On the Performance of Quantum Approximate Optimization-Based User Grouping for Rate-Splitting Multiple Access

Abstract

This letter investigates quantum approximate optimization algorithm (QAOA)-based user grouping for clustered rate-splitting multiple access (RSMA) with imperfect channel state information (CSI). In each local group, one common stream and multiple private streams are jointly transmitted. Hence, the grouping decision determines the common-rate bottleneck and private-stream interference. We construct a compatibility graph that combines robust common-stream decodability, private-stream separability, channel-strength balance, and CSI reliability. The fixed-size grouping problem is formulated as a quadratic unconstrained binary optimization (QUBO) problem and mapped to an Ising Hamiltonian for QAOA-based candidate generation. Retained candidates are evaluated by the RSMA stream model using sample-average approximation (SAA) and robust scalar-channel envelopes. Simulations show that the proposed scheme approaches exhaustive grouping with fewer continuous evaluations, improves outage behavior over baseline grouping rules, and supports larger local groups.