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Secure MIMO Communication Relying on Movable Antennas

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arxiv 2403.04269 v1 pith:F4VE3X7W submitted 2024-03-07 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords systemmatrixfixedmethodantennacommunicationcovariancemimo
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This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system's physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix and the MAs' positions under the constraints of the maximum transmit power and the minimum distance between MAs. To solve this non-convex problem with highly coupled optimization variables, the block coordinate descent (BCD) method is applied to alternately update the variables. Specifically, we first reformulate the SR into a tractable form by utilizing the minimum mean square error (MMSE) method, and derive the optimal TPC matrix and the AN covariance matrix with fixed MAs' positions by applying the Lagrangian multiplier method in semi-closed forms. Then, the majorization-minimization (MM) algorithm is employed to iteratively optimize each MA's position while keeping others fixed. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms and the significant advantages of the MA-aided system over conventional fixed position antenna (FPA)-based system in enhancing system's security.

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Cited by 2 Pith papers

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

  1. Flexible Cylindrical Arrays with Movable Antennas for MISO System: Beamforming and Position Optimization

    eess.SP 2025-01 conditional novelty 5.0 of 10

    Movable antennas on a cylindrical array, optimized with fractional programming and a constrained Adam algorithm, are simulated to improve multi-user sum rate by up to 31% over fixed antennas.

  2. Movable Antenna Aided NOMA: Joint Antenna Positioning, Precoding, and Decoding Design

    cs.IT 2024-12 conditional novelty 5.0 of 10

    A two-loop heuristic combining hippopotamus optimization with alternating optimization jointly tunes MA positions, precoding, and adaptive SIC decoding to improve the max-min rate in simulated MA-NOMA downlinks.

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