25 accepted papers
(Covariance matrix reconstruction has been the most widely used guiding objective in gridless direction-of-arrival (DoA) estimation for sparse linear arrays. Many semidefinite programming (SDP)-based methods fall under this category. Although deep learning-based approaches enable the construction of…
Sparse Bayesian Learning (SBL) is a popular sparse signal recovery method, and various algorithms exist under the SBL paradigm. In this paper, we introduce a novel re-parameterization that allows the iterations of existing algorithms to be viewed as special cases of a unified and general mapping fun…
We address the problem of the beam split (B-SP) caused by the spatial wideband (SW) effect in intelligent reflecting surface (IRS) aided wideband systems. The SW effect arises when the signal delay across the IRS aperture is comparable to the system sampling time. This leads to a B-SP, wherein the I…
We consider the problem of adaptive sensing for multi-path channel estimation in millimeter-wave (mmWave) communications system with single RF chain. Current adaptive sensing approaches either focus on estimating the single dominant path or assume apriori knowledge of the number of multi-path compon…
This paper introduces new and practically relevant non-Gaussian priors for the Sparse Bayesian Learning (SBL) framework applied to the Multiple Measurement Vector (MMV) problem. We extend the Gaussian Scale Mixture (GSM) framework to model prior distributions for row vectors, exploring the use of sh…
Deep neural networks (DNNs) have greatly benefited direction of arrival (DoA) estimation methods for speech source localization in noisy environments. However, their localization accuracy is still far from satisfactory due to the vulnerability to nonspeech interference. To improve the robustness aga…
We present a Light-Weight Sequential Sparse Bayesian Learning (LWS-SBL) algorithm as an alternative to the orthogonal matching pursuit (OMP) algorithm for the general sparse signal recovery problem. The proposed approach formulates the recovery problem under the Type-II estimation framework and the…
Multi-user massive MIMO signal detection from one-bit received measurements strongly depends on the wireless channel. To this end, majority of the model and learning-based approaches address detector design for the rich-scattering, homogeneous Rayleigh fading channel. Our work proposes detection for…
We propose a data-driven partial differential equation (PDE) identification scheme based on ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm minimization which can identify spatially-dependent PDEs from measurements. Spatially-dependent P…
A deep neural network using rectified linear units represents a continuous piecewise linear (CPWL) function and vice versa. Recent results in the literature estimated that the number of neurons needed to exactly represent any CPWL function grows exponentially with the number of pieces or exponential…
Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries. In this work we propose a novel Bayesian strategy to address the two dimensional harmonic retrieval problem, through remodeling and rep…
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. We propose a novel Sparse Bayesian Learning (SBL) method for block-sparse recovery based on popula…
Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-valued output or single residual block, the models fu…
Accurate and efficient methods for Direction of Arrival (DOA) estimation play an important role in mmWave channel estimation methods. This estimation procedure can potentially be affected by the different RF and analog components in the communication system. Such components add an unknown, nonlinear…
In this paper we analyse the performance of Sparse Bayesian Learning (SBL) in an environment with correlated sources. We provide two new perspectives to understand SBL's strategy for handling correlated sources. Using a Minimum Power Distortionless Response (MPDR) beamformer-based perspective of SBL…
While deep neural networks (DNNs) have achieved state-of-the-art results in many fields, they are typically over-parameterized. Parameter redundancy, in turn, leads to inefficiency. Sparse signal recovery (SSR) techniques, on the other hand, find compact solutions to overcomplete linear problems. Th…
Utilizing the array processing minimum power distortionless response (MPDR) beamformer framework, we present a new perspective on the sparse Bayesian learning (SBL) algorithm used in sparse signal recovery. In addition to providing more insight into the SBL algorithm, this new perspective allows us…
Relative Impulse Responses (ReIRs) have several applications in speech enhancement, noise suppression and source localization for multi-channel speech processing in reverberant environments. Noise is usually assumed to be white Gaussian during the estimation of the ReIR between two microphones. We s…
This paper investigates semi-blind channel estimation in massive multiple-input multiple-output (MIMO) systems using different priors on data symbols. We derive two tractable expectation-maximization (EM) based channel estimation algorithms; one based on a Gaussian prior and the other one based on a…
In this paper, we present a novel multimodal sparse dictionary learning algorithm based on a hierarchical sparse Bayesian framework. The framework allows for enforcing joint sparsity across dictionaries without restricting the actual entries to be equal. We show that the proposed method is able to l…
In this paper we address the problem of learning shared sparse representation across several tasks. Assuming that the tasks share a common set of relevant features across all tasks is highly restrictive. This acts as a motivation to look for a generalized model which will be able to learn any correl…
We study the problem of low-rank and sparse decomposition from possibly noisy observations. We propose a novel objective function with nuclear norm on the low-rank term and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -`norm' on the sparse…
In this paper we present a novel Hierarchical Bayesian approach to estimate Relative Impulse Response (ReIR) using short, noisy and reverberant microphone recordings. The information contained in ReIRs between two microphones is useful for a wide range of multichannel speech processing applications…
Methods for generating multizone soundfield have a wide range of applications. The desired dominant frequency bands to be generated and perceived, however, are not the same for different applications. Previous work proposed a low complexity algorithm for multizone wideband sound field generation usi…
We propose two novel methods of service delay control for multiuser wireless systems that employ a scheduling scheme based on the Cumulative Density Function (CDF) of user channels in correlated fading environments. The first method allows for different hard delay deadlines for the users. The second…