  Communication protocols are the languages used by network nodes. Before a
user equipment (UE) can exchange 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 mobile communications 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.
