Deep Reinforcement Learning-based Spectrum Allocation and Power Management for IAB Networks
Qingqing Cheng, Zhiqiang Wei, Jinhong Yuan
Abstract
The integrated access and backhaul (IAB) has demonstrated tremendous potential in the fifth-generation (5G) network, acting as an economical and sustainable alternative to conventional fiber-like architectures. Although promising, how to efficiently and effectively allocate bandwidth and power resource to the backhaul and access links is a challenging issue for IAB. In this paper, we propose a novel scheme to jointly allocate the spectrum and transmitting power of the IAB donor and IAB nodes, so as to serve their respective access links and backhaul links. With the objective of maximizing the downlink data rate, we formulate the spectrum allocation and power management as a mix-integer and non-linear programming problem. Then, we propose to use an advanced deep reinforcement learning (DRL), i.e., double deep Q-learning, to achieve an efficient policy for joint spectrum allocation and power management, namely SAPM-DDQN. We perform the proposed SAPM-DDQN in a decentralized manner, which can significantly reduce the transmission overhead, compared to the centralized one. To the best of the authors’ knowledge, this is the first attempt on the joint spectrum allocation and power management in a decentralized manner for IAB, leveraging the property of DRL techniques. The proposed SAPM-DDQN does not require any prior information from other units for optimization, which is suitable for practical deployment. The extensive simulation results are presented to verify the effectiveness of the proposed scheme for joint spectrum allocation and power management.