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Found 10 papers, 0 papers with code
Markovian Block Sparse Signal Detection Using One Bit Measurements
This paper presents a novel sparse signal detection scheme designed for a correlated Markovian Bernoulli-Gaussian sparse signal model, which can equivalently be viewed as a block sparse signal model.
Double-Private Distributed Estimation Algorithm Using Differential Privacy and a Key-Like Proportionate Matrix with Its Performance Analysis
In this brief, we present an enhanced privacy-preserving distributed estimation algorithm, referred to as the ``Double-Private Algorithm," which combines the principles of both differential privacy (DP) and cryptography.
Second-Order Nonlinearity Estimated and Compensated Diffusion LMS Algorithm: Theoretical Upper Bound, Cramer-Rao Lower bound, and Convergence Analysis
In this paper, an algorithm for estimation and compensation of second-order nonlinearity in wireless sensor setwork (WSN) in distributed estimation framework is proposed.
Diffusion LMS for Distributed Estimation over Wireless Networks with Inter-Node Interference Perturbation
In this paper, we investigate the diffusion least mean square (DLMS) algorithm over fading channel, where in addition to channel noise and path-loss the inter-node-interference (INI) among neighboring nodes of a host node is also taken into account.
Proportionate Adaptive Graph Signal Recovery
The first algorithm is the Proportionate-type Graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm.
Double-detector for Sparse Signal Detection from One Bit Compressed Sensing Measurements
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection.
Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks
A diffusion strategy is suggested for distributive learning of the sparse vector.
Dictionary Learning for Blind One Bit Compressed Sensing
In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain.
Bayesian Hypothesis Testing for Block Sparse Signal Recovery
The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal.
Iterative Bayesian Reconstruction of Non-IID Block-Sparse Signals
Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of the nonzero elements of the unknown signal to be independent and identically distributed (i. i. d.
