An Optimum Probabilistic Shaping Based Uplink SCMA Codebook Design using Hybrid Firefly-Bat Algorithm

Sparse Code Multiple Access (SCMA) is a potential Non-Orthogonal Multiple Access (NOMA) scheme that is widely employed in 5G wireless networks to address rigid improvisations through system capacity, reduced latency, and user-controlled connection. The design of the optimum codebook is critical to the operation of the uplink SCMA-based NOMA system. This paper proposes an optimum parity check polar coding (PCPC)-Probabilistic Shaping(PS)-based uplink SCMA codebook design using a biologically inspired-Hybrid-Firefly-Bat algorithm (HFFBA) for the enhancement of average mutual information (AMI) and a reduction in the bit error rate (BER). The application of optimal PCPC aids in reducing the receiver complexity in the uplink NOMA systems. Furthermore, the diversity gain and process reliability has been improved with the merging of PCPC with SCMA. The proposed system's performance is evaluated over Rayleigh and Additive White Gaussian Noise (AWGN) channels for codebook size of 8 and 16 and aims to improve the performance through AMI and BER. The proposed HFFBA codebook achieved an SNR of 12 dB for AWGN and 18 dB for the Rayleigh channel for M = 4 for a BER of 10(-3). The same was attained at an SNR of 21.4 dB for AWGN and 23.6 dB for the Rayleigh channel for M = 16. The gain in SNR for M = 4 to16 for the AWGN and Rayleigh channels are 9.4 dB and 5.6 dB respectively. The optimized codebooks with such biologically inspired algorithms adapt to the changing wireless network conditions and could achieve an increase in data rate and reduced error rate compared to existing systems.