Abstract
The 6G wireless aims at the Tb/s peak data rates are expected, a sub-millisecond latency, massive Internet of Things/vehicle connectivity, which requires sustainable access to audio over the air and energy-saving functionality. Cognitive Radio Networks CCNs help in alleviating the problem of spectrum scarcity, but classical sensing and allocation are still energy-consumption intensive, and sensitive to rapid spectrum variations. Our framework which centers on AI driven green CRN aims at integrating deep reinforcement learning (DRL) with transfer learning, energy harvesting (EH), reconfigurable intelligent surfaces (RIS) with other light-weight genetic refinement operations that optimally combine sensing timelines, transmit power, bandwidth distribution and RIS phase selection. Compared to two baselines, the utilization of MATLAB + NS-3 under dense loads, a traditional CRN with energy sensing under fixed policies, and a hybrid CRN with cooperative sensing under heuristic distribution of resource, there are (25-30%) fewer energy reserves used, sensing AUC greater than 0.90 and +6-13 p.p. higher PDR. The integrated framework is easily scalable to large IoT and vehicular applications, and it provides a feasible and sustainable roadmap to 6G CRNs. Index Terms--Cognitive Radio Networks (CRNs), 6G, Green Communication, Energy Efficiency, Deep Reinforcement Learning (DRL), Spectrum Sensing, RIS, Energy Harvesting, QoS, IoT.
Abstract (translated)
6G无线技术的目标是实现每秒太比特级别的峰值数据速率，亚毫秒级的延迟以及大规模物联网和车辆连接的需求，这需要持续的空中音频传输能力和节能功能。认知无线电网络（CRNs）有助于缓解频谱短缺的问题，但传统的感知和分配方法仍然消耗大量能量，并且对快速变化的频谱敏感。我们的框架以人工智能驱动的绿色CRN为中心，旨在将深度强化学习（DRL）与迁移学习、能量收集（EH）、可重构智能表面前沿技术以及其他轻量级遗传优化操作结合起来，这些操作能够最优地组合感知时间线、传输功率、带宽分配和RIS相位选择。 相较于两种基准方法——在高负载下使用MATLAB+NS-3的环境下的传统CRN频谱感知（固定策略）和混合CRN合作感知（基于启发式资源分布），我们的框架使用的能量储备减少了25%-30%，AUC值大于0.90，PDR提高了6-13个百分点。该综合框架易于扩展到大规模物联网和车辆应用，并为实现可持续发展的6G CRNs提供了可行的路线图。 关键词——认知无线电网络（CRN）、6G、绿色通信、能效、深度强化学习（DRL）、频谱感知、RIS、能量收集、服务质量（QoS）、物联网。
URL
https://arxiv.org/abs/2512.20739
