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Siya Yao

Publications and source records attributed to Siya Yao.

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Hybrid Beamforming for Subarray-Level Movable Antenna Enhanced MU-MIMO Communications

This study investigates subarray-level movable antenna (MA) architecture for multi-user MIMO (MU-MIMO) systems. Unlike conventional systems with fixed-position antennas (FPAs), the proposed scheme harnesses the additional positional degrees of freedom (DoFs) of movable subarrays to enhance spatial multiplexing capabilities for both multi-user and multi-stream communications. Our objective is to maximize the overall system spectral efficiency by jointly optimizing the hybrid beamforming design and the positions of all subarrays. To tackle this challenging non-convex optimization problem, we first adopt a block diagonalization (BD) based digital precoder to effectively eliminate multi-user interference. Subsequently, the joint optimization of the analog beamformer and the subarray positions is efficiently solved using a sequential interference cancellation (SIC)-based algorithm. Simulation results demonstrate that the proposed SIC-MA method significantly outperforms the benchmark SIC-FPA scheme where subarrays are fixed.

eess.SP

Generalizable Speech Deepfake Detection via Information Bottleneck Enhanced Adversarial Alignment

Neural speech synthesis techniques have enabled highly realistic speech deepfakes, posing major security risks. Speech deepfake detection is challenging due to distribution shifts across spoofing methods and variability in speakers, channels, and recording conditions. We explore learning shared discriminative features as a path to robust detection and propose Information Bottleneck enhanced Confidence-Aware Adversarial Network (IB-CAAN). Confidence-guided adversarial alignment adaptively suppresses attack-specific artifacts without erasing discriminative cues, while the information bottleneck removes nuisance variability to preserve transferable features. Experiments on ASVspoof 2019/2021, ASVspoof 5, and In-the-Wild demonstrate that IB-CAAN consistently outperforms baseline and achieves state-of-the-art performance on many benchmarks.

cs.SD