arXiv subjects

Huseyin Arslan

Publications and source records attributed to Huseyin Arslan.

At least 73 records · Page 4Linked to original sources

arXiv2022

Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO

Millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in the fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO channel into a beamspace where the channel exhibits sparsity. This beamspace transformation is equivalent to performing a Fourier transformation of the channel. Still, a Fourier transformation is not necessarily the optimal one, due to many reasons. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace. Since the dictionary is obtained by training over actual channel measurements, this transformation is shown to yield two immediate advantages. First is enhancing channel sparsity, thereby leading to more efficient pilot reduction. Second is improving the channel representation quality, and thus reducing the underlying power leakage phenomenon. Consequently, this allows for both improved channel estimation and facilitated beam selection in mmWave mMIMO. Besides, a learned dictionary is also used as the precoding operator for the same reasons. Extensive simulations under various operating scenarios and environments validate the added benefits of using learned dictionaries in improving the channel estimation quality and the beam selectivity, thereby improving the spectral efficiency.

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arXiv2022

Inter-Numerology Interference Pre-Equalization for 5G Mixed-Numerology Communications

This article proposes a pre-equalization method to remove inter-numerology interference (INI) that occurs in multi-numerology OFDM frame structures of fifth-generation New Radio (5G-NR) and beyond on the transmitter side. In the literature, guard bands, filters and interference cancellation methods are used to reduce the INI. In this work, we mathematically model how the INI is generated and how it can be removed completely for multi-numerology systems on the transmitter side.

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arXiv2022

On the Performance of Handover Mechanisms for Non-Terrestrial Networks

Next-generation wireless networks require massive connectivity and ubiquitous coverage, for which non-terrestrial networks (NTNs) are a promising enabler. However, NTNs, especially non-geostationary satellites bring about challenges such as increased handovers (HOs) due to the moving coverage area of the satellite on the ground. Accordingly, in this work, we compare the conventional measurement-based HO triggering mechanism with other alternatives such as distance, elevation angle, and timer-based methods in terms of the numbers of HOs, ping-pong HOs, and radio link failures. The system-level simulations, carried out in accordance with the 3GPP model, show that the measurement-based approach can outperform the other alternatives provided that appropriate values of hysteresis/offset margins and time-to-trigger parameters are used. Moreover, future directions regarding this work are also provided at the end.

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arXiv2021

IRS-Enabled Beam-Space Channel

The intelligent reflecting surface (IRS) is emphasized as a controlled scattering cluster. To this end, scatterers and traveling paths of multipath components are classified to build a new channel model. Unlike the conventional modeling, where the channels between system units are modeled independently, the new model considers the channel as a whole and decomposes it based on the traveling paths. The model shows clearly how IRS, in the beam-space context, converts the channel from a problem into a design element. After investigating IRS as a scattering cluster, based on a proposed segmentation scheme, the beamforming problem is considered with a focus on first-order reflections. Passive beamforming at IRS is shown to have two tiers; at the scatterer and antenna levels. A segment-activation scheme is proposed to maximize the received signal power, where the number of transmitting antenna elements to be used is given as a function of IRS positioning and beamforming at the receiver. The results show that while using more transmitting antenna elements to get narrower beams is possible, using fewer elements can give better performance, especially for larger IRS at close distances. The developed model also proves useful in addressing emerging issues in massive MIMO communication, namely, stationarity and spherical wavefronts.

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arXiv2021

Survey on Symbiotic Radio: A Paradigm Shift in Spectrum Sharing and Coexistence

Sixth-generation (6G) of mobile communication aims to connect this world digitally through green communication networks that provide secure, ubiquitous, and unlimited connectivity in an attempt to improve the overall quality of life.The driving force behind the development of these networks is the rapid evolution of Internet-of-Things (IoT), which has stimulated the proliferation of wireless applications in health,education, agriculture, utilities, etc. However, these applications are accompanied by the deployment of a massive number of IoT devices that require a significant radio spectrum for wireless connectivity. IoT devices usually have low data rate requirements and limited power provision but desirably a long life.Recently, the development of passive radio systems has opened new paradigms of spectrum sharing and coexistence. These systems utilize the radio resources and infrastructure of the active radio systems to perform their functionalities. By enabling the dependent coexistence, a new technology named symbiotic radio (SRad) enables the symbiotic relationships between the different radio systems ranging from mutual benefits or competition in terms of sharing the resources, in particular for IoT devices. This survey first provides the motivation for dependent coexistence and background of spectrum sharing through coexistence along with existing literature. Then, it describes the active and passive radio systems definition and a brief overview. Afterward, the history of symbiosis and the role of SRad technology in spectrum sharing and coexistence are defined while focusing on symbiotic communication. Lastly, we discuss research challenges, future directions, and applications scenarios.

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arXiv2021

Reconfigurable intelligent surface (RIS): Eigenvalue Decomposition-Based Separate Channel Estimation

Reconfigurable intelligent surface (RIS) has recently drawn significant attention in wireless communication technologies. However, identifying, modeling, and estimating the RIS channel in multiple-input multiple-output (MIMO) systems are considered challenging in recent studies. In this paper, a disassembled channel estimation framework for the RIS-MIMO system is proposed based on the eigenvalue decomposition (EVD) concept to separate the cascaded channel links and estimate each link separately. This estimation is based on modeling the RIS-MIMO channel as a keyhole MIMO system model. Numerical results show that the proposed estimation method has a low estimation time overhead while providing less estimation error.

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arXiv2021

Flexible Physical Layer Security for Joint Data and Pilots in Future Wireless Networks

In this work, novel physical layer security (PLS) schemes are proposed for orthogonal frequency-division multiplexing (OFDM) to secure both data and pilots. The majority of previous studies focus on only securing the data without considering the security of the pilots used for channel estimation. However, the leakage of channel state information (CSI) from a legitimate node to an eavesdropper allows the latter to acquire knowledge about the channel of the legitimate nodes. To this end, we propose adaptive and flexible PLS algorithms which can 1) secure data, 2) secure pilots, and 3) jointly secure both data and pilots. Particularly, minimum-phase all-pass channel decomposition is exploited, where the proposed algorithms use the all-pass component to provide security without harming the performance of the legitimate user. In the analysis for data security, we evaluated the secrecy under correlated and uncorrelated eavesdropping channels via closed-form bit error rate (BER) formulas. For pilot security, we analyzed the normalized mean squared error (NMSE) performance of the estimated channel. The simulation results along with theoretical analysis demonstrate that the proposed algorithms can effectively enhance the communication secrecy of the overall system.

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arXiv2021

Subcarrier Number and Indices-Based KeyGeneration for Future Wireless Networks

Physical layer key generation from the wireless channel is an emerging area of interest to provide confidentiality and authentication. One of the main challenges in this domain is to increase the length of the secret key while maintaining its randomness and uniformity. In this work, new dimensions for wireless channel-based key generation are proposed for orthogonal frequency division multiplexing (OFDM) systems. The novel perspective of the proposed work lies in the generation of key bits not only from the magnitudes of OFDM subchannels as it has conventionally been done but also from the number and positions of those subchannels whose channel gains are above the mean of the respective subblock. The effectiveness of the proposed algorithms is evaluated in terms of key generation rate and key mismatch rate. Additionally, a statistical test suite offered by the National Institute of Standards and Technology is used to evaluate the randomness of the generated key bits. It is shown in the simulation results that the involvement of the proposed dimensions can double the key generation rate compared to conventional algorithms.

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arXiv2021

Joint Estimation of Multiple RF Impairments Using Deep Multi-Task Learning

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, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments.

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arXiv2021

Enhancing Channel Shortening Based Physical Layer Security Using Coordinated Multipoint

Wireless networks have become imperative in all areas of human life. As such, one of the most critical concerns in next-generation networks is ensuring the security and privacy of user data/communication. Cryptography has been conventionally used to tackle this, but it may not be scalable (in terms of key exchange and management) with the increasingly heterogeneous network deployments. Physical layer security (PLS) provides a promising alternative, but struggles when an attacker boasts a better wireless channel as compared to the legitimate user. This work leverages the coordinated multipoint concept and its distributed transmission points, in conjunction with channel shortening, to address this problem. Results show significant degradation of the bit-error-rate experienced at the eavesdropper as compared to state-of-the-art channel shortening-based PLS methods.

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arXiv2021

Energy-Efficient Resource Allocation for 5G Cognitive Radio NOMA Using Game Theory

Cognitive radio non-orthogonal multiple access (CR-NOMA) networks promise improved spectrum utilization and capacity in 5G networks. In this work, we aim to investigate efficient power allocation for the secondary users (SUs) in underlay CR-NOMA networks using a game-theoretic approach. We present a novel power allocation to CR-NOMA network from a game-theoretic perspective. First, we specify the utility function of the primary users (PUs) and SUs, and formulate the game as a non-cooperative game. Then, the existence and uniqueness of the Nash equilibrium (NE) are investigated. Finally, the sum utilities of SUs is maximized by optimal power allocation at the NE point. Simulation results provided that the proposed scheme outperforms the conventional method, providing up to 37.5\% increase in sum utilities of the SUs.

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arXiv2020

Estimating Sparsity Level for Enabling Compressive Sensing of Wireless Channels and Spectra in 5G and Beyond

Applying compressive sensing (CS) allows for sub-Nyquist sampling in several application areas in 5G and beyond. This reduces the associated training, feedback, and computation overheads in many applications. However, the applicability of CS relies on the validity of a signal sparsity assumption and knowing the exact sparsity level. It is customary to assume a foreknown sparsity level. Still, this assumption is not valid in practice, especially when applying learned dictionaries as sparsifying transforms. The problem is more strongly pronounced with multidimensional sparsity. In this paper, we propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries. The proposed algorithm estimates the sparsity level over a dictionary by inferring it from its counterpart with respect to a compact discrete Fourier basis. This inference is achieved by a machine learning prediction. This setting learns the intrinsic relationship between the columns of a dictionary and those of such a fixed basis. The proposed algorithm is applied to estimating sparsity levels in wireless channels, and in cognitive radio spectra. Extensive simulations validate a high quality of sparsity estimation leading to performances very close to the impractical case of assuming known sparsity.

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arXiv2020

Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations

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 practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multi-layer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm.

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arXiv2020

Identification of The Number of Wireless Channel Taps Using Deep Neural Networks

In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS).

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arXiv2020

Blind Numerology Identification for Mixed Numerologies

5G New Radio (NR) introduces new flexibility that different numerologies can be selected to meet the requirements of a wide variety of services. For this new structure, blind numerology identification can increase system efficiency. Therefore, we propose a blind identification method for mixed numerologies. An autocorrelation method is applied in the time domain by correlating the cyclic prefix (CP) signal of the candidate numerology in the received composite signal for numerology type identification. Then, the location of each numerology in the frequency domain is identified by the variance difference in the power spectral density (PSD) of the subbands, on which different numerologies are occupied. The simulation results are obtained under additive white Gaussian noise (AWGN) and frequencyselective channels. The obtained results show that the proposed method has a robust identification accuracy and a satisfactory BER performance as compared to the non-blind identification approach in the conventional mixed-numerology system.

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arXiv2020

Multidimensional Index Modulation for 5G and Beyond Wireless Networks

This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits information bits through antenna indices, application of IM to orthogonal frequency division multiplexing (OFDM) subcarriers has opened the door for the extension of IM into different dimensions, such as radio frequency (RF) mirrors, time slots, codes, and dispersion matrices. Recent studies have introduced the concept of multidimensional IM by various combinations of one-dimensional IM techniques to provide higher spectral efficiency (SE) and better bit error rate (BER) performance at the expense of higher transmitter (Tx) and receiver (Rx) complexity. Despite the ongoing research on the design of new IM techniques and their implementation challenges, proper use of the available IM techniques to address different requirements of 5G and beyond networks is an open research area in the literature. For this reason, we first provide the dimensional-based categorization of available IM domains and review the existing IM types regarding this categorization. Then, we develop a framework that investigates the efficient utilization of these techniques and establishes a link between the IM schemes and 5G services, namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communication (URLLC). Additionally, this work defines key performance indicators (KPIs) to quantify the advantages and disadvantages of IM techniques in time, frequency, space, and code dimensions. Finally, future recommendations are given regarding the design of flexible IM-based communication systems for 5G and beyond wireless networks.

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arXiv2020

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

With the emergence of the internet of things (IoT) technology, wireless connectivity should be more ubiquitous than ever. In fact, the availability of wireless connection everywhere comes with security threats that, unfortunately, cannot be handled by conventional cryptographic solutions alone, especially in heterogeneous and decentralized future wireless networks. In general, physical layer security (PLS) helps in bridging this gap by taking advantage of the fading propagation channel. Moreover, the adoption of reconfigurable intelligent surfaces (RIS) in wireless networks makes the PLS techniques more efficient by involving the channel into the design loop. In this paper, we conduct a comprehensive literature review on the RIS-assisted PLS for future wireless communications. We start by introducing the basic concepts of RISs and their different applications in wireless communication networks and the most common PLS performance metrics. Then, we focus on the review and classification of RIS-assisted PLS applications, exhibiting multiple scenarios, system models, objectives, and methodologies. In fact, most of the works in this field formulate an optimization problem to maximize the secrecy rate (SR) or secrecy capacity (SC) at a legitimate user by jointly optimizing the beamformer at the transmitter and the RIS's coefficients, while the differences are in the adopted methodology to optimally/sub-optimally approach the solution. We finalize this survey by presenting some insightful recommendations and suggesting open problems for future research extensions.

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arXiv2020

A General Framework for RIS-Aided mmWave Communication Networks: Channel Estimation and Mobile User Tracking

Reconfigurable intelligent surface (RIS) has been widely discussed as new technology to improve wireless communication performance. Based on the unique design of RIS, its elements can reflect, refract, absorb, or focus the incoming waves toward any desired direction. These functionalities turned out to be a major solution to overcome millimeter-wave (mmWave)'s high propagation conditions including path attenuation and blockage. However, channel estimation in RIS-aided communication is still a major concern due to the passive nature of RIS elements, and estimation overhead that arises with multiple-input multiple-output (MIMO) system. As a consequence, user tracking has not been analyzed yet. This paper is the first work that addresses channel estimation, beamforming, and user tracking under practical mmWave RIS-MIMO systems. By providing the mathematical relation of RIS design with a MIMO system, a three-stage framework is presented. Starting with estimating the channel between a base station (BS) and RIS using hierarchical beam searching, followed by estimating the channel between RIS and user using an iterative resolution algorithm. Lastly, a popular tracking algorithm is employed to track channel parameters between the RIS and the user. System analysis demonstrates the robustness and the effectiveness of the proposed framework in real-time scenarios.

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