Learning Hierarchical Resource Allocation and Multi-agent Coordination of 5G mobile IAB Nodes

TL;DR

This paper proposes a hierarchical multi-agent reinforcement learning approach to dynamically optimize resource allocation and coordination in 5G millimeter-wave networks with integrated access and backhaul, improving network capacity and adaptability.

Contribution

It introduces a novel hierarchical reinforcement learning framework for joint relay positioning, user association, and backhaul capacity allocation in dynamic 5G IAB networks.

Findings

Up to 3.5x increase in network sum-rate.

Effective convergence of the hierarchical learning procedure.

Automatic adaptation to network changes.

Abstract

We consider a dynamic millimeter-wave network with integrated access and backhaul, where mobile relay nodes move to auto-reconfigure the wireless backhaul. Specifically, we focus on in-band relaying networks, which conduct access and backhaul links on the same frequency band with severe constraints on co-channel interference. In this context, we jointly study the complex problem of dynamic relay node positioning, user association, and backhaul capacity allocation. To address this problem, with limited complexity, we adopt a hierarchical multi-agent reinforcement with a two-level structure. A high-level policy dynamically coordinates mobile relay nodes, defining the backhaul configuration for a low-level policy, which jointly assigns user equipment to each relay and allocates the backhaul capacity accordingly. The resulting solution automatically adapts the access and backhaul network to changes in the number of users, the traffic distribution, and the variations of the channels. Numerical results show the effectiveness of our proposed solution in terms of convergence of the hierarchical learning procedure. It also provides a significant backhaul capacity and network sum-rate increase (up to 3.5x) compared to baseline approaches.