论文标题
基于集成访问和回程网络中的基于深厚学习的频谱分配
Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks
论文作者
论文摘要
我们开发了一个基于深度加强学习（DRL）的框架，以解决新兴的综合访问和回程（IAB）体系结构中的频谱分配问题，并在大规模部署和动态环境中解决。 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.这是钥匙功能的一种功能，其中5G与传统设置的不同之处在于，在该设施中，在该设施网络上设计了独立于Theaccess Networks。为了最大化所有UE组的总和量值率，我们制定了频谱分配问题构成混合和非线性编程。但是，要找到一个最佳解决方案是棘手的，尤其是当Theiab网络较大且随时间变化时。为了解决这个问题，我们建议通过集成Anactor-Critic频谱分配（ACSA）方案和深神经网络（DNN）来使用最新的DRL方法，以实现实时频谱分配无关的场景。通过数字模拟评估了所提出的方法，并与一些基线分配策略相比显示出令人鼓舞的结果。
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.
