Sensing-aided Multi-modal Channel Prediction in 6G mmWave Massive MIMO Systems

To integrate a variety of data-demanding and delay-sensitive applications, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system has emerged as a key enabler for 6G wireless communications. To fully exploit mmWave massive MIMO system, acquisition of accurate channel information is of great importance while such is challenging due to short coherence time of mmWave channel. Therefore, to obtain accurate channel information in a real-time manner, we propose sensing-aided multi-modal channel prediction technique (SMCPT). By analyzing sensing images, we can accurately and quickly extract the positions of mobile devices as well as the positions, orientations, and materials of entities affecting signal propagation, thus enhancing channel prediction accuracy. From practical experiments, we show that SMCPT achieves more than 69% channel prediction accuracy gain over conventional schemes.