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

The evolution toward the industry 5.0, with the applications such as the digital twins and the collaborative robotics, demands the wireless networks that jointly guarantee the ultra reliable connectivity, the fairness aware service, and the energy sustainability. The cognitive radio (CR) enabled high altitude platforms (HAPs) offer the wide area coverage and the flexible spectrum access; and their deployment is constrained by the stringent interference limits toward the terrestrial primary users (PUs), the limited onboard power, and the need for the uniform service among the secondary users (SUs). This paper proposes an energy efficient resource allocation framework for the rate splitting multiple access (RSMA) enabled cognitive HAP networks that addresses these challenges. We formulate a non convex energy efficiency (EE) maximization problem that explicitly couples the RSMA's common and private rate split with the beamforming design under the PU interference thresholds, the SU QoS requirements, and the fairness gap constraints. To solve this problem, we develop the two complementary algorithms: (i) the Dinkelbach SCA Joint Beamforming and Rate Allocation (D SCA JBRA), a high performance iterative scheme based on the fractional programming and the successive convex approximation; and (ii) the MRT NBS, a low complexity heuristic that integrates the maximum ratio transmission with the Nash bargaining based rate splitting to yield the closed form and the real time solutions. The extensive simulations against a comprehensive benchmark suite (including the OMA MRT, the RSMA EPA, the RSMA RBF, the NOMA FPA, and the RSMA WMMSE) show that the D SCA JBRA achieves up to 87 and 105% higher EE than the OMA MRT and the RSMA RBF, respectively, while maintaining the superior fairness. Meanwhile, the MRT NBS delivers the near optimal performance with over 90% lower computational complexity; and this validates its suitability for the real time HAP deployment. The proposed framework provides a scalable and sustainable solution for the interference resilient and the energy aware connectivity demands of the Industry 5.0 such as smart mining.