Sensing Information-assisted CSI Feedback for UAV Communication Systems

In unmanned aerial vehicle (UAV)-assisted frequency division duplex (FDD) wireless communication systems, accurate channel state information (CSI) is critical to harnessing the benefits of massive multiple-input and multiple-output (mMIMO). However, CSI feedback in UAV-assisted FDD mMIMO systems still faces significant challenges, including high feedback overhead, inevitable channel estimation (CE) errors at the UAV, and complex reconstruction algorithms at the ground base station (gBS). To address these challenges, this paper proposes a dual-branch CSI feedback method assisted by sensing information, inspired by integrated sensing and communication (ISAC). First, the UAV’s location and speed are extracted as prior sensing information from the global navigation satellite system (GNSS). Based on this information, we develop a sensing-aided two-stage CE error suppression method in the delay-Doppler (DD) domain, which incorporates initial and threshold-based error suppression techniques. Notably, a dual-branch compression scheme is designed to significantly reduce feedback overhead by leveraging the sensed prior information. At the gBS, a low-complexity CSI recovery algorithm is proposed. Benefiting from the dual-branch compression scheme, downlink CSI can be recovered through simple mapping operations or a few iterations, thereby reducing computational complexity while improving recovery accuracy. Experimental results demonstrate that the proposed method effectively enhances normalized mean square error (NMSE) performance while reducing both computational complexity and feedback overhead. The method also exhibits robustness against parameter variations. Additionally, it is compatible with deep learning (DL)-based CSI feedback frameworks.