arXiv subjects

Tianqu Kang

Publications and source records attributed to Tianqu Kang.

4 recordsLinked to original sources

arXiv2025

Pixel-based Reconfigurable Beamforming Networks Emulating Physical Movement in FAS

The concept of Fluid Antenna Systems (FAS) has emerged as an attractive new system technology for use in sixth-generation (6G) wireless systems. However, most FAS implementations rely on mechanical antenna movement and thus are too slow to be useful. In this paper, a novel pixel-based reconfigurable beamforming network (PRBFN) is used to emulate movement in Fluid Antenna Systems (FASs). Using the insight that changing an antenna's physical position is equivalent to changing radiation patterns that satisfy the desired pattern correlation, the PRBFN is used to control the excitation current vectors of a multi-port antenna, thereby governing the pattern correlation. Key novelties of our work involve the selection of current vectors, and the methodology for scaling the PRBFN to realize large-aperture FAS. Results are provided for our PRBFN combined with an FAS (denoted as a PRBFN-FAS) when the equivalent physical movement is set to 1.5 wavelengths. Measurements demonstrate that the PRBFN-FAS provides the desired spatial correlation, including the Bessel function relation from Clarke's model across a 5\% bandwidth, satisfying FAS requirements. System-level experiments confirm the viability of the PRBFN-FAS in communication scenarios.

eess.SP↗

arXiv2024

Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks

Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data. To lessen the computational burden on resource-limited devices, combining low-rank adaptation (LoRA) with federated learning enables parameter-efficient fine-tuning. Additionally, the split FedFT architecture partitions an FM between edge devices and a central server, reducing the necessity for complete model deployment on individual devices. However, the risk of privacy eavesdropping attacks in FedFT remains a concern, particularly in sensitive areas such as healthcare and finance. In this paper, we propose a split FedFT framework with differential privacy (DP) over wireless networks, where the inherent wireless channel noise in the uplink transmission is utilized to achieve DP guarantees without adding an extra artificial noise. We shall investigate the impact of the wireless noise on convergence performance of the proposed framework. We will also show that by updating only one of the low-rank matrices in the split FedFT with DP, the proposed method can mitigate the noise amplification effect. Simulation results will demonstrate that the proposed framework achieves higher accuracy under strict privacy budgets compared to baseline methods.

cs.LG↗

arXiv2024

The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective

Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to the model weights. However, FL faces a significant challenge in terms of large communication overhead when transmitting these model weights. To address this issue, quantization is commonly employed. Nevertheless, the presence of quantized Gaussian noise introduces complexities in understanding privacy protection. This research paper investigates the impact of quantization on privacy in FL systems. We examine the privacy guarantees of quantized Gaussian mechanisms using Rényi Differential Privacy (RDP). By deriving the privacy budget of quantized Gaussian mechanisms, we demonstrate that lower quantization bit levels provide improved privacy protection. To validate our theoretical findings, we employ Membership Inference Attacks (MIA), which gauge the accuracy of privacy leakage. The numerical results align with our theoretical analysis, confirming that quantization can indeed enhance privacy protection. This study not only enhances our understanding of the correlation between privacy and communication in FL but also underscores the advantages of quantization in preserving privacy.

cs.LG↗

arXiv2022

Optimization of a Real-Time Wavelet-Based Algorithm for Improving Speech Intelligibility

The optimization of a wavelet-based algorithm to improve speech intelligibility along with the full data set and results are reported. The discrete-time speech signal is split into frequency sub-bands via a multi-level discrete wavelet transform. Various gains are applied to the sub-band signals before they are recombined to form a modified version of the speech. The sub-band gains are adjusted while keeping the overall signal energy unchanged, and the speech intelligibility under various background interference and simulated hearing loss conditions is enhanced and evaluated objectively and quantitatively using Google Speech-to-Text transcription. A universal set of sub-band gains can work over a range of noise-to-signal ratios up to 4.8 dB. For noise-free speech, overall intelligibility is improved, and the Google transcription accuracy is increased by 16.9 percentage points on average and 86.7 maximum by reallocating the spectral energy toward the mid-frequency sub-bands. For speech already corrupted by noise, improving intelligibility is challenging but still realizable with an increased transcription accuracy of 9.5 percentage points on average and 71.4 maximum. The proposed algorithm is implementable for real-time speech processing and comparatively simpler than previous algorithms. Potential applications include speech enhancement, hearing aids, machine listening, and a better understanding of speech intelligibility.

cs.SD↗