Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks
This work addresses the challenge of optimizing resource allocation in IAB networks for future generations, offering a cost-effective solution with reduced network information requirements, though it appears incremental as it builds on existing DeepRL methods.
The authors tackled the joint subchannel and power allocation problem in Integrated Access and Backhauling (IAB) networks, which is non-convex and combinatorial, by developing a multi-agent Deep Reinforcement Learning framework based on DDQN to maximize downlink data rates, showing promising performance compared to baseline schemes like Deep Q-Learning Network and Random.
Integrated Access and Backhauling (IAB) is a viable approach for meeting the unprecedented need for higher data rates of future generations, acting as a cost-effective alternative to dense fiber-wired links. The design of such networks with constraints usually results in an optimization problem of non-convex and combinatorial nature. Under those situations, it is challenging to obtain an optimal strategy for the joint Subchannel Allocation and Power Allocation (SAPA) problem. In this paper, we develop a multi-agent Deep Reinforcement Learning (DeepRL) based framework for joint optimization of power and subchannel allocation in an IAB network to maximize the downlink data rate. SAPA using DDQN (Double Deep Q-Learning Network) can handle computationally expensive problems with huge action spaces associated with multiple users and nodes. Unlike the conventional methods such as game theory, fractional programming, and convex optimization, which in practice demand more and more accurate network information, the multi-agent DeepRL approach requires less environment network information. Simulation results show the proposed scheme's promising performance when compared with baseline (Deep Q-Learning Network and Random) schemes.