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25 January 2022

Block-sparse signal recovery via general total variation regularized sparse Bayesian learning

One of the main challenges in block-sparse signal recovery as encountered in e.g. multi-antenna mmWave channel models is block-patterned estimation without knowledge […]

13 May 2021

General Total Variation Regularized Sparse Bayesian Learning for Robust Block-Sparse Signal Recovery

Block-sparse signal recovery without knowledge of block sizes and boundaries, such as those encountered in multi-antenna mmWave channel models, is a hard […]

28 August 2020

Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, […]

31 October 2019

Low Complexity Sparse Channel Estimation for Wideband mmWave Systems

We consider the problem of channel estimation in hybrid transceiver architectures operating in millimeter wave (mmWave) band. Due to the dynamic features […]

13 May 2019

Signal Reconstruction Performance under Quantized Noisy Compressed Sensing

We study rate-distortion (RD) performance of various single-sensor compressed sensing (CS) schemes for acquiring sparse signals via quantized/encoded noisy linear measurements, motivated […]

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