Learning Aided Closed-Loop Feedback: A Concurrent Dual Channel Information Feedback Mechanism for Wi-Fi
Jie Mei, Xianbin Wang, Kan Zheng · IEEE Transactions on Wireless Communications · 2024
To achieve accurate awareness of channel condition, the access point (AP) of a Wi-Fi network has to collect channel state information (CSI) from stations (STAs) periodically. However, existing CSI feedback mechanisms in Wi-Fi are situation agnostic, leading to substantial overhead due to the lack of adaptability and intelligence under dynamic and complex environments. To address this challenge, a concurrent dual channel information feedback mechanism with improve situation-awareness is proposed based on need-driven AP-STA coordination, aiming to maintain the accuracy of collected CSI while dramatically reducing the feedback overhead. By analyzing the latency tolerance of the channel information to be gathered, this concurrent dual feedback mechanism consists of both a delayed channel feature information (CFI) feedback by data frame and an immediate CSI feedback via control frame. In the delayed CFI feedback, a STA collaborates with AP and proactively determines when and what content of CFI to be fed back to the AP. Specifically, the CFI represents channel statistical channel features, which are crucial for the AP to learn the evolving channel conditions. Then, CFI is transmitted to the AP by piggybacking it in the uplink data payload at cost of a certain delay. On the other hand, STA can also utilize the existing CSI feedback mechanism for immediate CSI feedback. With the situation-aware CFI updates from both feedbacks, AP can effectively infer the downlink channel pattern and adapt the time-frequency resolution of CSI feedback to reduce the overhead. Accordingly, a deep cooperative multi-agent reinforcement learning algorithm is proposed to enable a closed-loop coordination between STA and AP for feedback. Simulation results confirm the effectiveness of our proposed mechanism.