Comprehensive Review of Energy Efficiency Optimization for UAV-Assisted Cognitive Radio Networks with RIS and MEC Integration
DOI:
https://doi.org/10.69955/ajoeee.2025.v5i2.81Keywords:
Unmanned aerial vehicle (UAV), Cognitive radio networks (CRNs) and Mobile Edge Computing (MEC) systems, Reconfigurable Intelligent Surface (RIS).Abstract
This extensive analysis examines how Cognitive Radio Networks (CRNs) can optimise energy efficiency by integrating UAVs, RIS, and MEC systems. Synergistic applications in 6G networks and IoT infrastructure offer unprecedented opportunities to establish sustainable, intelligent wireless communication systems. UAVs enable flexible network deployment in varied contexts by providing mobile base stations with dynamic spectrum access. Amplification-free programmable passive beamforming and phase-shift control improve wireless communication with RIS. By localising data processing and offloading tasks, MEC reduces latency and communication energy consumption. The survey addresses system complexity, dynamic channel variability, interference control, resource scheduling, scalability, security risks, and privacy protection with a single framework. We note that the collaborative optimisation of UAV trajectories, RIS phase shifts, MEC resource allocation, and CRN spectrum sensing improves energy efficiency by 60-75%, exceeding the gains from decoupled component-level optimisation. Real-time machine learning algorithms for dynamic adaptation, hardware miniaturisation for aerial RIS deployment, blockchain-based security protocols, heterogeneous system interoperability standardisation, and field validation through real-world testbeds to prove theoretical energy efficiency gains are future research priorities.
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