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arXiv 2004.13133cs.ITeess.SPmath.IT
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基于深度强化学习的综合接入回传网络频谱分配

Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks

Wanlu Lei, Yu Ye, Ming Xiao

摘要:

本文提出了一种基于深度强化学习(Deep Reinforcement Learning,DRL)的频谱分配框架,以解决大规模部署和动态环境下的集成接入和回程(Integrated Access and Backhaul,IAB)架构中的频谱分配问题。可用频谱被划分为若干正交子信道,并且施主基站(DBS)和所有IAB节点具有相同的频谱资源用于分配,其中DBS将这些子信道用于相关联的用户设备(UE)的接入链路以及相关联的IAB节点的回程链路,并且IAB节点可以将所有子信道用于其相关联的UE。这是5G不同于传统设置的关键特征之一,传统设置中回程网络独立于接入网络设计。以最大化所有UE组的对数速率之和为目标,将频谱分配问题转化为一个混合整数非线性规划问题。然而,它是难以找到一个最佳的解决方案,特别是当IAB网络是大的和时变的。为了解决这个问题,我们建议使用最新的DRL方法,通过集成ACSA方案和深度神经网络(DNN)来实现不同场景的实时频谱分配。通过数值模拟对所提出的方法进行了评估,并与一些基线分配策略进行了比较,结果显示出良好的效果。

英文摘要:

We develop a framework based on deep reinforce-ment learning (DRL) to solve the spectrum allocation problem inthe emerging integrated access and backhaul (IAB) architecturewith large scale deployment and dynamic environment. The avail-able spectrum is divided into several orthogonal sub-channels,and the donor base station (DBS) and all IAB nodes have thesame spectrum resource for allocation, where a DBS utilizes thosesub-channels for access links of associated user equipment (UE)as well as for backhaul links of associated IAB nodes, and anIAB node can utilize all for its associated UEs. This is one ofkey features in which 5G differs from traditional settings wherethe backhaul networks were designed independently from theaccess networks. With the goal of maximizing the sum log-rateof all UE groups, we formulate the spectrum allocation probleminto a mix-integer and non-linear programming. However, itis intractable to find an optimal solution especially when theIAB network is large and time-varying. To tackle this problem,we propose to use the latest DRL method by integrating anactor-critic spectrum allocation (ACSA) scheme and deep neuralnetwork (DNN) to achieve real-time spectrum allocation indifferent scenarios. The proposed methods are evaluated throughnumerical simulations and show promising results compared withsome baseline allocation policies.

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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