AI-Enabled Joint Optimization of 6G Cognitive Radio Quality of Service and Hybrid Microgrid Energy Efficiency Using Federated and Reinforcement Learning

DOI

10.2991/978-94-6239-707-1_3How to use a DOI?

Keywords

6G Networks; AI-driven QoS; Cognitive Radio; Microgrid Optimization; Sustainable Energy; Federated Learning; Cross-Domain Co-Design

Abstract

This paper presents a novel, AI-driven cross-domain optimization framework designed to synergistically enhance Quality of Service (QoS) in 6G Cognitive Radio (CR) networks and energy efficiency in hybrid AC/DC microgrids. By leveraging a unified ensemble machine learning model—incorporating federated learning, Long Short-Term Memory (LSTM) networks, and reinforcement learning—the proposed system dynamically allocates communication and energy resources in real-time. Key innovations include an AI-augmented Selection Combining (SC) scheme for robust fading mitigation and a microgrid-aware resource allocation strategy. Extensive simulations demonstrate substantial performance improvements: a 40% reduction in communication outage probability, a 60% decrease in network power consumption, a 25% improvement in power quality (Total Harmonic Distortion), and a 30% gain in spectral efficiency. This research validates the transformative potential of integrated AI architectures in creating resilient, self-optimizing, and sustainable infrastructure for smart cities and industrial IoT, effectively bridging the gap between high-performance connectivity and clean energy integration.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nookala Venu
AU  - Nitesh Patidar
AU  - Mehak Kapoor
AU  - Naval Kishor Sharma
AU  - Manjeet Rajput
AU  - Vikash Dhakad
PY  - 2026
DA  - 2026/06/18
TI  - AI-Enabled Joint Optimization of 6G Cognitive Radio Quality of Service and Hybrid Microgrid Energy Efficiency Using Federated and Reinforcement Learning
BT  - Proceedings of the International Conference on Recent Advances in Intelligent and Sustainable Technologies (RAIST 2026)
PB  - Atlantis Press
SP  - 18
EP  - 32
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-707-1_3
DO  - 10.2991/978-94-6239-707-1_3
ID  - Venu2026
ER  -