We tackle the problem of joint frequency and power allocation while emphasizing the generalization capability of a deep reinforcement learning model. Most of the existing methods solve reinforcement learning-based wireless problems for a specific pre-determined wireless network scenario. The performance of a trained agent tends to be very specific to the network and deteriorates when used in a different network operating scenario (e.g., different in size, neighborhood, and mobility, among others). We demonstrate our approach to enhance training to enable a higher generalization capability during inference of the deployed model in a distributed multi-agent setting in a hostile jamming environment. With all these, we show the improved training and inference performance of the proposed methods when tested on previously unseen simulated wireless networks of different sizes and architectures. More importantly, to prove practical impact, the end-to-end solution was implemented on the embedded software-defined radio and validated using over-the-air evaluation.

翻译：本文聚焦于深度强化学习模型的泛化能力，研究联合频率与功率分配问题。现有方法大多针对特定预设无线网络场景解决基于强化学习的无线问题，训练后的智能体对其所处网络具有高度特异性，当应用于不同网络运行场景（如规模、邻域结构及移动性等差异）时性能会显著下降。我们提出一种增强训练方法，使分布式多智能体对抗性干扰环境中的部署模型在推理阶段具备更高的泛化能力。通过实验证明，所提方法在未经训练的、不同规模与架构的模拟无线网络上展现出更优的训练与推理性能。更重要的是，为验证实际应用效果，我们在嵌入式软件无线电平台上实现了端到端解决方案，并通过空中接口测试完成了评估验证。