4 papers
SR4-Fit: An Interpretable and Informative Classification Algorithm Applied to Prediction of U.S. House of Representatives Elections
Shyam Sundar Murali Krishnan, Dean Frederick Hougen
The growth of machine learning demands interpretable models for critical applications, yet most high-performing models are ``black-box'' systems that obscure input-output relations…
6G Twin: Hybrid Gaussian Radio Fields for Channel Estimation and Non-Linear Precoder Design for Radio Access Networks
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +3
This work introduces 6G Twin, the first end-to-end artificial intelligence (AI)-native radio access network (RAN) design that unifies (i) neural Gaussian Radio Fields (GRF) for com…
Channel Prediction under Network Distribution Shift Using Continual Learning-based Loss Regularization
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +4
Modern wireless networks face critical challenges when mobile users traverse heterogeneous network configurations with varying antenna layouts, carrier frequencies, and scattering…
Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models
Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer +4
Wireless channels in motion-rich urban microcell (UMi) settings are non-stationary; mobility and scatterer dynamics shift the distribution over time, degrading classical and deep e…