Efficient Symbol Detection for Holographic MIMO Communications With Unitary Approximate Message Passing

Abstract

Holographic multiple-input multiple-output (HMIMO) technology holds great promise for delivering high energy and spectral efficiency, boosting system capacity, enhancing diversity, and achieving other significant performance gains. In this work, we focus on the issue of HMIMO symbol detection, which is challenging due to the non-ideal characteristics of the HMIMO near-field (NF) channel matrix introduced by the dyadic Green’s function. These characteristics, such as high-dimensional, ill-conditioned, correlated or rank-deficient, pose considerable difficulties for effective symbol detection. To tackle this problem, we propose an efficient symbol detection algorithm by leveraging the structures of HMIMO NF channel. Specifically, by exploiting the block symmetry of the fully polarized NF channel model, we first decompose the high-dimensional signal model into multiple low-dimensional sub-models, which reduces the computational complexity of preprocessing for the channel matrix compared to its predecessor, thus permitting the design of efficient symbol detection algorithms. Then, building upon these multiple sub-signal models, we formulate the symbol detection problem within a probabilistic framework and construct the corresponding factor graph. By utilizing this factor graph and unitary approximate message passing (UAMP), we propose an efficient Bayesian symbol detection algorithm. The proposed symbol detection algorithm effectively mitigates the adverse effects caused by imperfections in the HMIMO NF polarized channel matrix. Simulation results verify the proposed method outperforms the conventional symbol detector.