Turbo Compressed Sensing with Partial DFT Sensing Matrix
arXiv:1408.3904 · doi:10.1109/LSP.2014.2351822
Abstract
In this letter, we propose a turbo compressed sensing algorithm with partial discrete Fourier transform (DFT) sensing matrices. Interestingly, the state evolution of the proposed algorithm is shown to be consistent with that derived using the replica method. Numerical results demonstrate that the proposed algorithm outperforms the well-known approximate message passing (AMP) algorithm when a partial DFT sensing matrix is involved.
IEEE Signal Processing Letters, (Volume:22, Issue: 2), Feb. 2015
References in corpus (5)
- Message Passing Algorithms for Compressed Sensing
- The dynamics of message passing on dense graphs, with applications to compressed sensing
- Generalized Approximate Message Passing for Estimation with Random Linear Mixing
- Analysis of Compressed Sensing with Spatially-Coupled Orthogonal Matrices
- Achievable Rates of MIMO Systems with Linear Precoding and Iterative LMMSE Detection
Cited by in corpus (33)
- Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access
- A Unified Bayesian Inference Framework for Generalized Linear Models
- Expectation Propagation Detector for Extra-Large Scale Massive MIMO
- Joint Pilot Optimization, Target Detection and Channel Estimation for Integrated Sensing and Communication Systems
- On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices
- Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC
- Joint Visibility Region and Channel Estimation for Extremely Large-scale MIMO Systems
- Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO with One-Dimensional Antenna Array
- Asymptotically Optimal Estimation Algorithm for the Sparse Signal with Arbitrary Distributions
- Generalized Turbo Signal Recovery for Nonlinear Measurements and Orthogonal Sensing Matrices
- MIMO-OFDM-Based Massive Connectivity With Frequency Selectivity Compensation
- Variance State Propagation for Structured Sparse Bayesian Learning
- Interference-Cancellation-Based Channel Knowledge Map Construction and Its Applications to Channel Estimation
- Rigorous Dynamics of Expectation-Propagation-Based Signal Recovery from Unitarily Invariant Measurements
- Denoising-based Turbo Message Passing for Compressed Video Background Subtraction
- Cross Domain Iterative Detection for Orthogonal Time Frequency Space Modulation
- Macroscopic Analysis of Vector Approximate Message Passing in a Model Mismatch Setting
- On Capacity Optimality of OAMP: Beyond IID Sensing Matrices and Gaussian Signaling
- TARM: A Turbo-type Algorithm for Affine Rank Minimization
- Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning
- Algorithms for the Iterative Estimation of Discrete-Valued Sparse Vectors
- Generalized Expectation Consistent Signal Recovery for Nonlinear Measurements
- Spectral Method for Phase Retrieval: an Expectation Propagation Perspective
- Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
- On Sparse Vector Recovery Performance in Structurally Orthogonal Matrices via LASSO
- Low-Complexity Iterative Algorithms for (Discrete) Compressed Sensing
- D-OAMP: A Denoising-based Signal Recovery Algorithm for Compressed Sensing
- Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
- Construction of optimal spectral methods in phase retrieval
- Denoising-based Turbo Compressed Sensing
- Sparse Vector Recovery: Bernoulli-Gaussian Message Passing
- Gridless Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs
- Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing