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To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6Gwireless networks, conventional medium access control (MAC) procedures need toevolve to enable base stations (BSs) and user equipments (UEs) to automaticallylearn innovative MAC protocols catering to extremely diverse services. Thistopic has received significant attention, and several reinforcement learning(RL) algorithms, in which BSs and UEs are cast as agents, are available withthe aim of learning a communication policy based on agents' local observations.However, current approaches are typically overfitted to the environment theyare trained in, and lack robustness against unseen conditions, failing togeneralize in different environments. To overcome this problem, in this work,instead of learning a policy in the high dimensional and redundant observationspace, we leverage the concept of observation abstraction (OA) rooted inextracting useful information from the environment. This in turn allowslearning communication protocols that are more robust and with much bettergeneralization capabilities than current baselines. To learn the abstractedinformation from observations, we propose an architecture based on autoencoder(AE) and imbue it into a multi-agent proximal policy optimization (MAPPO)framework. Simulation results corroborate the effectiveness of leveragingabstraction when learning protocols by generalizing across environments, interms of number of UEs, number of data packets to transmit, and channelconditions.

Learning Generalized Wireless MAC Communication Protocols via Abstraction