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Found 36 papers, 1 papers with code
Inverse Extended Kalman Filter -- Part II: Highly Non-Linear and Uncertain Systems
The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF).
Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities
The sixth generation (6G) wireless networks aim to achieve ultra-high data transmission rates, very low latency and enhanced energy-efficiency.
Federated Learning for THz Channel Estimation
This paper addresses two major challenges in terahertz (THz) channel estimation: the beam-split phenomenon, i. e., beam misalignment because of frequency-independent analog beamformers, and computational complexity because of the usage of ultra-massive number of antennas to compensate propagation losses.
Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks
Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802. 11p vehicular channels.
An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures
Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications.
Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals
Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration.
The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms
The intelligent reflecting surface (IRS) alters the behavior of wireless media and, consequently, has potential to improve the performance and reliability of wireless systems such as communications and radar remote sensing.
Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging
Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns.
Phase Retrieval for Radar Waveform Design
The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform.
Co-Pulsing FDA Radar
By exploiting these DoFs, we develop C-Cube auto-pairing (CCing) algorithm, in which all the parameters are ipso facto paired during a joint estimation.
Joint Transmit and Reflective Beamformer Design for Secure Estimation in IRS-Aided WSNs
Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel.
Inverse Extended Kalman Filter -- Part I: Fundamentals
The purpose of this paper and the companion paper (Part II) is to develop the theory of I-EKF in detail.
Unique Bispectrum Inversion for Signals with Finite Spectral/Temporal Support
In this paper, we present a an approach that uniquely recovers signals with finite spectral support (band-limited signals) from at least $3B$ measurements of its bispectrum function (BF), where $B$ is the signal's bandwidth.
Joint Radar-Communications Processing from a Dual-Blind Deconvolution Perspective
When the radar receiver is not collocated with the transmitter, such as in passive or multistatic radars, the transmitted signal is also unknown apart from the target parameters.
IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces
We demonstrate that the IRS can provide a virtual or non-line-of-sight (NLOS) link between the radar and target leading to an enhanced radar performance.
Resource Allocation in Heterogeneously-Distributed Joint Radar-Communications under Asynchronous Bayesian Tracking Framework
Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems.
Heterogeneously-Distributed Joint Radar Communications: Bayesian Resource Allocation
In this paper, we focus on a heterogeneous radar and communication network (HRCN), which consists of various generic radars for multiple target tracking (MTT) and wireless communications for multiple users.
Information Geometry and Classical Cramér-Rao Type Inequalities
We examine the role of information geometry in the context of classical Cram\'er-Rao (CR) type inequalities.
Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming
Wireless communications and sensing at terahertz (THz) band are increasingly investigated as promising short-range technologies because of the availability of high operational bandwidth at THz.
Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems
Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data.
Non-Contact Vital Signs Detection with UAV-Borne Radars
Aggregating radar measurements with the information from other sensors is broadening the applications of drones in life-critical situations.
Hybrid Federated and Centralized Learning
We address this through a novel hybrid federated and centralized learning (HFCL) framework to effectively train a learning model by exploiting the computational capability of the clients.
Cognitive Learning-Aided Multi-Antenna Communications
We discuss DL design challenges from the perspective of data, learning, and transceiver architectures.
Displaced Sensor Automotive Radar Imaging
Contrary to these works, we develop a displaced multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar signal model under coarse synchronization with only frame-level alignment.
A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces
Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges.
Localization with One-Bit Passive Radars in Narrowband Internet-of-Things using Multivariate Polynomial Optimization
To support the low-capacity links to the fusion center (FC), the range estimates obtained at individual sensors are then converted to one-bit data.
Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications
We present a spectral co-design of a statistical multiple-input-multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system both of which concurrently operate within the same frequency band.
Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning
Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements.
Generalized Bayesian Cramér-Rao Inequality via Information Geometry of Relative $α$-Entropy
The relative $\alpha$-entropy is the R\'enyi analog of relative entropy and arises prominently in information-theoretic problems.
Cramér-Rao Lower Bounds Arising from Generalized Csiszár Divergences
We study the geometry of probability distributions with respect to a generalized family of Csisz\'ar $f$-divergences.
A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems.
Low-Complexity Limited-Feedback Deep Hybrid Beamforming for Broadband Massive MIMO Communications
In broadband millimeter-wave (mm-Wave) systems, it is desirable to design hybrid beamformers with common analog beamformer for the entire band while employing different baseband beamformers in different frequency sub-bands.
Signal Processing
Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System
We present an ultra-short range IEEE 802. 11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet-Thue-Morse sequences in the preamble.
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.
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.
Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional ($d\geq 2$) Off-the-Grid Frequencies
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous.
