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A Reinforcement Learning Approach to Dynamic Spectrum Access in Internet-of-Things Networks

机译：物联网网络中动态频谱访问的强化学习方法

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摘要

To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.
机译：为了以大规模连接性支持物联网（IoT）系统的无线通信流量，动态频谱访问（DSA）是重要的问题。本文提出了一种基于强化学习（RL）算法的频谱传感器辅助DSA系统，旨在有效地利用现有网络上的IoT网络的频谱。由于物联网设备的体积小，它们不具有频谱感应功能。为了支持物联网设备的DSA，我们引入了传感器辅助DSA系统，该系统通过RL算法增强了空间频谱的可重用性。借助RL算法，拟议的DSA系统为大量IoT设备提供了自组织功能。我们展示了在使用时隙ALOHA协议的IoT设备的各种密度的情况下，基于RL的DSA系统的性能，该时隙具有AALHA协议所建议的DSA系统获悉的频谱访问概率。我们还介绍了基于RL的DSA系统提出的性能，该性能超过了用于信道访问协调的带冲突避免的分布式载波侦听多路访问（CSMA / CA）协议。当物联网设备以已获悉的频谱访问概率访问频谱频段时，我们还将展示现有用户的一致性能。

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