Modeling and Performance Analysis for Movable Antenna Enabled Wireless Communications
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Abstract (arXiv)
In this paper, we propose a novel antenna architecture called movable antenna (MA) to improve the performance of wireless communication systems. Different from conventional fixed-position antennas (FPAs) that undergo random wireless channel variation, the MAs with the capability of flexible movement can be deployed at positions with more favorable channel conditions to achieve higher spatial diversity gains. To characterize the general multi-path channel in a given region or field where the MAs are deployed, a field-response model is developed by leveraging the amplitude, phase, and angle of arrival/angle of departure (AoA/AoD) information on each of the multiple channel paths under the far-field condition. Based on this model, we then analyze the maximum channel gain achieved by a single receive MA as compared to its FPA counterpart in both deterministic and stochastic channels. First, in the deterministic channel case, we show the periodic behavior of the multi-path channel gain in a given spatial field, which can be exploited for analyzing the maximum channel gain of the MA. Next, in the case of stochastic channels, the expected value of an upper bound on the maximum channel gain of the MA in an infinitely large receive region is derived for different numbers of channel paths. The approximate cumulative distribution function (CDF) for the maximum channel gain is also obtained in closed form, which is useful to evaluate the outage probability of the MA system. Moreover, our results reveal that higher performance gains by the MA over the FPA can be acquired when the number of channel paths increases due to more pronounced small-scale fading effects in the spatial domain. Numerical examples are presented which validate our analytical results and demonstrate that the MA system can reap considerable performance gains over the conventional FPA systems with/without antenna selection (AS).
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Oct 2022.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based?cited by 1
- 2024Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approachcited by 0
- 2025Two-Timescale Joint Transmit and Pinching Beamforming for Pinching-Antenna Systemscited by 0
- 2025Deep Learning Optimization of Two-State Pinching Antennas Systemscited by 0
- 2026Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approachcited by 0
- 2026Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach2 May 2026
- 2025Deep Learning Optimization of Two-State Pinching Antennas Systems8 Jul 2025
- 2025Two-Timescale Joint Transmit and Pinching Beamforming for Pinching-Antenna Systems13 Apr 2025
- 2025Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based?12 Feb 2025
- 2024Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach21 Nov 2024