Double Deep Q-Learning based Backhaul Spectrum Allocation in Integrated Access and Backhaul Network

In the fifth-generation (5G) network, mmWave has been utilized to cope with a demand for an extremely high data rate. However, the harsh propagation characteristic of mmWave signal limits networks' coverage, thus requiring network densification. Under this circumstance, 3GPP has introduced Integrated Access and Backhaul (IAB) architecture for cost-effective network deployment&operation. Contrary to traditional network architecture using wired backhaul links, IAB uses wireless backhaul links to forward data traffic. This feature improves spectrum utilization and cost efficiency. However, due to the dynamic, time-varying environment of the IAB network, finding a proper resource allocation strategy is a challenging issue. In this paper, we formulate the backhaul spectrum allocation problem maximizing user sum capacity. Then propose a double deep Q-Iearning-based backhaul spectrum allocation strategy. The simulation result shows that the proposed reinforcement learning-based spectrum allocation can achieve 20% higher user sum capacity than static rule-based spectrum allocation.