Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization

Abstract

Movable antenna (MA) is a promising technology to improve wireless communication performance by varying the antenna position in a given finite area at the transceivers to create more favorable channel conditions. In this paper, we investigate the MA-enhanced multiple-access channel (MAC) for the uplink transmission from multiple users each equipped with a single MA to a base station (BS) with a fixedposition antenna (FPA) array. A field-response based channel model is used to characterize the multi-path channel between the antenna array of the BS and each user's MA with a flexible position. To evaluate the MAC performance gain provided by MAs, we formulate an optimization problem for minimizing the total transmit power of users, subject to a minimum-achievable-rate requirement for each user, where the positions of MAs and the transmit powers of users, as well as the receive combining matrix at the BS are jointly optimized. To solve this non-convex optimization problem involving intricately coupled variables, we develop two algorithms based on zero-forcing (ZF) and minimum mean square error (MMSE) combining methods, respectively. Specifically, for each algorithm, the combining matrix of the BS and the total transmit power of users are expressed as a function of the MAs' position vectors, which are then optimized by using the gradient descent method in an iterative manner. It is shown that the proposed ZF-based and MMSE-based algorithms can converge to high-quality suboptimal solutions with low computational complexities. Simulation results demonstrate that the proposed solutions for MA-enhanced multiple access systems can significantly decrease the total transmit power of users as compared to conventional FPA systems under both perfect and imperfect field-response information.

Entrepreneurship education of University of Hong Kong : positioning and implementation

“…In contrast to conventional MIMO systems, which comprise a set of antenna elements mounted at fixed locations, MA systems can utilize the full spatial DoFs within the available spatial transmitter area by leveraging the flexible movement of the MAs. Moreover, since MA systems require only a small number of antenna elements to exploit the available DoFs, the computational complexity entailed by the required signal processing is significantly reduced compared to holographic MIMO systems [7]- [9].…”

“…For instance, in [8], a suboptimal algorithm based on alternating optimization (AO) was proposed for MA-enabled MIMO systems, where both the base station (BS) and multiple users were equipped with MAs. Also, the authors in [9] considered a multiuser MA-enabled uplink communication system comprising multiple single-MA users and a BS equipped with a fixed antenna array. The BS employed zero-forcing (ZF) or minimum mean square error (MMSE) combining and the MA positions were adjusted using a gradient descent (GD) method.…”

“…The BS employed zero-forcing (ZF) or minimum mean square error (MMSE) combining and the MA positions were adjusted using a gradient descent (GD) method. However, both [8] and [9] assume optimistically that the positions of MA elements can be adjusted freely within a given region, which may not be practical. In the prototype designs of MA-enabled systems shown in [10] and [11], the motion control of the employed electromechanical devices is discrete with finite precision.…”

“…In the prototype designs of MA-enabled systems shown in [10] and [11], the motion control of the employed electromechanical devices is discrete with finite precision. Thus, the transmitter area is quantized in a pace [11], leading to a finite spatial resolution instead of the infinite resolution assumed in [7]- [9]. Moreover, the AO-based algorithm in [8] and the GD-based method in [9] cannot guarantee the joint optimality of the BS beamformer and the MA positions, as their performance highly relies on the selection of the initial point.…”

“…Thus, the transmitter area is quantized in a pace [11], leading to a finite spatial resolution instead of the infinite resolution assumed in [7]- [9]. Moreover, the AO-based algorithm in [8] and the GD-based method in [9] cannot guarantee the joint optimality of the BS beamformer and the MA positions, as their performance highly relies on the selection of the initial point. Thus, in this paper, we investigate for the first time the jointly globally optimal design of the BS beamforming matrix and MA positions for a multiuser MA-enabled downlink system with a spatially discrete transmitter area to fully reveal the potential of MA-enabled systems.…”