Towards Joint Learning of Optimal Signaling and Wireless Channel Access
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Communication protocols are the languages used by network nodes to accomplish their tasks. Before a User Equipment (UE) can exchange a data bit with the Base Station (BS), it must first negotiate the conditions and parameters for that transmission, which is supported by signaling messages at all layers of the protocol stack. Each year, the mobile communications industry invests large sums of money to define and standardize these messages, which are designed by humans during lengthy technical (and often political) debates. But is this the only way to develop a protocol? Could machines emerge their own signaling protocols automatically and without human intervention? We address the question of whether radios can learn a pre-given target language (i.e., a signaling) 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 signaling. We show that Multiagent Reinforcement Learning (MARL) and learning-to-communicate 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.
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