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Integrated Access and Backhauling (IAB) is a viable approach for meeting theunprecedented need for higher data rates of future generations, acting as acost-effective alternative to dense fiber-wired links. The design of suchnetworks with constraints usually results in an optimization problem ofnon-convex and combinatorial nature. Under those situations, it is challengingto obtain an optimal strategy for the joint Subchannel Allocation and PowerAllocation (SAPA) problem. In this paper, we develop a multi-agent DeepReinforcement Learning (DeepRL) based framework for joint optimization of powerand subchannel allocation in an IAB network to maximize the downlink data rate.SAPA using DDQN (Double Deep Q-Learning Network) can handle computationallyexpensive problems with huge action spaces associated with multiple users andnodes. Unlike the conventional methods such as game theory, fractionalprogramming, and convex optimization, which in practice demand more and moreaccurate network information, the multi-agent DeepRL approach requires lessenvironment network information. Simulation results show the proposed scheme'spromising performance when compared with baseline (Deep Q-Learning Network andRandom) schemes.

Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks