| Summary: | Congestion in dense traffic networks is a prominent obstacle towards realizing the performance requirements of 5G new radio. Since traditional adaptive traffic signal control cannot resolve this type of congestion, realizing context in the network and adapting resource allocation based on real-time parameters is an attractive approach. This article proposes a radio resource management solution for congestion avoidance on the access side of an integrated access and backhaul (IAB) network using deep reinforcement learning (DRL). The objective of this article is to obtain an optimal policy under which the transmission throughput of all UEs is maximized under the dictates of environmental pressures such as traffic load and transmission power. Here, the resource management problem was converted into a constrained problem using Markov decision processes and dynamic power management, where a deep neural network was trained for optimal power allocation. By initializing a power control parameter, <inline-formula> <tex-math notation="LaTeX">$\theta _{t}$ </tex-math></inline-formula>, with zero-mean normal distribution, the DRL algorithm adopts a learning policy that aims to achieve logical allocation of resources by placing more emphasis on congestion control and user satisfaction. The performance of the proposed DRL algorithm was evaluated using two learning schemes, i.e., individual learning and nearest neighbor cooperative learning, and this was compared with the performance of a baseline algorithm. The simulation results indicate that the proposed algorithms give better overall performance when compared to the baseline algorithm. From the simulation results, there is a subtle, but critically important insight that brings into focus the fundamental connection between learning rate and the two proposed algorithms. The nearest neighbor cooperative learning algorithm is suitable for IAB networks because its throughput has a good correlation with the congestion rate.
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