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Found 196 papers, 33 papers with code
A New Paradigm of User-Centric Wireless Communication Driven by Large Language Models
To address this gap, we propose a novel LLM-driven paradigm for wireless communication that innovatively incorporates the nature language to structured query language (NL2SQL) tool.
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution.
Fundamental Limit of Angular Resolution in Partly Calibrated Arrays with Position Errors
We then theoretically analyze the declining and plateau phases of CRB, and explain that the turning point of CRB in partly calibrated arrays is close to the angular resolution limit of distributed arrays without errors, demonstrating high resolution ability.
Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and Performance
In a multi-element, multi-path scenario, the EM-only mode improves the received signal power by 125% compared to the PBF-only mode.
Intelligent Reflecting Surface Based Localization of Mixed Near-Field and Far-Field Targets
By applying MUSIC on such a virtual signal, we are able to detect the far-field targets and the near-field targets, and estimate the angle-of-arrivals (AOAs) and/or ranges from the targets to the IRS.
Deep Task-Based Beamforming and Channel Data Augmentations for Enhanced Ultrasound Imaging
The proposed framework explores two approaches: (1) a Joint Beamformer and Classifier (JBC) that classifies the US images generated by the beamformer to provide feedback for image quality improvement; and (2) a Channel Data Classifier Beamformer (CDCB) that incorporates classification directly at the channel data representation within the beamformer's bottleneck layer.
A Hybrid Dynamic Subarray Architecture for Efficient DOA Estimation in THz Ultra-Massive Hybrid MIMO Systems
Terahertz (THz) communication combined with ultra-massive multiple-input multiple-output (UM-MIMO) technology is promising for 6G wireless systems, where fast and precise direction-of-arrival (DOA) estimation is crucial for effective beamforming.
Practical Modulo Sampling: Mitigating High-Frequency Components
Using a recovery method designed to handle quantization noise, we show that our approach effectively manages high-frequency artifacts, enabling reliable modulo recovery with realistic ADCs.
Low-Complexity Iterative Precoding Design for Near-field Multiuser Systems With Spatial Non-Stationarity
Furthermore, the proposed iterative precoding algorithms are proven to converge to RZF globally at an exponential rate.
Hybrid Near-field and Far-field Localization with Holographic MIMO
We consider a hybrid NF and FF localization scenario in this paper, where a base station (BS) locates multiple users in both NF and FF regions with the aid of a reconfigurable intelligent surface (RIS), which is a low-cost implementation of HMIMO.
Robust Phantom-Assisted Framework for Multi-Person Localization and Vital Signs Monitoring Using MIMO FMCW Radar
This study introduces a robust framework for multi-person localization and vital signs monitoring, using multiple-input-multiple-output frequency-modulated continuous wave radar, addressing challenges in real-world, cluttered environments.
Modulo Sampling: Performance Guarantees in The Presence of Quantization
In this case, we prove that $\mathrm{OF} > 3$ and $b > 3 + \log_2(\delta)$ for some $\delta > 1$ are sufficient conditions to unfold the modulo samples.
6DMA-Aided Hybrid Beamforming with Joint Antenna Position and Orientation Optimization
This paper studies a sub-connected six-dimensional movable antenna (6DMA)-aided multi-user communication system.
Compressed Sensing Based Residual Recovery Algorithms and Hardware for Modulo Sampling
It uses a non-linear modulo operator to map high-DR signals within the ADC range.
Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing
We first derive closed-form expressions for the gradients of the communications sum rate and sensing beampattern error with respect to the analog and digital precoders.
Holographic-Pattern Based Multi-User Beam Training in RHS-Aided Hybrid Near-Field and Far-Field Communications
The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users.
AI-Aided Kalman Filters
We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented.
Two Birds With One Stone: Enhancing Communication and Sensing via Multi-Functional RIS
Furthermore, we present two schemes that utilize MF-RISs to enhance the performance of integrated sensing and communication (ISAC).
Unrolled denoising networks provably learn optimal Bayesian inference
In recent years, algorithm unrolling has emerged as deep learning's answer to this age-old question: design a neural network whose layers can in principle simulate iterations of inference algorithms and train on data generated by the unknown prior.
Beam Focusing for Near-Field Multi-User Localization
The simulation results show that near-field localization accuracy based on a hybrid array or DMA can achieve performance close to that of fully digital arrays at a lower cost, and DMAs can attain better performance than hybrid solutions with the same aperture.
Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements
While DANSE provides good predictive/forecasting performance to model the temporal measurement data as a time series, its unsupervised learning lacks suitable regularization for tackling the BSCM task.
CmWave and Sub-THz: Key Radio Enablers and Complementary Spectrum for 6G
Sixth-generation (6G) networks are poised to revolutionize communication by exploring alternative spectrum options, aiming to capitalize on strengths while mitigating limitations in current fifth-generation (5G) spectrum.
Modulo Sampling in Shift-Invariant Spaces: Recovery and Stability Enhancement
Sampling shift-invariant (SI) signals with a high dynamic range poses a notable challenge in the domain of analog-to-digital conversion (ADC).
Holographic Intelligence Surface Assisted Integrated Sensing and Communication
Traditional discrete-array-based systems fail to exploit interactions between closely spaced antennas, resulting in inadequate utilization of the aperture resource.
Reliable Sub-Nyquist Spectrum Sensing via Conformal Risk Control
Such existing solutions can only provide guarantees in terms of false negative rate (FNR) in the asymptotic regime of large held-out data sets.
ECG-TEM: Time-based sub-Nyquist sampling for ECG signal reconstruction and Hardware Prototype
For such portable systems, minimizing power consumption and sampling rate is critical due to the substantial data generated during long-term ECG monitoring.
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy
The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization.
Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging
This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study.
NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning
This paper presents NeuPAN: a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner.
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system.
Deep, convergent, unrolled half-quadratic splitting for image deconvolution
Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm.
Real-Time Model-Based Quantitative Ultrasound and Radar
However, current quantitative imaging techniques that estimate physical properties from received signals, such as Full Waveform Inversion, are time-consuming and tend to converge to local minima, making them unsuitable for medical imaging.
Pragmatic Communication in Multi-Agent Collaborative Perception
Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration.
Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach
To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes.
Holographic Imaging with XL-MIMO and RIS: Illumination and Reflection Design
This paper addresses a near-field imaging problem utilizing extremely large-scale multiple-input multiple-output (XL-MIMO) antennas and reconfigurable intelligent surfaces (RISs) already in place for wireless communications.
Power-Efficient Sampling
Analog-to-digital converters (ADCs) facilitate the conversion of analog signals into a digital format.
Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas
Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communications.
Deep Internal Learning: Deep Learning from a Single Input
Yet, in many cases there is value in training a network just from the input at hand.
Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach
In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications.
Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling
Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G).
Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization
In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture.
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding
Hence, we provide conditions, in terms of the network width and the number of training samples, on these unfolded networks for the PL$^*$ condition to hold.
Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks
This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device, which adapts the model size during FL to reduce both computation and communication latency and increases the learning accuracy for devices with non-independent and identically distributed data.
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission.
Kernel Based Reconstruction for Generalized Graph Signal Processing
In this paper, we study GGSP signal reconstruction as a kernel ridge regression (KRR) problem.
On the Learning of Digital Self-Interference Cancellation in Full-Duplex Radios
The experimental results demonstrate the robustness of the model-based SIC methods, providing practical evidence of their effectiveness.
One-shot Learning for Channel Estimation in Massive MIMO Systems
In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems.
Model-Based Deep Learning
The methodologies that lie in the middle ground of this spectrum, thus integrating model-based signal processing with deep learning, are referred to as model-based deep learning, and are the focus here.
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference
Given receive SIR and model size, the optimization of the tradeoff yields two schemes for controlling the breathing depth that can be either fixed or adaptive to channels and the learning process.
Generalization and Estimation Error Bounds for Model-based Neural Networks
In practice, model-based neural networks exhibit higher generalization capability compared to ReLU neural networks.
25 Years of Signal Processing Advances for Multiantenna Communications
In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries.
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation
To handle statistical heterogeneity of users data, which is a second major challenge in FL, we extend BAAF to allow for appropriate local updates by the users and develop the Controlled Bayesian Air Aggregation Federated-learning (COBAAF) algorithm.
Hybrid RIS-Assisted MIMO Dual-Function Radar-Communication System
Dual-function radar-communication (DFRC) technology is emerging in next-generation wireless systems.
AI-Empowered Hybrid MIMO Beamforming
We discuss the architectural constraints and characterize the core challenges associated with hybrid beamforming optimization.
Hardware Implementation of Task-based Quantization in Multi-user Signal Recovery
In this work, we apply task-based quantization to multi-user signal recovery and present a hardware prototype implementation.
A Hardware Prototype of Wideband High-Dynamic Range ADC
The dynamic range of an ADC also plays an important role, and ideally, it should be greater than the signal's; otherwise, the signal will be clipped.
Hardware Prototype of a Time-Encoding Sub-Nyquist ADC
The suggested hardware and reconstruction approach retrieves FRI parameters with an error of up to -25dB while operating at rates approximately 10 times lower than the Nyquist rate, paving the way to low-power ADC architectures.
Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications
In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle.
Learning-Based Reconstruction of FRI Signals
While the deep unfolded network achieves similar performance as the classical FRI techniques and outperforms the encoder-decoder network in the low noise regimes, the latter allows to reconstruct the FRI signal even when the sampling kernel is unknown.
Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions
The diagnosis of primary bone tumors is challenging, as the initial complaints are often non-specific.
Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture
To this end, we exploit the orthogonality of the signals of partly calibrated arrays.
Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks
Simulations show that the proposed unfolding neural network achieves better recovery performance, convergence rate, and adaptivity than current baselines.
Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers
In this work, we propose a power-efficient hybrid MIMO receiver with low-quantization rate ADCs, by jointly optimizing the analog and digital processing in a hardware-oriented manner using task-specific quantization techniques.
Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mmWave) and terahertz (THz) frequency bands.
Near-field Localization with Dynamic Metasurface Antennas
We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy.
Joint Microstrip Selection and Beamforming Design for MmWave Systems with Dynamic Metasurface Antennas
Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems.
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM
The proposed split structure in the computation of the Kalman gain allows to compensate for state and measurement model mismatch effects independently.
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning
This method attempts to mitigate the negative effects of data heterogeneity in FL by aligning each client's feature space.
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks
Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals.
Seventy Years of Radar and Communications: The Road from Separation to Integration
Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age.
Unrolled Compressed Blind-Deconvolution
To reduce its computational and implementation cost, we propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time.
Design and Analysis of Hardware-limited Non-uniform Task-based Quantizers
Here, we propose a new framework based on generalized Bussgang decomposition that enables the design and analysis of hardware-limited task-based quantizers that are equipped with non-uniform scalar quantizers or that have inputs with unbounded support.
Unitary Approximate Message Passing for Matrix Factorization
We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas.
Physics Embedded Machine Learning for Electromagnetic Data Imaging
To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work.
Neural Greedy Pursuit for Feature Selection
NGP is efficient in selecting $N$ features when $N \ll P$, and it provides a notion of feature importance in a descending order following the sequential selection procedure.
Modulo Sampling of FRI Signals
In the context of modulo folding for FRI sampling, existing works operate at a very high sampling rate compared to the rate of innovation (RoI) and require a large number of samples compared to the degrees of freedom (DoF) of the FRI signal.
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization
For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form.
BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation
Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model.
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
Robust Unlimited Sampling Beyond Modulo
We show that our algorithm has the lowest mean-squared error while recovering the signal for a given sampling rate, noise level, and dynamic range of the compared to existing algorithms.
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resolution.
Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces
On the other hand, the dual-RIS assisted ISAC system improves both minimum user SINR as well as worst-case target illumination power at the targets, especially when the users and targets are not directly visible.
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios.
Graph Signal Sampling Under Stochastic Priors
We propose a generalized sampling framework for stochastic graph signals.
Analog Compressed Sensing for Sparse Frequency Shift Keying Modulation Schemes
Our results show that using a compressed sensing receiver allows for the simplification of the analog receiver with the trade off of a slight degradation in recovery performance.
Nonlinear Waveform Inversion for Quantitative Ultrasound
Due to its non-invasive and non-radiating nature, along with its low cost, ultrasound (US) imaging is widely used in medical applications.
Sparsity Based Non-Contact Vital Signs Monitoring of Multiple People Via FMCW Radar
To this end, we first show that spatial sparsity allows for both accurate detection of multiple people and computationally efficient extraction of their Doppler samples, using a joint sparse recovery approach.
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization
Decision making algorithms are used in a multitude of different applications.
Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization
This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region.
Ultrasound Signal Processing: From Models to Deep Learning
We aim to inspire the reader to further research in this area, and to address the opportunities within the field of ultrasound signal processing.
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.
6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing
As a consequence, it is expected that some portion of future 6G wireless communications may take place in the radiating near-field (Fresnel) region, in addition to the far-field operation as in current wireless technologies.
Transmit Precoder Design Approaches for Dual-Function Radar-Communication Systems
As radio-frequency (RF) antenna, component and processing capabilities increase, the ability to perform multiple RF system functions from a common aperture is being realized.
Channel Estimation with Simultaneous Reflecting and Sensing Reconfigurable Intelligent Metasurfaces
As an indicative application of HRISs, we formulate and solve the individual channels identification problem for the uplink of multi-user HRIS-empowered systems.
Deep Task-Based Analog-to-Digital Conversion
In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out.
Deep Proximal Learning for High-Resolution Plane Wave Compounding
Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler.
Task-Based Graph Signal Compression
The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation.
Learning to Estimate Without Bias
In such applications, we show that BCE leads to asymptotically consistent estimators.
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed.
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover
In this paper, we consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications.
Unsupervised Learned Kalman Filtering
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervised manner, i. e., without requiring ground-truth states.
Adaptive Time-Channel Beamforming for Time-of-Flight Correction
Adaptive beamforming can lead to substantial improvement in resolution and contrast of ultrasound images over standard delay and sum beamforming.
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
The smoothing task is core to many signal processing applications.
Residual Recovery Algorithm For Modulo Sampling
Existing recovery algorithms to recover the signal from its modulo samples operate at a high sampling rate and are not robust in the presence of noise.
Deep Unrolled Recovery in Sparse Biological Imaging
Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization.
Extended Successive Convex Approximation for Phase Retrieval with Dictionary Learning
The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models.
Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges
Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure.
Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond
As the standardization of 5G is being solidified, researchers are speculating what 6G will be.
Deep Algorithm Unrolling for Biomedical Imaging
In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques.
Multimodal Unrolled Robust PCA for Background Foreground Separation
Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene.
Compressed Ultrasound Imaging:from Sub-Nyquist Rates to Super-Resolution
This huge and promising market is constantly driven by new imaging and processing techniques.
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
State estimation of dynamical systems in real-time is a fundamental task in signal processing.
Learned super resolution ultrasound for improved breast lesion characterization
This study demonstrates the feasibility of in vivo human super resolution, based on a clinical scanner, to increase US specificity for different breast lesions and promotes the use of US in the diagnosis of breast pathologies.
Deep Unfolding with Normalizing Flow Priors for Inverse Problems
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements.
Graph Signal Restoration Using Nested Deep Algorithm Unrolling
We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually.
Learning to Sample: Data-Driven Sampling and Reconstruction of FRI Signals
Typically, Fourier samples of the FRI signals are used for reconstruction.
FRaC: FMCW-Based Joint Radar-Communications System via Index Modulation
The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling.
A Wasserstein Minimax Framework for Mixed Linear Regression
We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance between the learned and target mixture regression models.
Beam Focusing for Near-Field Multi-User MIMO Communications
As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays.
Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization
In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar.
Federated Learning: A Signal Processing Perspective
Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.
Bayesian Estimation of Graph Signals
We show that the proposed sample-GSP estimators outperform the sample-LMMSE estimator for a limited training dataset and that the parametric GSP-LMMSE estimators are more robust to topology changes in the form of adding/removing vertices/edges.
Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image
This necessitates sampling rates exceeding the Nyquist rate and the use of a large number of antenna elements to ensure sufficient image quality.
Structured LISTA for Multidimensional Harmonic Retrieval
In this paper, we show that the mutual inhibition matrix of a MHR problem naturally has a Toeplitz structure, which means that the degrees of freedom (DoF) of the matrix can be reduced from a quadratic order to a linear order.
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models.
LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.
Image Restoration by Deep Projected GSURE
In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP.
Cramér-Rao Bound Optimization for Joint Radar-Communication Design
We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios.
Unitary Approximate Message Passing for Sparse Bayesian Learning
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.
Model-Based Machine Learning for Communications
We present an introduction to model-based machine learning for communication systems.
Bayesian Federated Learning over Wireless Networks
This paper proposes a Bayesian federated learning (BFL) algorithm to aggregate the heterogeneous quantized gradient information optimally in the sense of minimizing the mean-squared error (MSE).
Phase Transitions in Frequency Agile Radar Using Compressed Sensing
FAR has improved anti-jamming performance over traditional pulse-Doppler radars under complex electromagnetic circumstances.
Unambiguous Delay-Doppler Recovery from Random Phase Coded Pulses
Our method encodes each pulse with a random phase, varying from pulse to pulse, and then processes the received samples jointly to resolve the range ambiguity.
Model-Based Deep Learning
We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision.
Statistical model-based evaluation of neural networks
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs).
Deep Networks for Direction-of-Arrival Estimation in Low SNR
The proposed architecture demonstrates enhanced robustness in the presence of noise, and resilience to a small number of snapshots.
Direction of Arrival Estimation
Multi-Label Classification
+1
FedRec: Federated Learning of Universal Receivers over Fading Channels
Wireless communications is often subject to channel fading.
Point of Care Image Analysis for COVID-19
Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19.
Compressed Fourier-Domain Convolutional Beamforming for Wireless Ultrasound imaging
Our results pave the way towards wireless US and demonstrate that high resolution US images can be produced using sub-Nyquist sampling and a small number of receiving channels.
Signal Processing Image and Video Processing
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.
Unrolling of Deep Graph Total Variation for Image Denoising
While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set.
Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks
In autonomous driving, radar systems play an important role in detecting targets such as other vehicles on the road.
Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests
A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large populations.
A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation
This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) with application to video foreground-background separation.
BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization
One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver.
Over-the-Air Federated Learning from Heterogeneous Data
Our analysis reveals the ability of COTAF to achieve a convergence rate similar to that achievable over error-free channels.
Deep-Learning Based Adaptive Ultrasound Imaging from Sub-Nyquist Channel Data
Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals.
eSampling: Energy Harvesting ADCs
We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints.
Massive MIMO As an Extreme Learning Machine
This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM).
Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications
Next generation wireless base stations and access points will transmit and receive using extremely massive numbers of antennas.
UVeQFed: Universal Vector Quantization for Federated Learning
We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
Learned Factor Graphs for Inference from Stationary Time Sequences
Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising
We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals.
Ensemble Wrapper Subsampling for Deep Modulation Classification
Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples.
Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy
The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization, but at the cost of low temporal resolution.
Image and Video Processing
Spatial Modulation for Joint Radar-Communications Systems: Design, Analysis, and Hardware Prototype
For the radar subsystem, our experiments show that the spatial agility induced by the GSM transmission improves the angular resolution and reduces the sidelobe level in the transmit beam pattern compared to using fixed antenna allocations.
Sampling Signals on Graphs: From Theory to Applications
In this article, we review current progress on sampling over graphs focusing on theory and potential applications.
Data-Driven Symbol Detection via Model-Based Machine Learning
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver.
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection
In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging.
Data-Driven Factor Graphs for Deep Symbol Detection
In particular, we propose to use machine learning (ML) tools to learn the factor graph, instead of the overall system task, which in turn is used for inference by message passing over the learned graph.
Sparse Array Design via Fractal Geometries
To that end, we introduce a fractal array design in which a generator array is recursively expanded according to its difference coarray.
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
In this article, we review algorithm unrolling for signal and image processing.
Deep Task-Based Quantization
In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine how to map an analog signal into its digital representation using deep learning tools.
Deep Neural Network Symbol Detection for Millimeter Wave Communications
This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed.
Deep learning in ultrasound imaging
We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications.
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data.
Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi.
Sample Efficient Toeplitz Covariance Estimation
In addition to results that hold for any Toeplitz $T$, we further study the important setting when $T$ is close to low-rank, which is often the case in practice.
Complex Trainable ISTA for Linear and Nonlinear Inverse Problems
Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications.
Deep Algorithm Unrolling for Blind Image Deblurring
We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID).
An Algorithm Unrolling Approach to Deep Image Deblurring
We first present an iterative algorithm that may be considered a generalization of the traditional total-variation regularization method on the gradient domain, and subsequently unroll the half-quadratic splitting algorithm to construct a neural network.
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.
Fast Deep Learning for Automatic Modulation Classification
We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy.
Deep Signal Recovery with One-Bit Quantization
Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.
Functional Nonlinear Sparse Models
Even if they are, recovering sparse solutions using convex relaxations requires assumptions that may be hard to meet in practice.
Dictionary Learning for Adaptive GPR Landmine Classification
For the case of abandoned anti-personnel landmines classification, we compare the performance of K-SVD with three online algorithms: classical Online Dictionary Learning, its correlation-based variant, and DOMINODL.
Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis
In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio.
The Global Optimization Geometry of Shallow Linear Neural Networks
We examine the squared error loss landscape of shallow linear neural networks.
Super-resolution Ultrasound Localization Microscopy through Deep Learning
This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios.
Cognitive Radar Antenna Selection via Deep Learning
Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna.
Convolutional Phase Retrieval via Gradient Descent
We study the convolutional phase retrieval problem, of recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements consisting of the magnitude of its cyclic convolution with a given kernel $\mathbf a \in \mathbb C^m $.
Sparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) produces an image of a sample by scanning it with a focused beam of electrons.
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Solving inverse problems with iterative algorithms is popular, especially for large data.
Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow
This paper presents a new algorithm, termed \emph{truncated amplitude flow} (TAF), to recover an unknown vector $\bm{x}$ from a system of quadratic equations of the form $y_i=|\langle\bm{a}_i,\bm{x}\rangle|^2$, where $\bm{a}_i$'s are given random measurement vectors.
DOLPHIn - Dictionary Learning for Phase Retrieval
We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase.
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
To recover $\beta^*$, we propose an $\ell_1$-regularized least-squares estimator.
On the Minimax Risk of Dictionary Learning
The main conceptual contribution of this paper is the adaption of the information-theoretic approach to minimax estimation for the DL problem in order to derive lower bounds on the worst case MSE of any DL scheme.
Compressed sensing for longitudinal MRI: An adaptive-weighted approach
Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies.
Performance Limits of Dictionary Learning for Sparse Coding
We consider the problem of dictionary learning under the assumption that the observed signals can be represented as sparse linear combinations of the columns of a single large dictionary matrix.
Compressive Shift Retrieval
The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.
Acceleration of Randomized Kaczmarz Method via the Johnson-Lindenstrauss Lemma
The Kaczmarz method is an algorithm for finding the solution to an overdetermined consistent system of linear equations Ax=b by iteratively projecting onto the solution spaces.
Numerical Analysis
On Quantum Detection and the Square-Root Measurement
In this paper we consider the problem of constructing measurements optimized to distinguish between a collection of possibly non-orthogonal quantum states.
Quantum Physics
