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 […]
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 […]
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, […]
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 […]
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 […]