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We consider the problem of autonomous channel access (AutoCA), where a groupof terminals tries to discover a communication strategy with an access point(AP) via a common wireless channel in a distributed fashion. Due to theirregular topology and the limited communication range of terminals, apractical challenge for AutoCA is the hidden terminal problem, which isnotorious in wireless networks for deteriorating the throughput and delayperformances. To meet the challenge, this paper presents a new multi-agent deepreinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in thepresence of hidden terminals. MADRL-HT exploits topological insights andtransforms the observation space of each terminal into a scalable formindependent of the number of terminals. To compensate for the partialobservability, we put forth a look-back mechanism such that the terminals caninfer behaviors of their hidden terminals from the carrier sensed channelstates as well as feedback from the AP. A window-based global reward functionis proposed, whereby the terminals are instructed to maximize the systemthroughput while balancing the terminals' transmission opportunities over thecourse of learning. Extensive numerical experiments verified the superiorperformance of our solution benchmarked against the legacy carrier-sensemultiple access with collision avoidance (CSMA/CA) protocol.

Learning-based Autonomous Channel Access in the Presence of Hidden Terminals