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.

中文翻译：

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迈向最佳 MAC 信令和无线信道接入的联合学习

通信协议是网络节点使用的语言。在用户设备 (UE) 与基站 (BS) 交换数据之前，它必须首先协商该传输的条件和参数。协议栈所有层的信令消息都支持这种协商。每年，电信行业都会对这些信息进行定义和标准化，这些信息是人类在冗长的技术（通常是政治）辩论中设计的。在此标准化工作之后，开发阶段开始，在此行业解释和实施由此产生的标准。但是，这种大规模的开发是实现给定协议的唯一方法吗？我们解决了无线电是否可以学习预先给定的目标协议作为发展自己的中间步骤的问题。此外，我们训练蜂窝无线电以形成在目标协议的约束下以最佳方式执行的信道访问策略。我们展示了多智能体强化学习 (MARL) 和学习交流 (L2C) 技术通过优于专家系统的优势实现了这一目标。最后，我们深入了解这些结果在训练期间从未见过的场景的可转移性。