This paper investigates a two-timescale uplink transmission framework for a fluid antenna-enabled multiuser multi-input multi-output system (MIMO-FAS). Antenna positions are optimized based on statistical channel state information (CSI), while beamforming vectors at the base station (BS) adapt to instantaneous CSI. Under a Rician fading channel with imperfect CSI, we establish a linear minimum mean square error (LMMSE)-based channel estimation approach and derive a closed-form expression for the achievable uplink rate using a low-complexity maximal-ratio-combining (MRC) detector. The optimization problem is formulated as a minimum user rate maximization problem by optimizing the fluid antenna positions, subject to the feasible region and the minimum spacing distance constraints. To address this non-convex problem, a genetic algorithm (GA) method is proposed, encoding antenna configurations as population individuals. Additionally, an accelerated gradient ascent algorithm is proposed to enhance computational efficiency. Numerical results validate the mathematical derivations and demonstrate that the proposed two-timescale transmission strategy significantly outperforms traditional FPA systems, with both algorithms achieving enhanced gains.

This paper proposes an uplink two-timescale transmission scheme for a fluid antenna-enhanced multi-user multiinput multi-output system (MU-MIMO-FAS), where antenna positions are optimized based on statistical channel state information (CSI), and beamforming at the base station (BS) adapts to rapidly-varying instantaneous CSI. Using a Rician channel model with imperfect CSI, we employ the linear minimum mean square error (LMMSE) method for channel estimation and a maximal ratio combining (MRC) detector to derive a closed-form expression for the achievable rate. Subsequently, we formulate a minimum user rate maximization problem for antenna position design, subject to movement and spacing constraints, and utilize a genetic algorithm (GA) to solve this non-convex problem. Numerical results demonstrate that the proposed two-timescale MU-MIMO-FAS design significantly outperforms the traditional fixed-position antenna (FPA) system.

This study proposes a two-timescale transmission scheme for extremely large-scale reconfigurable intelligent surface aided (XL-RIS-aided) massive multi-input multi-output (MIMO) systems in the presence of visibility regions (VRs). The beamforming of base stations (BSs) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase shifts of RIS are configured based on slowly varying statistical CSI. Specifically, we first formulate a system model with spatially correlated Rician fading channels and introduce the concept of VRs. Then, we derive a closed-form approximate expression for the achievable rate and analyze the impact of VRs on system performance and computational complexity. Then, we solve the problem of maximizing the minimum user rate by optimizing the phase shifts of RIS through an algorithm based on accelerated gradient ascent. Finally, we present numerical results to validate the performance of the considered system from different aspects and reveal the low system complexity of deploying XL-RIS in massive MIMO systems with the help of VRs.