[Paper Review] Multiuser Communications with Movable-Antenna Base Station: Joint Antenna Positioning, Receive Combining, and Power Control

Zhenyu Xiao, Xiangyu Pi+3 more|arXiv (Cornell University)|Aug 18, 2023

Advanced MIMO Systems OptimizationEngineering3 citations

TL;DR

This paper proposes a joint optimization framework for multiuser uplink communications using a base station equipped with multiple movable antennas (MAs), jointly optimizing antenna positions, receive combining vectors, and user transmit power to maximize the minimum user rate. By leveraging a two-loop particle swarm optimization (PSO) algorithm, the scheme achieves significant rate gains over fixed-antenna baselines, even under imperfect channel state information due to estimation errors in angles of departure and path gains.

ABSTRACT

Movable antenna (MA) is an emerging technology which enables a local movement of the antenna in the transmitter/receiver region for improving the channel condition and communication performance. In this paper, we study the deployment of multiple MAs at the base station (BS) for enhancing the multiuser communication performance. First, we model the multiuser channel in the uplink to characterize the wireless channel variation due to MAs' movements at the BS. Then, an optimization problem is formulated to maximize the minimum achievable rate among multiple users for MA-aided uplink multiuser communications by jointly optimizing the MAs' positions, their receive combining at the BS, and the transmit power of users, under the constraints of finite moving region for MAs, minimum inter-MA distance, and maximum transmit power of each user. To solve this challenging non-convex optimization problem, a two-loop iterative algorithm is proposed by leveraging the particle swarm optimization (PSO) method. Specifically, the outer-loop updates the positions of a set of particles, where each particle's position represents one realization of the antenna position vector (APV) of all MAs. The inner-loop implements the fitness evaluation for each particle in terms of the max-min achievable rate of multiple users with its corresponding APV, where the receive combining matrix of the BS and the transmit power of each user are optimized by applying the block coordinate descent (BCD) technique. Simulation results show that the antenna position optimization for MAs-aided BSs can significantly improve the rate performance as compared to conventional BSs with fixed-position antennas (FPAs).

Motivation & Objective

• To address the limited spatial degrees of freedom in conventional fixed-antenna base stations (FPAs) by enabling movable antennas (MAs) to dynamically adjust position for improved channel quality.
• To formulate and solve a non-convex optimization problem that jointly optimizes MA positions, receive combining, and user power control under practical constraints (finite moving region, minimum inter-MA distance, max transmit power).
• To evaluate the robustness of the proposed scheme under imperfect knowledge of the channel’s finite-rate information (FRI), including angle-of-arrival (AoA) and path-response coefficient (PRV) estimation errors.
• To demonstrate that MA-aided systems can outperform conventional FPA systems and existing schemes like maximum ratio combining (MRC) and zero-forcing (ZF) even with channel estimation errors.

Proposed method

• Models the multiuser uplink channel as a function of the antenna position vector (APV), capturing path-specific delays and phase shifts due to MA mobility.
• Formulates a max-min rate optimization problem to enhance fairness among users, subject to constraints on MA movement region, minimum separation between MAs, and individual user power limits.
• Develops a two-loop iterative PSO-based algorithm: the outer loop optimizes the APV via particle swarm, while the inner loop uses block coordinate descent (BCD) to jointly optimize receive combining and user power for each APV candidate.
• Implements fitness evaluation in the inner loop using the actual channel state (including AoA and PRV) to compute the max-min achievable rate, ensuring realistic performance assessment.
• Incorporates robustness analysis by modeling AoA errors as i.i.d. uniform variables and PRV errors as i.i.d. complex Gaussian variables to simulate imperfect FRI.
• Validates the algorithm using simulations under both perfect and imperfect FRI conditions, comparing performance against FPA, MRC, and ZF baselines.

Experimental results

Research questions

• RQ1 Can joint optimization of movable antenna positioning, receive combining, and user power control significantly improve the minimum achievable rate in uplink multiuser MIMO systems compared to fixed-antenna baselines?
• RQ2 How does the performance of the proposed MA-aided system degrade under imperfect channel state information, particularly due to errors in angle-of-arrival (AoA) and path-response coefficient (PRV) estimation?
• RQ3 Does the proposed two-loop PSO-based algorithm effectively converge to a high-quality solution for the non-convex joint optimization problem involving continuous antenna positions and discrete power control?
• RQ4 How does the performance of the proposed scheme compare to conventional schemes like maximum ratio combining (MRC) and zero-forcing (ZF) in terms of fairness and robustness to channel estimation errors?
• RQ5 What is the impact of increasing AoA and PRV estimation errors on the optimal antenna position vector (APV) and resulting system rate performance?

Key findings

• The proposed MA-aided system achieves significantly higher minimum user rates than conventional fixed-antenna base stations (FPAs), demonstrating the advantage of exploiting spatial DoFs through antenna mobility.
• Even with large AoA estimation errors (e.g., μ = 0.2), the MA scheme outperforms the FPA scheme in terms of minimum achievable rate, indicating strong robustness to channel estimation inaccuracies.
• The performance gap between the proposed scheme and the ZF-based benchmark increases with higher AoA error, as ZF is more sensitive to misaligned beamforming due to incorrect AoA estimates.
• PRV estimation errors degrade the performance of the MA scheme, but the proposed algorithm maintains a performance advantage over FPA and ZF schemes even at high normalized variance (δ = 0.5) of PRV errors.
• The two-loop PSO algorithm effectively converges to a suboptimal solution that maximizes the min-rate, with simulation results confirming its effectiveness in solving the complex non-convex optimization problem.
• Robustness analysis shows that the proposed scheme maintains high performance under imperfect FRI, especially when the APV is optimized using estimated channel parameters, highlighting its practical viability.