Joint Movable Antenna Positioning and RIS Partitioning for Sum-Rate Maximization

2026-06-09

AI Summary

This work addresses the problem of maximizing the achievable sum rate in downlink wireless communication by jointly optimizing the placement of movable antennas and the selection of active elements on a reconfigurable intelligent surface (RIS). The proposed approach co-designs three-dimensional antenna positioning, subarray-based beamforming, and RIS element partitioning to enhance desired signals and mitigate inter-user interference, subject to constraints on antenna spacing and transmit power. By synergistically integrating the spatial degrees of freedom offered by antenna mobility with the selective activation capability of RIS elements, the method introduces a novel design dimension for performance enhancement. An alternating optimization framework is developed, combining zero-forcing beamforming, low-complexity one-dimensional search, block coordinate descent, and CVX-based convex optimization. Numerical results demonstrate that the proposed scheme significantly outperforms benchmarks with fixed antenna positions or random RIS configurations, achieving substantial gains in system sum rate.

Abstract

This paper investigates the utility of the movable antenna (MA) and reconfigurable intelligent surface (RIS) framework for downlink wireless communications. In the considered scenario, a base station (BS) is equipped with two sub-arrays of MAs transmits signals to the users via the RIS. By jointly exploiting the antenna-positioning flexibility of MAs and the RIS element selection capability, the proposed joint MA-RIS framework introduces additional design degrees of freedom to enhance desired signals and mitigate inter-user interference, thereby maximizing the network sum-rate. To this end, we formulate a joint optimization problem involving MA positioning, sub-array beamforming, and RIS element selection, subject to the minimum antenna separation and transmit power constraints. The resulting problem is highly non-convex and challenging to solve directly. To address this issue, an alternating optimization framework is developed that decomposes the problem into three tractable subproblems. Specifically, zero-forcing beamforming is employed for transmit beamformer design, a low-complexity one-dimensional search is derived for RIS element selection, and the MA positioning problem is solved using block coordinate descent (BCD) and convex optimization techniques implemented via CVX. Simulation results demonstrate that the proposed joint MA-RIS framework significantly improves the achievable sum-rate compared with conventional fixed MAs and benchmark schemes with random configurations.

Problem

• movable antenna
• reconfigurable intelligent surface
• sum-rate maximization
• antenna positioning
• RIS partitioning

Innovation

• Movable Antenna
• Reconfigurable Intelligent Surface
• Sum-Rate Maximization
• Alternating Optimization
• Beamforming