基于深度强化学习的异构物联网设备通信自主RACH资源切片

Hussen Yesuf Ali, Sun Goulin, Abegaz Mohammed Seid
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引用次数: 0

摘要

在无线网络基础设施中,初始同步过程主要决定在设备和基站之间发送或接收数据。此过程通常由随机访问(RA)机制提供动力,以动态共享和分配无线电资源。在过去的几年里,电信行业见证了物联网(IoT)技术的巨大增长,这些技术继续在世界各地推出不同的服务并具有各种需求。然而,当大规模物联网(mIoT)设备试图在一定时间内通过有限数量的随机接入通道(RACH)资源访问网络时,网络会变得过载,导致人与人(H2H)通信性能低下,服务质量(QoS)可能无法得到保证。为了解决上述问题,我们提出了一种基于深度强化学习(DRL)的动态资源切片和访问类限制(ACB)机制,用于新的RACH场景,以动态控制和管理资源。仿真结果表明,我们提出的方法能够根据可用的无线电资源为每个类提供公平的RACH资源分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous RACH Resource Slicing for Heterogeneous IoT Devices Communication Using Deep Reinforcement Learning
In a wireless network infrastructure, the initial synchronization process primarily decides whether to send or receive data between a device and base station. This process is usually powered by a random access (RA) mechanism to share and allocate radio resources dynamically. Over the past years, telecommunication industry has witnessed a massive growth in the Internet of Things (IoT) technologies which continue to be rolled out around the world with different services and having a variety of requirements. However, when massive IoT (mIoT) devices attempt to access the network over a limited number of Random Access Channel (RACH) resources within a time frame, the network becomes overloaded, leading to a low performance of human to human (H2H) communication and Quality of Services (QoS) may not be assured. To solve the above problems, we propose a dynamic resource slicing and access class barring (ACB) mechanism using deep reinforcement learning (DRL) for a new RACH scenario to control and manage the resource dynamically. Simulation results prove that our proposed technique provides a fair RACH resource allocation for each class according to the available radio resource.
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