Capacity Maximization for FAS-Enhanced MIMO Systems: A Low Complexity Approach

In this paper, the problem of fluid antenna system (FAS)-enhanced multiple-input multiple-output (MIMO) capacity maximization is studied. Different from conventional MIMO systems equipped with fixed-position antennas (FPAs), we consider fluid antennas (FAs) can be flexibly repositioned in a fixed region, such that the MIMO channel between them is reconfigured to achieve higher capacity. We aim to jointly optimize the beamforming matrix and FA positions of the transmitter to maximize the MIMO capacity. First, we develop an alternating optimization (AO) algorithm to find a locally optimal solution by iteratively optimizing the transmit beamforming matrix and the positions of each FA with the other variable being fixed. For the beamforming matrix optimization, we adopt the conventional eigenmode transmission scheme, and the optimal power allocation is calculated by water-filling method. For the FA positions optimization, we identify a small set of promising candidates that can be exhaustively searched efficiently. Next, we analyze two special cases in which the MIMO-capacity maximization reduces to maximizing the sum of eigenvalues with respect to the FA positions. The proposed candidate set can then be directly applied to these cases as well. Numerical results show that the performance of the proposed method is close to that of the successive convex approximation (SCA)-based method, while consistently outperforming other benchmarks up to 30% in achievable rate. Most importantly, the proposed algorithm requires only 16.9% of the simulation time compared to the SCA-based method. This highlights its potential for practical and efficient position optimization of FAs, paving the way for real-world applications in 6G.