Hybrid Array and Privacy-Preserving Signaling Optimization for NextG Wireless Communications

This PhD research tackles two critical challenges in NextG wireless networks: hybrid precoder design for wideband sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communications and privacy-preserving federated learning (FL) over wireless networks.

In the first part, we propose a novel hybrid precoding framework that integrates true-time delay (TTD) devices and phase shifters (PS) to counteract the beam squint effect - a significant challenge in the wideband sub-THz massive MIMO systems that leads to considerable loss in array gain. Unlike previous methods that only designed TTD values while fixed PS values and assuming unbounded time delay values, our approach jointly optimizes TTD and PS values under realistic time delays constraint. We determine the minimum number of TTD devices required to achieve a target array gain using our proposed approach. Then, we extend the framework to multi-user wideband systems and formulate a hybrid array optimization problem aiming to maximize the minimum data rate across users. This problem is decomposed into two sub-problems: fair subarray allocation, solved via continuous domain relaxation, and subarray gain maximization, addressed via a phase-domain transformation.

The second part focuses on preserving privacy in FL over wireless networks. First, we design a differentially-private FL algorithm that applies time-varying noise variance perturbation. Taking advantage of existing wireless channel noise, we jointly design differential privacy (DP) noise variances and users transmit power to resolve the tradeoffs between privacy and learning utility. Next, we tackle two critical challenges within FL networks: (i) privacy risks arising from model updates and (ii) reduced learning utility due to quantization heterogeneity. Prior work typically addresses only one of these challenges because maintaining learning utility under both privacy risks and quantization heterogeneity is a non-trivial task. We approach to improve the learning utility of a privacy-preserving FL that allows clusters of devices with different quantization resolutions to participate in each FL round. Specifically, we introduce a novel stochastic quantizer (SQ) that ensures a DP guarantee and minimal quantization distortion. To address quantization heterogeneity, we introduce a cluster size optimization technique combined with a linear fusion approach to enhance model aggregation accuracy. Lastly, inspired by the information-theoretic rate-distortion framework, a privacy-distortion tradeoff problem is formulated to minimize privacy loss under a given maximum allowable quantization distortion. The optimal solution to this problem is identified, revealing that the privacy loss decreases as the maximum allowable quantization distortion increases, and vice versa.

This research advances hybrid array optimization for wideband sub-THz massive MIMO and introduces novel algorithms for privacy-preserving quantized FL with diverse precision. These contributions enable high-throughput wideband MIMO communication systems and privacy-preserving AI-native designs, aligning with the performance and privacy protection demands of NextG networks.