Joint Scheduling and Resource Allocation in mmWave IAB Networks Using Deep RL

TL;DR

This paper introduces a deep reinforcement learning framework for joint scheduling and resource allocation in mmWave IAB networks, significantly improving throughput and accuracy in dynamic, interference-prone environments.

Contribution

It presents a novel decentralized DRL approach combining greedy DDQN schedulers and multi-agent DDQN allocators for efficient link management.

Findings

Achieves 99.84% scheduling accuracy.

Provides 20.90% throughput improvement.

Operates effectively in dynamic, interference-rich scenarios.

Abstract

Integrated Access and Backhaul (IAB) is critical for dense 5G and beyond deployments, especially in mmWave bands where fiber backhaul is infeasible. We propose a novel Deep Reinforcement Learning (DRL) framework for joint link scheduling and resource slicing in dynamic, interference-prone IAB networks. Our method integrates a greedy Double Deep Q-Network (DDQN) scheduler to activate access and backhaul links based on traffic and topology, with a multi-agent DDQN allocator for bandwidth and antenna assignment across network slices. This decentralized approach respects strict antenna constraints and supports concurrent scheduling across heterogeneous links. Evaluations across 96 dynamic topologies show 99.84 percent scheduling accuracy and 20.90 percent throughput improvement over baselines. The framework's efficient operation and adaptability make it suitable for dynamic and resource-constrained deployments, where fast link scheduling and autonomous backhaul coordination are vital.