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

TL;DR

This paper proposes a multi-agent Deep Reinforcement Learning framework to optimize joint power and subchannel allocation in IAB networks, aiming to maximize data rates efficiently with less network information.

Contribution

It introduces a novel multi-agent DeepRL approach using DDQN for joint SAPA in IAB networks, outperforming traditional methods in computational efficiency and information requirements.

Findings

The proposed DeepRL method achieves higher data rates than baseline schemes.

It requires less detailed network information compared to traditional optimization methods.

Simulation results validate the effectiveness of the multi-agent DeepRL framework.

Abstract

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.