Distributed Delay-Aware Link Scheduling and Route Selection in mmWave IAB Networks

Abstract

Integrated Access and Backhaul (IAB) represents a fast and cost-efficient network deployment technology that enhances the coverage of millimeter-wave (mmWave) 5G networks. In addition to the conventional challenges of wireless multi-hop relaying such as, e.g., increased interference and packet delays, traffic asymmetry can lead to significant delay degradation. While centralized coordination can mitigate these challenges, it may also lead to unnecessary overheads. In this paper, we propose an effective delay-aware distributed solution for joint access and backhaul link scheduling and route selection designed to function with limited information, which relies only on the knowledge collected from immediate neighbors. We formulate the joint upstream and downstream routing and scheduling problem, which is solved in a distributed manner for the IAB system with diverse delay requirements. To effectively tackle this problem, we employ deep reinforcement learning (DRL) algorithms. Our numerical results demonstrate that the proposed distributed solution provides improved scalability as compared to the centralized approach without a significant performance loss.