用生成流网络优化无线资源分配，提升性能并减少试错次数。
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks
- 基于生成流网络逐步采样资源分配方案
- 相比基准方法性能提升20%，试错轮次减半
- 适合需要高效资源调度的下一代无线系统
本文研究集成通信、感知与计算功能的无线系统中的射频资源分配问题。针对其高维离散特性，设计能同时满足异构需求且可扩展的资源管理技术。提出一种新型主动学习框架：依次生成资源分配模式，在环境中评估后用于迭代更新环境代理模型。该方法利用生成流网络（GFlowNet）采样高回报解，因其训练目标为按奖励比例生成组合对象，能有效覆盖解空间的多种模式。由此生成多样且高绩效的资源管理方案，驱动代理模型更新并快速发现优质解。仿真结果表明，本方法在性能上相较基准提升20%，且所需采集轮次不足一半。
原文摘要 · Abstract (English)
In this work, we consider the radio resource allocation problem in a wireless system with various integrated functionalities, such as communication, sensing and computing. We design suitable resource management techniques that can simultaneously cater to those heterogeneous requirements, and scale appropriately with the high-dimensional and discrete nature of the problem. We propose a novel active learning framework where resource allocation patterns are drawn sequentially, evaluated in the environment, and then used to iteratively update a surrogate model of the environment. Our method leverages a generative flow network (GFlowNet) to sample favorable solutions, as such models are trained to generate compositional objects proportionally to their training reward, hence providing an appropriate coverage of its modes. As such, GFlowNet generates diverse and high return resource management designs that update the surrogate model and swiftly discover suitable solutions. We provide simulation results showing that our method can allocate radio resources achieving 20% performance gains against benchmarks, while requiring less than half of the number of acquisition rounds.
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