Low-Complexity 6DMA Rotation and Position Optimization Based on Statistical Channel Information

TL;DR

This paper introduces a low-complexity sequential optimization method for 6DMA systems that jointly optimizes antenna positions and rotations based on statistical channel information, improving efficiency without sacrificing performance.

Contribution

It proposes a novel sequential optimization approach for 6DMA design that reduces computational complexity compared to traditional methods.

Findings

Achieves similar performance to alternating optimization with lower complexity

Outperforms fixed-position/rotation antenna arrays in simulations

Significantly reduces computational time for 6DMA optimization

Abstract

The six-dimensional movable antenna (6DMA) is a promising technology to fully exploit spatial variation in wireless channels by allowing flexible adjustment of three-dimensional (3D) positions and rotations of antennas at the transceiver. In this paper, we consider a 6DMA-equipped base station (BS) and aim to maximize the average sum logarithmic rate of all users served by the BS by jointly designing 6DMA surface positions and rotations based on statistical channel information (SCI). Different from prior works on 6DMA design which use alternating optimization to iteratively update surface positions and rotations, we propose a new sequential optimization method that first determines the optimal rotations and then identifies feasible positions to realize these rotations under practical antenna placement constraints. Simulation results show that our proposed optimization scheme significantly reduces the computational complexity of conventional alternating optimization (AO), while achieving communication performance comparable to the AO-based approach and superior to existing fixed-position/rotation antenna arrays.