Design and implementation of nonlinear precoding for MIMO-SDMA toward 6G wireless

In this paper, we present a novel and robust nonlinear precoding (NLP) design and detection structure specifically tailored for multiple-input multiple-output space division multiple access (MIMO-SDMA) systems toward 6G wireless. Our approach aims to effectively mitigate the impact of imperfect channel estimation by leveraging the channel fluctuation mean square error (MSE) for reconstructing a highly accurate precoding matrix at the transmitter. Furthermore, we introduce a simplified receiver structure that eliminates the need for equalization, resulting in reduced interference and notable enhancements in overall system performance. We conduct both computer simulations and experimental tests to validate the efficacy of our proposed approach. The results reveals that the proposed NLP scheme offers significant performance improvements, making it particularly well-suited for the forthcoming 6G wireless.