Low-Complexity Maximum Likelihood Decoding Algorithm for Sparse Superposition Codes

As the demand for high-reliability and low-latency communication grows, short packet transmission has emerged as a key area of research. Recently, sparse superposition codes (SSCs) have shown outstanding performance in short-length channel coding. This extended abstract proposes a low-complexity decoding algorithm for SSCs based on Golay complementary pairs (GCPs), leveraging their properties and algebraic structures of Boolean functions. Simulation results demonstrate that proposed algorithm can effectively reduce the computational complexity while maintaining the same error rate performance as the maximum likelihood (ML) decoding algorithm, thus supporting sustainable and energy-efficient communications.