We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment. The available spectrum is divided into several orthogonal sub-channels, and the donor base station (DBS) and all IAB nodes have the same spectrum resource for allocation, where a DBS utilizes those sub-channels for access links of associated user equipment (UE) as well as for backhaul links of associated IAB nodes, and an IAB node can utilize all for its associated UEs. This is one of key features in which 5G differs from traditional settings where the backhaul networks are designed independently from the access networks. With the goal of maximizing the sum log-rate of all UE groups, we formulate the spectrum allocation problem into a mix-integer and non-linear programming. However, it is intractable to find an optimal solution especially when the IAB network is large and time-varying. To tackle this problem, we propose to use the latest DRL method by integrating an actor-critic spectrum allocation (ACSA) scheme and deep neural network (DNN) to achieve real-time spectrum allocation in different scenarios. The proposed methods are evaluated through numerical simulations and show promising results compared with some baseline allocation policies.

中文翻译：

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集成接入和回程网络中基于深度强化学习的频谱分配


我们开发了一个基于深度强化学习（DRL）的框架，以解决大规模部署和动态环境的新兴集成接入和回程（IAB）架构中的频谱分配问题。可用频谱被划分为多个正交子信道，施主基站（DBS）和所有IAB节点具有相同的频谱资源进行分配，其中DBS将这些子信道用于相关用户设备（UE）的接入链路以及关联IAB节点的回程链路，并且IAB节点可以将所有链路用于其关联UE。这是 5G 与传统设置不同的关键特征之一，在传统设置中，回程网络是独立于接入网络设计的。为了最大化所有 UE 组的总对数速率，我们将频谱分配问题表述为混合整数和非线性规划。然而，找到最优解是很困难的，特别是当 IAB 网络很大且时变时。为了解决这个问题，我们建议使用最新的DRL方法，通过集成演员批评家频谱分配（ACSA）方案和深度神经网络（DNN）来实现不同场景下的实时频谱分配。所提出的方法通过数值模拟进行了评估，并与一些基线分配政策相比显示出有希望的结果。