Perception Assistance for Compressed Sensing-Based CSI Feedback
Chaojin Qing, Haowen Jiang, Zilong Wang, Yuqiao Yang, Yu Sun, Pengfei Du · IEEE Transactions on Wireless Communications · 2025
In massive multiple-input and multiple-output (mMIMO) systems, compressed sensing (CS)-based channel state information (CSI) feedback methods still face significant challenges, such as unknown channel sparsity, high computational complexity, and unavoidable channel estimation (CE) errors at the user equipment (UE). These factors degrade the accuracy of downlink CSI reconstruction at the base station (BS). To tackle these challenges, inspired by the work of perception-assisted communication, a perception-assisted CS-based CSI feedback method is proposed in this paper. In this method, an active perception scheme is developed to extract the support set of downlink CSI from the echo signals, addressing the issue of unknown channel sparsity in CS-based methods. With the perceived support set, the perception-assisted reconstruction without perception errors (PaRwoPE) method is proposed to achieve a lower bound of CSI recovery accuracy. This method develops a perception-assisted suppression scheme to reduce CE errors and transceiver noise and uses the perceived support set to avoid iterative reconstruction, thereby significantly improving the recovery accuracy of downlink CSI with markedly reduced computational complexity. To suppress the inevitable perception errors, a perception-assisted reconstruction with perception errors (PaRPE) method is developed. This method utilizes path correlation to eliminate false paths in the downlink CSI, while also developing a low-complexity iterative scheme to recover missed paths. The computational complexity analysis shows that the proposed method notably reduces computational complexity. Furthermore, simulation results demonstrate its effectiveness in improving normalized mean squared error (NMSE) performance and exhibit robustness against parameter variations.