Browsing by Author "Aygül, Mehmet Ali"

Now showing items 1-17 of 17

Blind signal analysis

Aygül, Mehmet Ali; Naeem, Ahmed; Arslan, Hüseyin (Wiley, 2021)

Blind signal analysis (BSA) plays an essential role in wireless communication when the receiver does not know most or all of the received signal parameters. This chapter provides an in-depth understanding of BSA with ...
Channel modeling for 5G and beyond

Nazzal, Mahmoud; Aygül, Mehmet Ali; Arslan, Hüseyin (Institution of Engineering and Technology, 2020)

The wide variety in enabling technologies, operating scenarios, environments, and use cases required for the fifth generation (5G) communication system and beyond 5G (B5G) entails the availability of descriptive channel ...
Deep learning-assisted detection of PUE and jamming attacks in cognitive radio systems

Aygül, Mehmet Ali; Furqan, Haji Muhammad; Nazzal, Mahmoud; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2020)

Cognitive radio (CR)-based internet of things systems can be considered as an efficient solution for futuristic smart technologies. However, CRs are naturally vulnerable to two major security threats; primary user emulation ...
Deep learning-based optimal ris interaction exploiting previously sampled channel correlations

Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin (IEEE - Institute of Electrical and Electronics Engineers, Inc, 2021)

The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in ...
Deep RL-based spectrum occupancy prediction exploiting time and frequency correlations

Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2022)

In cognitive radio systems, predicting spectrum occupancies is a convenient alternative way to continuous spectrum sensing. It can provide information on spectrum usage and so empty spectrum bands can be used by secondary ...
Developing novel spectrum occupancy prediction and physical layer security techniques

Aygül, Mehmet Ali (İstanbul Medipol Üniversitesi Fen Bilimleri Enstitüsü, 2020)

Wireless communication systems have pervaded every aspect of our lives; from financial transactions to health records, from entertainment to work, from education to travelling. This extensive usage of wireless devices is ...
Dictionary learning-based beamspace channel estimation in millimeter-wave massive mimo systems with a lens antenna array

Nazzal, Mahmoud; Aygül, Mehmet Ali; Görçin, Ali; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2019)

Recent research considers the application of a lens antenna array in order to provide efficient beam selection in beamspace massive MIMO. Achieving the advantages of this beam selection paradigm requires efficient channel ...
Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models

Aygül, Mehmet Ali; Nazzal, Mahmoud; Sağlam, Mehmet İzzet; da Costa, Daniel Benevides; Ateş, Hasan Fehmi; Arslan, Hüseyin (MDPI, 2021)

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. ...
Estimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation

Nazzal, Mahmoud; Aygül, Mehmet Ali; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2022)

Obtaining accurate channel state estimates at reasonable training overheads remains a big challenge for the applicability of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). Recently, ...
Identification of distorted RF components via deep multi-task learning

Aygül, Mehmet Ali; Memişoğlu, Ebubekir; Çırpan, Hakan Ali; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2022)

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects ...
Joint estimation of multiple RF impairments using deep multi-task learning

Aygül, Mehmet Ali; Memişoğlu, Ebubekir; Arslan, Hüseyin (IEEE-Institute of Electrical and Electronics Engineers Inc., 2022)

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, ...
Primary user emulation and jamming attack detection in cognitive radio via sparse coding

Furqan, Haji Muhammad; Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin (Springer, 2020)

Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade ...
Signal relation-based physical layer authentication

Aygül, Mehmet Ali; Büyükçorak, Saliha; Costa, Daniel Benevides Da; Ateş, Hasan Fehmi; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2020)

Most physical-layer authentication techniques use channel information to prevent spoofing attacks. In such techniques, one must estimate the channel information for each authentication procedure. However, when the number ...
Sparse coding for transform domain-based sparse OFDM channel estimation

Nazzal, Mamoud; Aygül, Mehmet Ali; Görçin, Ali; Arslan, Hüseyin (IEEE-Inst Electrical Electronics Engineering Inc, 2019)

In orthogonal frequency division multiplexing (OFDM) systems, frequency domain pilot-aided channel estimation is based on interpolating a down-sampled version of the channel frequency response. This is achieved by transforming ...
Sparse coding with enhanced atom selection for FDD massive MIMO channel estimation

Nazzal, Mahmoud; Aygül, Mehmet Ali; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2021)

In sparse coding-based channel estimation, atom selection is based on jointly minimizing the sparsity and the error of the representation of the noisy measurement. However, this selection is not necessarily optimal in terms ...
Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM

Aygül, Mehmet Ali; Nazzal, Mahmoud; Ekti, Ali Rıza; Görçin, Ali; da Costa, Daniel Benevides; Ateş, Hasan Fehmi; Arslan, Hüseyin (Institute of Electrical and Electronics Engineers Inc., 2020)

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on ...
Using OMP and SD algorithms together in mm-Wave mMIMO channel estimation

Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin (Springer London Ltd, 2022)

Lens antenna array is considered as an effective beam selection mechanism in millimeter wave massive multiple input multiple output systems. Efficient channel estimation (CE) algorithms are required to use the advantage ...

Browsing by Author "Aygül, Mehmet Ali"

Blind signal analysis ﻿

Channel modeling for 5G and beyond ﻿

Deep learning-assisted detection of PUE and jamming attacks in cognitive radio systems ﻿

Deep learning-based optimal ris interaction exploiting previously sampled channel correlations ﻿

Deep RL-based spectrum occupancy prediction exploiting time and frequency correlations ﻿

Developing novel spectrum occupancy prediction and physical layer security techniques ﻿

Dictionary learning-based beamspace channel estimation in millimeter-wave massive mimo systems with a lens antenna array ﻿

Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models ﻿

Estimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation ﻿

Identification of distorted RF components via deep multi-task learning ﻿

Joint estimation of multiple RF impairments using deep multi-task learning ﻿

Primary user emulation and jamming attack detection in cognitive radio via sparse coding ﻿

Signal relation-based physical layer authentication ﻿

Sparse coding for transform domain-based sparse OFDM channel estimation ﻿

Sparse coding with enhanced atom selection for FDD massive MIMO channel estimation ﻿

Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM ﻿

Using OMP and SD algorithms together in mm-Wave mMIMO channel estimation ﻿

Blind signal analysis

Channel modeling for 5G and beyond

Deep learning-assisted detection of PUE and jamming attacks in cognitive radio systems

Deep learning-based optimal ris interaction exploiting previously sampled channel correlations

Deep RL-based spectrum occupancy prediction exploiting time and frequency correlations

Developing novel spectrum occupancy prediction and physical layer security techniques

Dictionary learning-based beamspace channel estimation in millimeter-wave massive mimo systems with a lens antenna array

Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models

Estimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation

Identification of distorted RF components via deep multi-task learning

Joint estimation of multiple RF impairments using deep multi-task learning

Primary user emulation and jamming attack detection in cognitive radio via sparse coding

Signal relation-based physical layer authentication

Sparse coding for transform domain-based sparse OFDM channel estimation

Sparse coding with enhanced atom selection for FDD massive MIMO channel estimation

Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM

Using OMP and SD algorithms together in mm-Wave mMIMO channel estimation