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Movable Antenna for Wireless Communications:Prototyping and Experimental Results

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arxiv 2408.08588 v1 pith:Y3YHKY2I submitted 2024-08-16 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords antennapowerresultsexperimentallambdaperformanceprototypesystem
verification ladder T0 review T1 audit T2 compute T3 formal
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Movable antenna (MA), which can flexibly change the position of antenna in three-dimensional (3D) continuous space, is an emerging technology for achieving full spatial performance gains. In this paper, a prototype of MA communication system with ultra-accurate movement control is presented to verify the performance gain of MA in practical environments. The prototype utilizes the feedback control to ensure that each power measurement is performed after the MA moves to a designated position. The system operates at 3.5 GHz or 27.5 GHz, where the MA moves along a one-dimensional horizontal line with a step size of 0.01{\lambda} and in a two-dimensional square region with a step size of 0.05{\lambda}, respectively, with {\lambda} denoting the signal wavelength. The scenario with mixed line-of-sight (LoS) and non-LoS (NLoS) links is considered. Extensive experimental results are obtained with the designed prototype and compared with the simulation results, which validate the great potential of MA technology in improving wireless communication performance. For example, the maximum variation of measured power reaches over 40 dB and 23 dB at 3.5 GHz and 27.5 GHz, respectively, thanks to the flexible antenna movement. In addition, experimental results indicate that the power gain of MA system relies on the estimated path state information (PSI), including the number of paths, their delays, elevation and azimuth angles of arrival (AoAs), as well as the power ratio of each path.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Near-Field Integrated Imaging and Communication in Distributed MIMO Networks

    eess.SP 2025-08 unverdicted novelty 5.0 of 10

    Distributed MIMO networks can image near-field targets by converting spatial-domain signals to the Fourier domain, using RMA for small objects and sparse Bayesian learning for large scenes.

  2. An Efficient Model-Driven Groupwise Approach for Atlas Construction

    cs.CV 2025-08 reject novelty 5.0 of 10

    The abstract and full text are two different papers, so the claimed DARC atlas-construction framework is never presented or supported.

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