Symbol-level precoding (SLP) can achieve remarkable gains in multi-user multiple-input-single-output (MU-MISO) systems, while its extension to multi-user multiple-input-multiple-output (MU-MIMO) systems offers even greater potential by exploiting the spatial dimensions at both the transmitter and receiver. However, existing MU-MIMO SLP designs require symbol-dependent receive combining matrices or rely on alternating iterative optimization, resulting in high signaling overhead and complexity. To overcome these limitations, this letter revisits the receiver design for SLP-based MU-MIMO systems from a minimum mean square error (MMSE) perspective. By explicitly exploiting the rank-one structure of the effective channel induced by SLP, it is proven that the MMSE-optimal linear receiver admits a simple matched-filter form with low computational complexity. Furthermore, it is analytically demonstrated that the conventional regularized MMSE (RMMSE) receiver yields identical decoding performance for any choice of the regularization factor under SLP transmission. Simulation results validate the theoretical analysis and demonstrate that the proposed receiver structures achieve superior performance with substantially reduced complexity compared to existing approaches.
Xiao Tong, Lei Lei, Xiaoyan Hu et al.· IEEE Wireless Communications...· 0 citations
In this paper, we propose an fluid antenna (FA)-enhanced interference exploitation symbol-level precoding (SLP) for downlink multi-user multiple-input single-output (MU-MISO) systems, focusing on transmitter-side fluid antenna systems (Tx-FAS) that allow flexible spatial correlation effects while preserving user portability. First, we derive an explicit expression for the achievable rate of SLP under finite-alphabet inputs, and further establish a closed-form upper bound that quantifies the theoretical rate improvement achieved by spatial reconfigurability of Tx-FAS. Subsequently, a joint optimization problem involving SLP design and Tx-FAS port selection is formulated to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) under strict transmit power constraints. Due to the highly non-convex nature of the problem, we devise a genetic algorithm (GA) to efficiently obtain a near-optimal solution. Moreover, based on the optimal channel structure of SLP, we further develop a low-complexity algorithm with a spatial filtering mechanism to obtain a sub-optimal solution to the considered problem. Simulation results confirm that the proposed FAS-enhanced SLP scheme delivers notable performance improvements over both conventional precoding techniques and the FAS-only transmission approach.
Guorui Wei, Ang Li, Xiao-Yan Hu et al.· IEEE Transactions on Communi...· 0 citations
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