Two-Layer Resource Allocation for RIS-Enhanced MA Systems With Imperfect CSI and HWIs

Abstract

Movable antenna (MA) and reconfigurable intelligent surface (RIS) technologies provide additional controllable degrees of freedom for improving wireless transmission. However, in RIS-enhanced MA systems, channel state information (CSI) uncertainties, transceiver hardware impairments (HWIs), and finite-resolution RIS phase shifts are tightly coupled with RIS reflection design and MA position optimization, making resource allocation (RA) particularly challenging. In this paper, we investigate a robust RA problem for a downlink RIS-enhanced MA system, where the base station beamforming matrix, continuous/discrete RIS phase shifts, and transmit/receive MA positions are jointly designed to maximize the system sum-rate. To cope with statistical CSI uncertainties, the original outage-constrained problem is first converted into a deterministic max-min problem with bounded channel uncertainties by using the sphere-bounding method. Then, a two-layer semi-analytical optimization framework is developed to solve the resulting max-min problem. Specifically, the inner layer derives a semi-closed-form solution for the worst-case channel uncertainties, while the outer layer alternately updates the resource variables via a block coordinate descent framework with semi-closed-form and low-complexity solutions. Simulation results demonstrate that, under the same power budget, the proposed algorithm improves the average sum-rate by 45.7% and 7.6% over fixed-position antenna and single-side MA schemes, respectively, outperforms the conventional rounding method in discrete RIS phase-shift optimization, and enhances robustness over the non-robust algorithm under imperfect CSI and HWIs.