Sparse Array Synthesis Including Mutual Coupling for MU-MIMO Average Capacity Maximization

Artikel i vetenskaplig tidskrift, 2022

A hybrid optimization algorithm including mutual coupling (MC) is proposed to synthesize an irregular sparse array (ISA) for average capacity maximization in a multi-user multiple-input multiple-output (MU-MIMO) system. The hybrid approach is composed of two phases to sub-optimally determine the location of a fixed number of omni-directional thin dipole antennas in an arbitrary sparse aperture via a diagonal antenna selection matrix. In Phase I, the problem is relaxed to a convex optimization by ignoring the MC and weakening the constraints. The output of Phase I is accounted as a reliable initial guess for the genetic algorithm (GA) in Phase II, which incorporates the MC effects through the coupling matrix and avoids the convex relaxation technique. The proposed approach outperforms the conventional GA with a random initial population, while it avoids trying several starting positions. Meanwhile, the undesirable appearance of grating lobes, due to the under-sampling, and the degrading MC effects are suppressed by aperiodicity. It is observed that, doubling the conventional inter-element spacing (half-wavelength) and finding the location of eight dipoles in a sparse aperture by the proposed method improves the average capacity by 3.27-11.9% when the number of users varies from two to eight and the signal-to-noise ratio (SNR) is 30dB.