Design of NOMA Sparse Signature Matrix for 6G Integrating Sensing and Communications Networks

The low density superposition modulation (LDSM) scheme is one of the NOMA schemes which can support ubiquitous Internet of Things (IoT) devices. This paper focuses on designing the sparse signature matrix with a large girth for LDSM under imperfect channel state information (CSI) for 6G Integrating Sensing and Communications networks. Based on the orthogonal pilot and linear minimum mean square error (LMMSE) estimation, the LDSM optimized by bare-bone particle swarm optimization (BBPSO) algorithm has a larger girth and can gather more accurate information in the process of iterative decoding convergence. An extrinsic information transfer (EXIT) chart analysis is designed for the LDSM-OFDM system as a theoretical analysis tool. The simulation results show that the optimized LDSM outperforms the reference LDSM system, bringing about a 0.5 dB performance gain.