Semantic-Assisted Joint Channel Estimation and Symbol Detection for MU-MIMO Systems

Abstract

This paper presents a semantic-assisted joint channel estimation and symbol detection (Sem-JCED) receiver framework for text transmission in multi-user multiple-input multiple-output (MU-MIMO) systems. Sem-JCED integrates a linear minimum mean-square error (LMMSE) channel estimator, an expectation propagation (EP) symbol detector, and a semantic corrector based on ByT5, a byte-level language model. At each outer iteration, LMMSE estimates the channel from known pilots and data symbol feedback, and EP detects the data symbols. ByT5 then maps the data symbol probabilities to byte probabilities, which provide soft symbol statistics and semantic priors for feedback to LMMSE and EP, respectively, in the next iteration. Simulation results show that Sem-JCED requires about 7~dB lower signal-to-noise ratio (SNR) than conventional JCED to reach a bit error rate (BER) of $1\times10^{-2}$ with four users and four receive antennas, and outperforms maximum-likelihood detection with perfect channel knowledge over the simulated SNR range.