0,0,node_values)
count = 0
for i in range(len(node_values)):
if(stored_msg[i] != node_values[i]):
total+=1
count = 1
totalblkerror+=count
Ber_sc.append(total/(Nsim*N[z]))
success_sc.append(1-(totalblkerror/(Nsim)))
blker_sc.append(totalblkerror/(Nsim))
plt.figure(figsize=(10, 6))
plt.figure(1)
plt.yscale('log')
# Plot simulated data
plt.plot(EbN0dB, Ber_sc, marker='o', linestyle='-', color='blue', label='Simulated', linewidth=2)
# Plot theoretical data
plt.plot(EbN0dB, theoritical, marker='s', linestyle='--', color='red', label='Uncoded AWGN', linewidth=2)
plt.xlabel('Eb/N0 (dB)') # Label x-axis
plt.ylabel('Bit Error Rate (BER for SC)') # Label y-axis
plt.title(f'Bit Error Rate (SC) vs. Eb/N0 (N={N}, K={K}, Rate={Rate}, Nsim={Nsim})')
plt.grid(True, which='both', linestyle='--', linewidth=0.5) # Add gridlines
plt.xlim(0,10)
plt.legend(loc='upper right') # Move legend to upper right corner
plt.figure(2)
plt.yscale('log')
plt.plot(EbN0dB, blker_sc, marker='o', linestyle='-', color='blue', label='Block Error Rate Simulated', linewidth=2)
plt.xlabel('Eb/N0 (dB)') # Label x-axis
plt.ylabel('Block Error Rate (BER for SC)') # Label y-axis
plt.title(f'Block Error Rate (SC) vs. Eb/N0 (N={N}, K={K}, Rate={Rate}, Nsim={Nsim})')
plt.grid(True, which='both', linestyle='--', linewidth=0.5) # Add gridlines
plt.xlim(0,10)
plt.legend(loc='upper right')
plt.figure(3)
plt.yscale('log')
plt.plot(EbN0dB, success_sc, label=f'(N={N[z]}, K={K[z]})', linewidth=2)
plt.xlabel('Eb/N0 (dB)') # Label x-axis
plt.ylabel('Success Rate (SC)') # Label y-axis
plt.title(f'Success Rate for different (N,K) polar codes with Rate={Rate} and Nsim={Nsim}')
plt.grid(True, which='both', linestyle='--', linewidth=0.5) # Add gridlines
plt.legend(loc='lower right')
plt.xlim(0,10)
plt.ylim(0,1.01)
plt.show()