collaborators

8 papers

eess.SP2026

Variational Bayes Estimation for Affine-Precoded Superimposed Pilots in Partially Connected Dual-Wideband Tera-Hertz MU-MIMO Systems

Abhisha Garg, Suraj Srivastava, Aditya K. Jagannatham

This work conceives two affine precoding based system models, common precoding with joint channel estimation (CP-JCE) and user-specific precoding for decoupled channel estimation (…

eess.SP2026

SEMIKHORN: Globally balanced affinities for mmWave Localization in MU mMIMO systems

Abhisha Garg, Raghav Shukla, Suraj Srivastava +1

This work conceives SEMIKHORN, a semisupervised channel charting (CC) framework for mmWave localization, which leverages t-SNEkhorn, a doubly stochastic variant of t-distributed St…

eess.SP2026

Subarray based Wideband Beamforming and Variational Sparse CSI Estimation for Low-Resolution MU THz MIMO Systems

Abhisha Garg, Suraj Srivastava, Akash Kumar +1

This work conceives a unified channel estimation and beamforming framework, formulated within the principles of variational Bayesian inference. Recognizing the limitations imposed…

eess.SP2026

Bayesian Learning-Aided Near-Field Channel Estimation for mmWave Hybrid MIMO systems employing Uniform Circular Array

Abhisha Garg, Priya Gupta, Suraj Srivastava +1

This work conceives a Ring-Bayes channel learning framework that unifies Bayesian learning with near-field channel estimation in millimeter-wave (mmWave) hybrid MIMO systems. As th…

eess.SP2026

Semi-Blind Channel Estimation and Hybrid Receiver Beamforming in the Tera-Hertz Multi-User Massive MIMO Uplink

Abhisha Garg, Suraj Srivastava, Varsha Dubey +2

We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, refl…

eess.SP2026

Terahertz Beamforming and Group Sparse Channel Estimation Relying on Low-Resolution ADCs in MU Hybrid MIMO systems

Abhisha Garg, Suraj Srivastava, Akash Kumar +3

A unified beamforming and channel estimation framework relying on Bayesian learning is conceived. Recognizing the limitations imposed by low-resolution analog-to-digital converter…