Zhiyu Huang, Chen Sun, Guyue Li, Guanghui Wen, Xianbin Wang
原文摘要
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.
Ejafa Bassam, Konstantin Garbers, Yingsheng Geng, Dalton Jens, Jiaxu Qian, Guyue Liu
原文摘要
Data augmentation improves visual recognition by exposing models to synthetic training examples that encourage generalization beyond the observed data. Mixed-sample methods extend this idea by combining multiple images. However, many common approaches either mix only pairs of images or rely on fixed small layouts, limiting control over source count, visible area proportions, and induced supervision. We propose TreemapMix, a distribution-controlled multi-image augmentation that samples source weights from a Dirichlet distribution and assigns them to area-proportional regions using a treemap partition. The resulting known region areas support two complementary objectives. TreemapMix with soft cross-entropy (SCE) treats areas as soft class-probability targets, while TreemapMix with a Plackett-Luce (PL) objective converts areas into ordinal supervision. On ImageNet-1K under matched training recipes, TreemapMix-SCE achieves substantially lower calibration error than evaluated mixed-sample baselines, while TreemapMix-PL achieves the strongest Top-1 accuracy. Transfer experiments on object detection and instance segmentation suggest that TreemapMix-pretrained features remain useful beyond classification. These results show that controlled multi-image composition can expose the same region-area information as either probability-matching or rank-matching supervision. Code is available at https://github.com/Ejafa/treemapmix.