arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.09948cs.ITcs.NImath.IT
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最优MAC信令和无线信道接入的联合学习

Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access

Alvaro Valcarce, Jakob Hoydis

摘要:

通信协议是网络节点使用的语言。在用户设备(UE)可以与基站(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,移动通信行业都会对这些信息进行定义和分类,这些信息是由人类在漫长的技术(通常是政治)辩论中设计的。在标准化工作之后,开发阶段开始,其中行业解释和实现所产生的标准。但是,这种大规模的发展是实现给定协议的唯一途径吗?我们解决的问题,无线电是否可以学习一个预先给定的目标协议,作为一个中间步骤,朝着自己的发展。此外,我们训练蜂窝无线电出现一个信道访问策略,在目标协议的约束下,执行最佳。我们表明,多智能体强化学习(MARL)和学习交流(L2C)技术实现了这一目标,增益超过专家系统。最后,我们提供了深入了解这些结果的可移植性,在训练过程中从未见过的场景。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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