10 papers
Transformer-Based Sparse CSI Estimation for Non-Stationary Channels
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +4
Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility…
On the Fundamental Limits of LLMs at Scale
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +13
Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) re…
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…
Continual Learning for Wireless Channel Prediction
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +2
Modern 5G/6G deployments routinely face cross-configuration handovers--users traversing cells with different antenna layouts, carrier frequencies, and scattering statistics--which…