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Found 44 papers, 3 papers with code
Hybrid Beamforming for Integrated Sensing and Communications With Low Resolution DACs
Therefore, this paper presents the design of Hybrid ANalog and Digital BeAmformers with Low resoLution (HANDBALL) digital-to-analog converters (DACs).
Near-Field Signal Processing: Unleashing the Power of Proximity
After nearly a century of specialized applications in optics, remote sensing, and acoustics, the near-field (NF) electromagnetic propagation zone is experiencing a resurgence in research interest.
The Curse of Beam-Squint in ISAC: Causes, Implications, and Mitigation Strategies
Integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize spectrum and thereby save cost and power.
Index Modulation for Integrated Sensing and Communications: A Signal Processing Perspective
A joint design of both sensing and communication can lead to substantial enhancement for both subsystems in terms of size, cost as well as spectrum and hardware efficiency.
NEAT-MUSIC: Auto-calibration of DOA Estimation for Terahertz-Band Massive MIMO Systems
Terahertz (THz) band is envisioned for the future sixth generation wireless systems thanks to its abundant bandwidth and very narrow beamwidth.
Spherical Wavefront Near-Field DoA Estimation in THz Automotive Radar
In this work, we examine near-field DoA estimation for THz automotive radar.
Near-field Hybrid Beamforming for Terahertz-band Integrated Sensing and Communications
Terahertz (THz) band communications and integrated sensing and communications (ISAC) are two main facets of the sixth generation wireless networks.
Terahertz-Band Direction Finding With Beam-Split and Mutual Coupling Calibration
Terahertz (THz) band is currently envisioned as the key building block to achieving the future sixth generation (6G) wireless systems.
Sparse Array Design for Direction Finding using Deep Learning
In the past few years, deep learning (DL) techniques have been introduced for designing sparse arrays.
Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications
In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution.
Spatial Path Index Modulation in mmWave/THz-Band Integrated Sensing and Communications
Then, we propose to employ a family of hybrid beamforming techniques such as hybrid, SI, and subcarrier-dependent analog-only, and beam-split-aware beamformers.
Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation
Unlike prior works which mostly ignore the impact of near-field beam-split (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB.
BSA-OMP: Beam-Split-Aware Orthogonal Matching Pursuit for THz Channel Estimation
Terahertz (THz)-band has been envisioned for the sixth generation wireless networks thanks to its ultra-wide bandwidth and very narrow beamwidth.
NBA-OMP: Near-field Beam-Split-Aware Orthogonal Matching Pursuit for Wideband THz Channel Estimation
The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth.
Millimeter-Wave Radar Beamforming with Spatial Path Index Modulation Communications
To efficiently utilize the wireless spectrum and save hardware costs, the fifth generation and beyond (B5G) wireless networks envisage integrated sensing and communications (ISAC) paradigms to jointly access the spectrum.
Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques
With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization.
Machine Learning for Metasurfaces Design and Their Applications
Metasurfaces (MTSs) are increasingly emerging as enabling technologies to meet the demands for multi-functional, small form-factor, efficient, reconfigurable, tunable, and low-cost radio-frequency (RF) components because of their ability to manipulate waves in a sub-wavelength thickness through modified boundary conditions.
A Unified Approach for Beam-Split Mitigation in Terahertz Wideband Hybrid Beamforming
However, the ultra-wide bandwidth in THz causes beam-split phenomenon due to the use of a single analog beamformer (AB).
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model
For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks.
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 Multi-Task Learning for THz Wideband Channel and DoA 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.
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.
A Hybrid Architecture for Federated and Centralized Learning
To address this common scenario, we propose a more efficient approach called hybrid federated and centralized learning (HFCL), wherein only the clients with sufficient resources employ FL, while the remaining ones send their datasets to the PS, which computes the model on behalf of them.
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 Learning for Physical Layer Design
Model-free techniques, such as machine learning (ML), have recently attracted much interest towards the physical layer design, e. g., symbol detection, channel estimation, and beamforming.
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.
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.
Vehicular Networks for Combating a Worldwide Pandemic: Preventing the Spread of COVID-19
As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids.
Cognitive Learning-Aided Multi-Antenna Communications
We discuss DL design challenges from the perspective of data, learning, and transceiver architectures.
A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces
Data-driven techniques, such as deep learning (DL), are critical in addressing these challenges.
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO
Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output.
A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback
To reduce the complexity and provide robustness, in this work, we propose a deep learning (DL) framework to deal with both hybrid beamforming and channel estimation.
Federated Learning in Vehicular Networks
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response.
Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO
In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users.
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.
Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems.
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.
DeepMUSIC: Multiple Signal Classification via Deep Learning
This letter introduces a deep learning (DL) framework for direction-of-arrival (DOA) estimation.
V-Shaped Sparse Arrays For 2-D DOA Estimation
The performance of the proposed method is evaluated with numerical simulations and it is shown that the proposed array geometries VCA and VNA can provide much less sensors as compared to the conventional coprime planar arrays.
Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach
In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output.
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
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.
