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：本文聚焦于联合频率与功率分配问题，重点探讨深度强化学习模型的泛化能力。现有方法大多针对特定预定义的无线网络场景解决基于强化学习的无线通信问题，训练后的智能体性能往往高度依赖特定网络环境，当应用于不同网络运行场景（如规模、邻域结构及移动性等存在差异的场景）时性能会显著下降。我们提出一种增强训练的方法，使部署模型在分布式多智能体敌对干扰环境中具备更强的推理泛化能力。通过上述方法，我们展示了所提方案在未参与训练的不同规模及架构的无线网络仿真测试中，其训练与推理性能均得到提升。更重要的是，为验证实际应用价值，本端到端解决方案已在嵌入式软件无线电平台上实现，并通过空中接口评估完成验证。