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

TL;DR

This paper proposes an AI-driven green cognitive radio network framework for 6G that integrates deep reinforcement learning, transfer learning, energy harvesting, and reconfigurable intelligent surfaces to enhance energy efficiency and spectrum utilization.

Contribution

It introduces a novel integrated framework combining multiple AI and energy techniques for sustainable 6G cognitive radio networks, demonstrating significant energy savings and performance improvements.

Findings

25-30% less energy consumption compared to baselines

Sensing accuracy (AUC > 0.90) achieved

PDR increased by 6-13 percentage points

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.