Low-Resolution Quantized Precoding for Multiple-Input Multiple-Output Dual-Functional Radar–Communication Systems Used for Target Sensing

Dual-functional radar–communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-function radar–communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios.