A package for Polar codes simulation.
pip install python-polar-coding
Here is a simple example of simulation using python_polar_coding.
Binary messages encoded with Polar code, modulated using BPSK, transmitted over channel with AWGN and decoded using Fast SSC algorithm.
from python_polar_coding.channels import SimpleBPSKModulationAWGN from python_polar_coding.polar_codes import FastSSCPolarCodec from python_polar_coding.simulation.functions import ( compute_fails, generate_binary_message, ) N = 128 K = 64 design_snr = 0.0 messages = 1000 # SNR in [.0, .5, ..., 4.5, 5] snr_range = [i / 2 for i in range(11)] codec = FastSSCPolarCodec(N=N, K=K, design_snr=design_snr) bpsk = SimpleBPSKModulationAWGN(fec_rate=K/N) result_ber = dict() result_fer = dict() print('Python polar coding simulation') print(f'Simulating ({codec.N}, {codec.K}) systematic polar code with Design SNR {codec.design_snr} dB') print() print('\tSNR (dB)|\tBER\t|\tFER') for snr in snr_range: ber = 0 fer = 0 for _ in range(messages): msg = generate_binary_message(size=K) encoded = codec.encode(msg) transmitted = bpsk.transmit(message=encoded, snr_db=snr) decoded = codec.decode(transmitted) bit_errors, frame_error = compute_fails(msg, decoded) ber += bit_errors fer += frame_error result_ber[snr] = ber / (messages * codec.K) result_fer[snr] = fer / messages print(f'\t{snr}\t|\t{result_ber[snr]:.4f}\t|\t{result_fer[snr]:.4f}')
- Arikan's Bhattacharyya bounds Section V.A
- BPSK
- Arikan’s Monte-Carlo estimation Section V.B
- Trifonov’s Gaussian approximation Section V.D
- SC STACK Decoding
- Fast SSC List Decoding
- Generalized Fast SSC LIST Decoding
- CRC-aided decoders
- Q-PSK
- 4-QAM