Fair and scalable energy-efficient rate splitting multiple access in cognitive high altitude platform networks

Area of Science:

• Wireless communication networks
• Resource allocation optimization
• Cognitive radio technology

Background:

• Industry 5.0 demands ultra-reliable, fair, and sustainable wireless networks.
• High Altitude Platforms (HAPs) offer wide coverage but face interference and power constraints.
• Cognitive radio (CR) and rate splitting multiple access (RSMA) are key technologies for HAP networks.

Purpose of the Study:

• To propose an energy-efficient resource allocation framework for RSMA-enabled cognitive HAP networks.
• To address challenges including interference limits, limited onboard power, and user fairness.
• To maximize energy efficiency (EE) while meeting Quality of Service (QoS) and fairness requirements.

Main Methods:

• Formulation of a non-convex energy efficiency maximization problem coupling RSMA rate splitting and beamforming.
• Development of two algorithms: Dinkelbach SCA Joint Beamforming and Rate Allocation (D SCA JBRA) and Maximum Ratio Transmission Nash Bargaining Solution (MRT NBS).
• Utilizing fractional programming, successive convex approximation, and Nash bargaining for optimization.

Main Results:

• The D SCA JBRA algorithm achieved up to 87% and 105% higher EE than benchmarks, maintaining superior fairness.
• The MRT NBS algorithm provided near-optimal performance with over 90% lower computational complexity.
• Both algorithms demonstrated effectiveness in addressing HAP network constraints and user demands.

Conclusions:

• The proposed framework offers a scalable and sustainable solution for Industry 5.0 connectivity.
• The developed algorithms provide efficient and practical approaches for RSMA-enabled cognitive HAP networks.
• This research supports interference-resilient and energy-aware wireless solutions for advanced industrial applications.