Search Results for author: Ahmet M. Elbir

Found 41 papers, 3 papers with code

Index Modulation for Integrated Sensing and Communications: A Signal Processing Perspective

no code implementations16 Jan 2024 Ahmet M. Elbir, Abdulkadir Celik, Ahmed M. Eltawil, Moeness G. Amin

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

no code implementations7 Nov 2023 Ahmet M. Elbir, Abdulkadir Celik, Ahmed M. Eltawil

Terahertz (THz) band is envisioned for the future sixth generation wireless systems thanks to its abundant bandwidth and very narrow beamwidth.

Near-field Hybrid Beamforming for Terahertz-band Integrated Sensing and Communications

no code implementations25 Sep 2023 Ahmet M. Elbir, Abdulkadir Celik, Ahmed M. Eltawil

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

no code implementations6 Sep 2023 Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas

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

no code implementations8 Aug 2023 Kumar Vijay Mishra, Ahmet M. Elbir, Koichi Ichige

In the past few years, deep learning (DL) techniques have been introduced for designing sparse arrays.

Feature Engineering Transfer Learning

Antenna Selection With Beam Squint Compensation for Integrated Sensing and Communications

no code implementations14 Jul 2023 Ahmet M. Elbir, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil

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

no code implementations22 Mar 2023 Ahmet M. Elbir, Kumar Vijay Mishra, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil

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

no code implementations9 Feb 2023 Ahmet M. Elbir, Wei Shi, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas

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.

Federated Learning

BSA-OMP: Beam-Split-Aware Orthogonal Matching Pursuit for THz Channel Estimation

no code implementations5 Feb 2023 Ahmet M. Elbir, Symeon Chatzinotas

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

no code implementations3 Feb 2023 Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas

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

no code implementations8 Nov 2022 Ahmet M. Elbir, Kumar Vijay Mishra, Abdulkadir Çelik, Ahmed M. Eltawil

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

no code implementations3 Nov 2022 Ahmet M. Elbir, Kumar Vijay Mishra, Sergiy A. Vorobyov, Robert W. Heath Jr

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.

Astronomy

Machine Learning for Metasurfaces Design and Their Applications

no code implementations2 Nov 2022 Kumar Vijay Mishra, Ahmet M. Elbir, Amir I. Zaghloul

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

no code implementations24 Sep 2022 Ahmet M. Elbir

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

no code implementations7 Aug 2022 Ahmet M. Elbir, Wei Shi, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas

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.

Federated Learning

Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities

no code implementations2 Aug 2022 Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas, Mehdi Bennis

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

no code implementations13 Jul 2022 Ahmet M. Elbir, Wei Shi, Kumar Vijay Mishra, Symeon Chatzinotas

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.

Federated Learning Multi-Task Learning

Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks

no code implementations24 Jun 2022 Ahmet M. Elbir, Wei Shi, Kumar Vijay Mishra, Anastasios K. Papazafeiropoulos, Symeon Chatzinotas

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.

BIG-bench Machine Learning

Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals

no code implementations6 May 2022 Ahmet M. Elbir, Sinem Coleri, Kumar Vijay Mishra

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.

Federated Learning

The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms

no code implementations14 Apr 2022 Ahmet M. Elbir, Kumar Vijay Mishra, M. R. Bhavani Shankar, Symeon Chatzinotas

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

no code implementations7 May 2021 Ahmet M. Elbir, Sinem Coleri, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Symeon Chatzinotas

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.

BIG-bench Machine Learning Federated Learning

Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming

no code implementations27 Feb 2021 Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas

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

no code implementations23 Feb 2021 Ahmet M. Elbir, Anastasios K. Papazafeiropoulos, Symeon Chatzinotas

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 Learning Privacy Preserving

Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems

no code implementations15 Feb 2021 Ahmet M. Elbir, Sinem Coleri, Kumar Vijay Mishra

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.

Federated Learning

Hybrid Federated and Centralized Learning

no code implementations13 Nov 2020 Ahmet M. Elbir, Sinem Coleri, Kumar Vijay Mishra

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.

Federated Learning

Vehicular Networks for Combating a Worldwide Pandemic: Preventing the Spread of COVID-19

no code implementations15 Oct 2020 Ahmet M. Elbir, Gokhan Gurbilek, Burak Soner, Anastasios K. Papazafeiropoulos, Pandelis Kourtessis, Sinem Coleri

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.

Humanitarian

Cognitive Learning-Aided Multi-Antenna Communications

no code implementations7 Oct 2020 Ahmet M. Elbir, Kumar Vijay Mishra

We discuss DL design challenges from the perspective of data, learning, and transceiver architectures.

Federated Learning Transfer Learning

A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces

no code implementations5 Sep 2020 Ahmet M. Elbir, Kumar Vijay Mishra

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

no code implementations25 Aug 2020 Ahmet M. Elbir, Sinem Coleri

Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output.

Federated Learning

A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback

no code implementations19 Jun 2020 Ahmet M. Elbir

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

no code implementations2 Jun 2020 Ahmet M. Elbir, Burak Soner, Sinem Coleri, Deniz Gunduz, Mehdi Bennis

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.

Autonomous Driving Federated Learning +3

Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO

no code implementations20 May 2020 Ahmet M. Elbir, Sinem Coleri

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.

BIG-bench Machine Learning Federated Learning

Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning

no code implementations24 Apr 2020 Ahmet M. Elbir, Kumar Vijay Mishra

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.

Direction of Arrival Estimation Transfer Learning

Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

1 code implementation29 Jan 2020 Ahmet M. Elbir, A Papazafeiropoulos, P. Kourtessis, S. Chatzinotas

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

no code implementations20 Dec 2019 Ahmet M. Elbir, Kumar Vijay Mishra, M. R. Bhavani Shankar, Björn Ottersten

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

1 code implementation9 Dec 2019 Ahmet M. Elbir

This letter introduces a deep learning (DL) framework for direction-of-arrival (DOA) estimation.

Classification General Classification

V-Shaped Sparse Arrays For 2-D DOA Estimation

1 code implementation1 Dec 2019 Ahmet M. Elbir

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.

Direction of Arrival Estimation

Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

no code implementations11 Nov 2019 Ahmet M. Elbir, Anastasios Papazafeiropoulos

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

no code implementations31 Oct 2019 Ahmet M. Elbir, Kumar Vijay Mishra

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

no code implementations27 Feb 2018 Ahmet M. Elbir, Kumar Vijay Mishra, Yonina C. Eldar

Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna.

General Classification Multi-class Classification

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