We consider a dynamic millimeter-wave network with integrated access and backhaul, where mobile relay nodes move to auto-reconfigure the wireless backhaul. Specifically, we focus on in-band relaying networks, which conduct access and backhaul links on the same frequency band with severe constraints on co-channel interference. In this context, we jointly study the complex problem of dynamic relay node positioning, user association, and backhaul capacity allocation. To address this problem, with limited complexity, we adopt a hierarchical multi-agent reinforcement with a two-level structure. A high-level policy dynamically coordinates mobile relay nodes, defining the backhaul configuration for a low-level policy, which jointly assigns user equipment to each relay and allocates the backhaul capacity accordingly. The resulting solution automatically adapts the access and backhaul network to changes in the number of users, the traffic distribution, and the variations of the channels. Numerical results show the effectiveness of our proposed solution in terms of convergence of the hierarchical learning procedure. It also provides a significant backhaul capacity and network sum-rate increase (up to 3.5x) compared to baseline approaches.
翻译:我们考虑的是具有综合接入和回航功能的动态毫米波网络,移动中继节点移动到自动重组无线回路。具体地说,我们侧重于带内中继网络,在同一个频带上进行接驳和回航连接,对共同通道干扰有严重限制。在这方面,我们共同研究动态中继节点定位、用户关联和回航能力配置等复杂问题。为了解决这个问题,我们采用了一个具有两个层次结构的等级多试剂加固系统。高级别政策动态协调移动中继节点,确定低级别政策的回航配置,将用户设备联合分配给每个中继系统,并相应分配回航能力。由此产生的解决方案自动调整了接驳和回航网络,以适应用户数量、交通分布和渠道变化的变化。数字结果显示,我们提议的解决方案在分级学习程序趋同方面的效力。它还提供了与基线方法相比,显著的回航能力和网络总和总算增加(达3.5x)。