Implementation of a BPSK Coded and Interleaved Communication Link Simulation

This article demonstrates how to construct a communication link simulation system that integrates BPSK modulation with convolutional coding and interleaving techniques, with a focus on analyzing the Bit Error Rate (BER) performance of different decoding algorithms under various channel conditions.

### System Architecture The simulated communication chain primarily consists of the following modules: data source generation, convolutional encoding, interleaving, BPSK modulation, channel transmission, demodulation, de-interleaving, and decoding. The system uses a 100 kbps bit rate as the source input. Forward error correction is applied through a 1/2 rate convolutional encoder (with generator polynomials 561 and 753), followed by block interleaving (interleaver depth of 100 bits, width of 10) to combat burst errors. (In code implementation, the convolutional encoder can be modeled using shift registers and polynomial arithmetic, while the interleaver can be implemented as a matrix write/read operation with row-column permutation.)

### Key Technologies BPSK Modulation: Binary Phase Shift Keying is the most fundamental digital modulation scheme, offering good noise immunity and serving as a suitable baseline modulation technique. (Implementation typically involves mapping binary 0/1 to phases 0/π radians using cosine waveform generation.) Convolutional Coding and Viterbi Decoding: A 1/2 rate encoder is implemented using generator polynomials (561, 753). The decoding performance comparison between hard-decision and soft-decision (8-bit quantization) approaches shows that soft-decision decoding typically provides approximately 2 dB gain. (The Viterbi algorithm implementation requires maintaining a trellis structure and calculating path metrics, with soft-decision using Euclidean distance calculations.) Interleaving Technique: Row-column interleaving disperses burst errors, enhancing the error correction capability of channel coding. The interleaver depth and width should be optimized based on channel characteristics. (Code implementation involves writing bits row-wise into a matrix and reading them column-wise, with parameters adjustable through configuration variables.)

### Performance Analysis The simulation evaluates Bit Error Rate using 10^6 data points, focusing on the following scenarios: Comparison of BER performance between hard-decision and soft-decision decoding in AWGN channels Performance improvement provided by interleaving technology when burst interference is present System robustness analysis under different Signal-to-Noise Ratio conditions (In MATLAB implementations, the BER calculation typically involves comparing transmitted and received bit sequences using logical operations and statistical counting functions.)

This simulation enables clear comparison of different decoding strategies and provides valuable reference for practical communication system design.