Luo, Qu and Liu, Zilong and Chen, Gaojie and Xiao, Pei (2023) Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels. IEEE Transactions on Wireless Communications, 23 (4). pp. 3676-3690. DOI https://doi.org/10.1109/TWC.2023.3310000
Luo, Qu and Liu, Zilong and Chen, Gaojie and Xiao, Pei (2023) Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels. IEEE Transactions on Wireless Communications, 23 (4). pp. 3676-3690. DOI https://doi.org/10.1109/TWC.2023.3310000
Luo, Qu and Liu, Zilong and Chen, Gaojie and Xiao, Pei (2023) Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels. IEEE Transactions on Wireless Communications, 23 (4). pp. 3676-3690. DOI https://doi.org/10.1109/TWC.2023.3310000
Abstract
Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems.
Item Type: | Article |
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Uncontrolled Keywords: | Sparse code multiple access (SCMA); signal space diversity (SSD); average mutual information (AMI); lower bound; modified minimum product distance (MMPD); codebook design |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 29 Aug 2023 09:12 |
Last Modified: | 24 Apr 2024 05:18 |
URI: | http://repository.essex.ac.uk/id/eprint/36232 |
Available files
Filename: SSD_SCMA_TWC.pdf