Near-Optimal MIMO-SCMA Uplink Detection With Low-Complexity Expectation Propagation

Multiple-input multiple-output (MIMO) and sparse code multiple access (SCMA) can be combined to achieve higher spectrum efficiency and more access for users, which also introduces more difficulties in signal detection. This paper explores low-complexity and low-latency iterative algorithms for soft symbol detection in an uplink MIMO-SCMA system over Rayleigh flat-fading channels. An expectation propagation framework (EPA) based on the extended factor graph is developed for MIMO-SCMA with multiantenna users. A new initialization method is proposed to accelerate convergence. Moreover, the SC-EPA with lower complexity is proposed by introducing QR decomposition and RE cluster-based decentralized factor node (FN) processing. Furthermore, new approaches for message passing between variable nodes (VNs) and FNs are proposed to improve the parallelism and reduce the complexity of the algorithm. The complexity of SC-EPA scales linearly with constellation size <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\Omega < \text {M}$ </tex-math></inline-formula>) and is independent of the receiving antenna <inline-formula> <tex-math notation="LaTeX">$\text {N}_{\text {r}}$ </tex-math></inline-formula> without any performance penalties. The robustness of the proposed algorithm in imperfect channels is evaluated, and the state evolution (SE) of the SC-EPA is derived. The link-level simulation results demonstrate that the EPA and SC-EPA receivers can achieve nearly the same performance as state of-the-art methods but with much lower complexity.