Sagar Shrestha, Xiao Fu

International Conference on Learning Representations (ICLR) 2024

Unsupervised domain translation is the problem of learning a mapping between two domains without paired data. Existing methods (e.g. CycleGAN and follow-up works) generally tackle the problem with distribution matching objectives. A fundamental issue with these approaches is the the "non-identifiability" of translation maps due to the existence of multiple solutions. This results in "content-misaligned" translations in practice. Our work proposes a simple yet provable solution to address this issue. We reveal interesting insights into the nature of the UDT problem. The proposed identifiable solution is demonstrated to be effective in avoiding the content-misalignment issue.

Sagar Shrestha, Xiao Fu, Mingyi Hong

IEEE Transactions on Signal Processing (TSP) 2023

Joint Beamforming and Antenna selection is a mix of non-convex continuous and combinatorial optimization problem. Existing methods are based on continuous convex approximation or greedy solutions that are suboptimal. We address this issue by first proposing an optimal Branch and Bound (B&B) algorithm for the problem. To address the potential scalability issue of the B&B, we propose a GNN based policy learnt via imitation learning to speed up the B&B. The proposed approach is demonstrated to be effective in achieving near-optimal solutions with significantly reduced computational complexity.

Sagar Shrestha, Xiao Fu

IEEE Transactions on Signal Processing (TSP) 2023

Classic and deep learning-based GCCA algorithms seek low-dimensional common representations of data entities from multiple “views” (e.g., audio and image) using linear transformations and neural networks, respectively. When the views are acquired and stored at different computing agents (e.g., organizations and edge devices) and data sharing is undesired due to privacy or communication cost considerations. This work puts forth a convergence-guaranteed communication-efficient federated learning framework for both linear and deep GCCA under the Maximum Variance (MAX-VAR) formulation. Compared to the unquantized version, the proposed algorithm enjoys more than 90% communication overhead reduction with virtually no loss in accuracy and convergence speed.

Subash Timilsina, Sagar Shrestha, Xiao Fu, Mingyi Hong

IEEE Transactions on Signal Processing (TSP) 2023

Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e.g., frequency and space) radio power propagation maps from limited sensor measurements. Existing SC approaches assume that sensors send real-valued (full-resolution) measurements to the fusion center, which is unrealistic. This work puts forth a quantized SC framework that generalizes the BTD and DGM-based SC to scenarios where heavily quantized sensor measurements are used. A maximum likelihood estimation (MLE)-based SC framework under a Gaussian quantizer is proposed. Recoverability of the radio map using the MLE criterion is characterized under realistic conditions, e.g., imperfect radio map modeling and noisy measurements. Simulations and real-data experiments are used to showcase the effectiveness of the proposed approach.

Sagar Shrestha, Xiao Fu, Mingyi Hong

IEEE Transactions on Signal Processing (TSP) 2022

The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an ill-posed tensor completion problem. In this work, an emitter radio map disaggregation-based approach is proposed, under which only individual emitters radio maps are modeled by DNNs. Using the learned DNNs, a fast nonnegative matrix factorization-based two-stage SC method and a performance-enhanced iterative optimization algorithm are proposed. Theoretical aspects—such as recoverability of the radio tensor, sample complexity, and noise robustness—under the proposed framework are characterized, and such theoretical properties have been elusive in the context of DL-based radio tensor completion. Experiments using synthetic and real-data from indoor and heavily shadowed environments are employed to showcase the effectiveness of the proposed methods.