AI 中文总结
本研究利用机器学习使多认知雷达自主共享频谱，提出元学习方法，在五个基准环境中优于传统强化学习，实现高效且低冲突的频谱共享。
AI 中文摘要
本项目探讨多个认知雷达如何学习与其他无线电用户共享有限的无线频谱，同时避免相互干扰。通过机器学习（ML），每个设备独立决定在固定的100 MHz频段内传输的位置和宽度。系统分析真实或模拟的信号活动，以检测频谱中当前正在使用的部分和空闲部分。基于这些信息，设备调整其传输选择，以避免拥挤的频率，同时高效利用可用空间。目标是开发一种灵活、可扩展的频谱共享方法，以支持未来的无线通信系统。使用空中软件定义无线电（SDR）记录和模拟环境的实验结果表明，所提出的元学习方法在多智能体频谱共享场景中始终比传统强化学习（RL）方法更好地平衡竞争目标。在五个多智能体基准环境中，我们的方法在主要基线算法中获得了最高的平均奖励，同时保持了低碰撞率和稳定的传输行为。
英文摘要
This project explores how multiple cognitive radars can learn to share limited wireless spectrum with other radio users without interfering with one another. Using machine learning (ML), each device independently decides where and how widely to transmit within a fixed 100 MHz band. The system analyzes real or simulated signal activity to detect which parts of the spectrum are currently in use and which are open. Based on this information, the devices adapt their transmission choices to avoid crowded frequencies while making efficient use of available space. The goal is to develop a flexible, scalable approach to spectrum sharing that could support future wireless communication systems. Experimental results using both over-the-air software-defined radio (SDR) recordings and simulated environments demonstrate that the proposed meta-learning approach consistently balances competing objectives better than conventional reinforcement learning (RL) methods in multi-agent spectrum-sharing scenarios. Across five multi-agent benchmark environments, our proposed method achieved the highest average reward among the primary baseline algorithms while simultaneously maintaining low collision rates and stable transmission behavior.
