用元学习让无线网络快速高效分配频谱，减少试错风险。
Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
- 通过元学习构建可快速适应新场景的初始策略
- 频谱分配峰值吞吐量达48Mbps，远超基线的10Mbps
- 适合需要低延迟、高安全性的6G动态网络部署
5G/6G网络中动态频谱分配对资源利用效率至关重要。但传统深度强化学习（DRL）因样本复杂度高且探索无序易引发严重干扰，难以应用。为此，我们提出一种元学习框架，使智能体在少量数据下即可快速适应新无线场景。实现三种元学习架构：模型无关元学习（MAML）、循环神经网络（RNN）及注意力增强型RNN，与非元学习的近端策略优化（PPO）基线在模拟动态集成接入/回传（IAB）环境中对比。结果表明性能差距显著：基于注意力的元学习智能体达到峰值平均网络吞吐量48 Mbps，而PPO基线骤降至10 Mbps；同时，其信干噪比（SINR）和延迟违规率降低超50%。该方法仅需少量数据即可实现公平性指数0.7的快速适应，验证了元学习在复杂无线系统智能控制中的高效性与安全性。
原文摘要 · Abstract (English)
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.
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