Movable antennas boost wireless key generation when signals change slowly
Movable Antenna-Assisted Secret Key Generation over Temporally Correlated Channels
Information TheoryCryptography and Security
Summary
Wireless devices often create secret keys using how signals bounce around, but when signals stay similar for a long time, it's hard to make strong keys fast. The authors study how moving antennas around can create more randomness even when signals change slowly. They develop math tools to measure the new randomness and methods to pick the best antenna spots and beam directions to improve key rates. Their tests show moving antennas and optimizing both position and beam improves key generation rate significantly compared to fixed antennas.
What this means in practice
- •For wireless network engineers: Improve secret key rates in slow-changing wireless channels by optimizing antenna positions and beamforming jointly.
- •For security hardware designers: Design secure wireless devices using movable antennas to mitigate weak randomness from temporally correlated channels.
Authors
Zhiyu Huang, Chen Sun, Guyue Li, Guanghui Wen, Xianbin Wang
Abstract
Physical-layer key generation (PLKG) exploits reciprocal channel dynamics to generate symmetric keys, addressing the key distribution problem in wireless communications. However, strong temporal correlation in slow-fading environments limits fresh randomness and results in low key generation rates. Movable antennas (MAs) introduce spatial degrees of freedom through antenna repositioning, providing a new approach to addressing the limitation. This paper investigates MA-assisted PLKG over temporally correlated channels. A first-order Gauss-Markov process is utilized to characterize the temporal correlation of multipath gains. We characterize the full-sequence mutual information (MI) over temporally correlated channels. For fixed antenna positions and beamforming, we establish a finite-horizon approximation error bound that vanishes as both effective signal-to-noise ratios (SNRs) increase. We derive a closed-form expression for the one-step conditional MI and adopt it as a tractable key-information metric. A nonconvex problem is formulated to jointly optimize antenna positions and per-slot beamforming under transmit-power and minimum-spacing constraints. We develop an alternating optimization algorithm, combining an exact principal-eigenvector beamforming solution with feasible SCA-based projected gradient position updates. Simulation results demonstrate that the MA-assisted scheme outperforms the fixed-position antenna baseline. At a high temporal correlation coefficient, position optimization achieves approximately $51\%$ higher rates than fixed antennas. Joint position and beamforming optimization provides a further $106\%$ gain over position optimization alone, demonstrating more pronounced gains under strong temporal correlation.