Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks

TL;DR

This paper introduces a meta-learning approach for spectrum allocation in dynamic wireless networks, enabling rapid adaptation and improved performance over traditional DRL methods, with significant gains in throughput and safety.

Contribution

It develops and evaluates three meta-learning architectures for fast, data-efficient spectrum management, demonstrating superior performance and safety compared to standard DRL algorithms.

Findings

Meta-learning architectures outperform PPO baseline in throughput.

Attention-based meta-learning reduces interference and latency violations.

Rapid adaptation improves fairness and resource allocation.

Abstract

The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexity and the safety risks associated with unguided exploration, which can cause severe network interference. To address these challenges, we propose a meta-learning framework that enables agents to learn a robust initial policy and rapidly adapt to new wireless scenarios with minimal data. We implement three meta-learning architectures, model-agnostic meta-learning (MAML), recurrent neural network (RNN), and an attention-enhanced RNN, and evaluate them against a non-meta-learning DRL algorithm, proximal policy optimization (PPO) baseline, in a simulated dynamic integrated access/backhaul (IAB) environment. Our results show a clear performance gap. The attention-based meta-learning agent reaches a peak mean network throughput of 48 Mbps, while the PPO baseline decreased drastically to 10 Mbps. Furthermore, our method reduces SINR and latency violations by more than 50% compared to PPO. It also shows quick adaptation, with a fairness index 0.7, showing better resource allocation. This work proves that meta-learning is a very effective and safer option for intelligent control in complex wireless systems.