基于分布式强化学习的低复杂度无线收发器IoTs媒体接入
Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers
摘要：
本文提出了一种基于分布式强化学习（RL）的框架，该框架可用于在具有低复杂度无线收发器的物联网网络中合成MAC层无线协议。所提出的框架不依赖于复杂的硬件功能，例如载波侦听及其相关的算法复杂性，这些功能在低成本和低能耗物联网设备的无线收发器中通常不受支持。在这个框架中，访问协议首先制定为马尔可夫决策过程（MDP），然后使用RL解决。一个分布式和多Agent RL框架被用作协议合成的基础。分布式行为使得节点独立地学习最优传输策略，而不必依赖于完整的网络级信息和其他节点行为的直接知识。节点学习最小化分组冲突，使得可以针对高于用于IoT设备的已知基准协议（诸如ALOHA）的负载条件获得和维持最佳吞吐量，而无需复杂的收发器。此外，观察到的节点能够学习最佳的行为在异构负载和网络拓扑条件的存在下。最后，所提出的学习方法允许无线带宽以不依赖于这种异质性的方式在网络节点之间公平地分布。通过仿真实验，本文展示了学习范式的性能和它的能力，使节点适应他们的最佳传输策略，在飞行中响应各种网络动态。
英文摘要：
This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks with low-complexity wireless transceivers. The proposed framework does not rely on complex hardware capabilities such as carrier sensing and its associated algorithmic complexities that are often not supported in wireless transceivers of low-cost and low-energy IoT devices. In this framework, the access protocols are first formulated as Markov Decision Processes (MDP) and then solved using RL. A distributed and multi-Agent RL framework is used as the basis for protocol synthesis. Distributed behavior makes the nodes independently learn optimal transmission strategies without having to rely on full network level information and direct knowledge of behavior of other nodes. The nodes learn to minimize packet collisions such that optimal throughput can be attained and maintained for loading conditions that are higher than what the known benchmark protocols (such as ALOHA) for IoT devices without complex transceivers. In addition, the nodes are observed to be able to learn to act optimally in the presence of heterogeneous loading and network topological conditions. Finally, the proposed learning approach allows the wireless bandwidth to be fairly distributed among network nodes in a way that is not dependent on such heterogeneities. Via simulation experiments, the paper demonstrates the performance of the learning paradigm and its abilities to make nodes adapt their optimal transmission strategies on the fly in response to various network dynamics.
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。
