AI-Driven Green Cognitive Radio Networks for Sustainable 6G Communication

2025-12-23

AI Summary

To address the pressing requirements of 6G sustainable communications—namely, terabit-per-second throughput, sub-millisecond latency, and massive IoT/vehicular network access—this work tackles key bottlenecks in conventional cognitive radio networks (CRNs): high spectrum sensing energy consumption, poor dynamic adaptability, and spectrum scarcity. We propose the first lightweight, synergistic optimization framework integrating deep reinforcement learning (DRL), transfer learning, energy harvesting (EH), and reconfigurable intelligent surfaces (RIS). The framework enables joint adaptive control of spectrum sensing, transmission, and RIS reflection, facilitating green, autonomous operation in dynamic spectral environments. MATLAB+NS-3 co-simulation results demonstrate that, compared to baseline schemes, the proposed approach reduces system energy consumption by 25–30%, achieves an area-under-curve (AUC) of ≥0.90 for spectrum sensing, and improves packet delivery ratio by 6–13 percentage points—significantly enhancing scalability and energy-efficiency robustness in large-scale deployments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• AI optimizes spectrum sensing and allocation for energy efficiency
• Integrates DRL with RIS and energy harvesting for sustainable 6G
• Reduces energy use and improves reliability in dense IoT networks

Innovation

• Integrates DRL with transfer learning for optimization
• Combines energy harvesting and RIS for efficiency
• Uses lightweight genetic operations for resource allocation