Quantum-Inspired Generic Optimization for Multiuser Fluid-MIMO Communications and Sensing: Joint Port Selection and Beamforming

Abstract

With fluid multiple-input multiple-output (Fluid-MIMO) emerging as a promising technology for next-generation systems, jointly selecting transmit or receive ports and designing multiuser beamforming becomes a key challenge. Although beamforming with fixed port selection is tractable, their strong coupling results in a high-dimensional, nonconvex combinatorial problem that limits the scalability of conventional methods. To address this, this paper proposes a generic quantum-inspired optimization framework based on quadratic unconstrained binary optimization (QUBO) modeling to jointly optimize port selection and beamforming. Specifically, considering the objective function involves complex sub-problems, we formulate the overall task as a black-box binary optimization problem. To solve this, we propose a Taylor-based approximation technique to locally model the objective as a tractable QUBO form. This allows the leverage of the simulated bifurcation (SB) solver for efficient parallel search. We verify our proposed algorithm for three typical objectives: sum rate maximization, signal-to-interference-plus-noise ratio (SINR) balancing and sensing signal-clutter-noise ratio (SCNR) maximization. Simulation results demonstrate that the proposed algorithm converges rapidly within 10 iterations. In a dense 10-user system, it outperforms the simulated annealing (SA) baseline by 10.1% in sum rate and 53.1% in minimum SINR. Furthermore, it achieves a 27.4% SCNR gain over SA under a 10 dB communication constraint.