55 citations · 206 across the 25 of their papers we have counts for
29 papers
Multiple Support Recovery Using Very Few Measurements Per Sample
Lekshmi Ramesh, Chandra R. Murthy, Himanshu Tyagi
In the problem of multiple support recovery, we are given access to linear measurements of multiple sparse samples in . These samples can be partitioned into …
Uplink Performance Analysis of Cell-Free mMIMO Systems under Channel Aging
Ribhu Chopra, Chandra R. Murthy, Anastasios K. Papazafeiropoulos
In this paper, we investigate the impact of channel aging on the uplink performance of a cell-free~(CF) massive multiple-input multiple-output (mMIMO) system with a minimum mean sq…
Phase Transitions for Support Recovery from Gaussian Linear Measurements
Lekshmi Ramesh, Chandra R. Murthy, Himanshu Tyagi
We study the problem of recovering the common -sized support of a set of samples of dimension , using noisy linear measurements per sample. Most prior work has focuse…
Controllability of Linear Dynamical Systems Under Input Sparsity Constraints
Geethu Joseph, Chandra R. Murthy
In this work, we consider the controllability of a discrete-time linear dynamical system with sparse control inputs. Sparsity constraints on the input arises naturally in networked…
Sample-Measurement Tradeoff in Support Recovery under a Subgaussian Prior
Lekshmi Ramesh, Chandra R Murthy, Himanshu Tyagi
Data samples from with a common support of size are accessed through random linear projections (measurements) per sample. It is well-known that roughly …
Learned-SBL: A Deep Learning Architecture for Sparse Signal Recovery
Rubin Jose Peter, Chandra R. Murthy
In this paper, we present a computationally efficient sparse signal recovery scheme using Deep Neural Networks (DNN). The architecture of the introduced neural network is inspired…