6 papers
Difference-of-Convex Regularization for Graph Learning by Differentiable Programming
Liping Tao, Chee Wei Tan
Laplacian-regularized minimization is fundamental in signal processing and machine learning, but explicit construction of the dense graph Laplacian pseudoinverse can be computation…
DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming
Chee Wei Tan, Siya Chen
Next-generation wireless networks, including satellite-to-Open RAN systems, demand agile and intelligent resource management capable of handling dynamic multi-user interference und…
Adversarial Water-Filling: Theory, Algorithms, and a Domain-Specific Wireless Foundation Model
Xindi Tong, Chee Wei Tan, H. Vincent Poor
Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case interference. This formulation natu…
Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey
Liping Tao, Xindi Tong, Chee Wei Tan
Low earth orbit (LEO) satellite networks are emerging as important infrastructure for global connectivity and space-based sensing, while their rapidly varying geometry, heterogeneo…
Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
Liping Tao, Chee Wei Tan
Spectrum cartography reconstructs spatial radio fields from sparse and heterogeneous wireless measurements, underpinning many sensing and optimization tasks in wireless networks. A…
Learning to Optimize by Differentiable Programming
Liping Tao, Xindi Tong, Chee Wei Tan
Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost. This tutorial highlights a shift in optimization: using differentiabl…