Toward Joint Learning of Optimal MAC Signaling and Wireless Channel Access

Communication protocols are the languages used by network nodes. Before a user equipment (UE) exchanges data with a base station (BS), it must first negotiate the conditions and parameters for that transmission. This negotiation is supported by signaling messages at all layers of the protocol stack. Each year, the telecoms industry defines and standardizes these messages, which are designed by humans during lengthy technical (and often political) debates. Following this standardization effort, the development phase begins, wherein the industry interprets and implements the resulting standards. But is this massive development undertaking the only way to implement a given protocol? We address the question of whether radios can learn a pre-given target protocol as an intermediate step towards evolving their own. Furthermore, we train cellular radios to emerge a channel access policy that performs optimally under the constraints of the target protocol. We show that multi-agent reinforcement learning (MARL) and learning-to-communicate (L2C) techniques achieve this goal with gains over expert systems. Finally, we provide insight into the transferability of these results to scenarios never seen during training.