A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning - Zihao Li; Xiaolin Zhou; Chengrui Wan; Gang Du; Yuequan Wang (Applied Sciences, 2023)
With the development of the Internet of Things (IoT), most communication systems are difficult to implement on a large scale due to their high complexity. Multiple-input multiple-output (MIMO) precoding is a generally used technique for improving the reliability of free-space optical (FSO) communications, which is a key technology in the 6G era. However, traditional MIMO precoding schemes are typically designed based on the assumption of additive white Gaussian noise (AWGN). In this paper, we present a novel MIMO precoding method based on reinforcement learning (RL) that is specifically designed for the Poisson shot noise model. Unlike traditional MIMO precoding schemes, our proposed scheme takes into account the unique statistical characteristics of Poisson shot noise. Our approach achieves significant performance gains compared to existing MIMO precoding schemes. The proposed scheme can achieve the bit error rate (BER) of <semantics>10−5</semantics> in a strong turbulence channel and exhibits superior robustness against imperfect channel state information (CSI).