Energy-Efficient Resource Allocation Design for Active IRS-Aided C-RSMA Systems
2024 IEEE Wireless Communications and Networking Conference (WCNC)(2024)
Abstract
This paper investigates robust resource allocation design for active intelligent reflecting surface (IRS)-aided cog-nitive rate-splitting multiple access (C-RSMA) systems. In particular, an active IRS is deployed to shape a favorable wireless communication environment for enhancing the system performance. We aim to maximize the system energy efficiency by jointly optimizing the common rate allocations for the users, the transmit beamforming vectors at the coordinated base stations, and the active beamforming matrix at the IRS. We formulate the resource allocation design as a non-convex optimization problem taking into account the discrete nature of the IRS elements and the transmit power budget constraints of the base stations as well as the active IRS. To tackle the non-convex design problem, we propose a computationally effective iterative suboptimal algorithm. Simulation results reveal a nontrivial tradeoff between the system energy efficiency and the number of the IRS elements. Moreover, our results unveil that active IRS elements equipped with limited bit-resolution of discrete amplifiers and phase shifters is sufficient to achieve a significant gain in the system energy efficiency.
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Key words
Resource Allocation Design,Optimization Problem,System Performance,Wireless,Energy Efficiency,Phase Shift,Base Station,Robust Design,Non-convex Problem,Discrete Phase,System Energy Efficiency,Beamforming Vector,Intelligent Reflecting Surface,Intelligent Reflecting Surface Elements,Colloidal,Objective Function,Additive Noise,Path Loss,Cognitive Networks,Linear Matrix Inequalities,Baseline Schemes,Non-convex Constraints,Feasible Point,Secondary Network,Interference Management,Imperfect Channel State Information,Convergence Tolerance,Channel Coefficients,Perfect Channel State Information,Block Coordinate Descent
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