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Found 26 papers, 4 papers with code
Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
In particular, we propose losses that measure the length of the shortest path between subspaces viewed on a union of Grassmannians, and prove that it is possible for a DNN to approximate signal subspaces.
Novel Active Sensing and Inference for mmWave Beam Alignment Using Single RF Chain Systems
We propose a novel sensing approach for the beam alignment problem in millimeter wave systems using a single Radio Frequency (RF) chain.
Regularized Neural Detection for One-Bit Massive MIMO Communication Systems
The numerical results show that the combination of the DNN-augmented regularized GD and constellation-based loss function improve the quality of our one-bit detector, especially for higher order M-QAM constellations.
A DNN based Normalized Time-frequency Weighted Criterion for Robust Wideband DoA Estimation
However, the best-performing design of T-F weights is criterion-dependent in general.
Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems
We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput.
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
When the number of pieces is unknown, we prove that, in terms of the number of distinct linear components, the neural complexities of any CPWL function are at most polynomial growth for low-dimensional inputs and factorial growth for the worst-case scenario, which are significantly better than existing results in the literature.
Maximum Likelihood-based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL with Grid Refinement
The proposed optimization problem is non-convex, and we propose a majorization-minimization based iterative procedure to estimate the structured matrix; each iteration solves a semidefinite program.
A Generalized Proportionate-Type Normalized Subband Adaptive Filter
Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems.
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees
To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i. e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets.
A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem
Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries.
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 problem for compressed sensing (CS) algorithms.
Modified Vector Quantization for Small-Cell Access Point Placement with Inter-Cell Interference
In this paper, we explore the small-cell uplink access point (AP) placement problem in the context of throughput-optimality and provide solutions while taking into consideration inter-cell interference.
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection
Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module.
Multimodal Sparse Bayesian Dictionary Learning
This paper addresses the problem of learning dictionaries for multimodal datasets, i. e. datasets collected from multiple data sources.
Re-Weighted Learning for Sparsifying Deep Neural Networks
This paper addresses the topic of sparsifying deep neural networks (DNN's).
Relevance Subject Machine: A Novel Person Re-identification Framework
We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem.
A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL).
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers.
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.
Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem
We refer to the proposed method as rectified sparse Bayesian learning (R-SBL).
Type I and Type II Bayesian Methods for Sparse Signal Recovery using Scale Mixtures
In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR).
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices.
Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware
Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints.
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable.
Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation
We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizing signals' block structure.
