Yazar "Nazzal, Mahmoud" seçeneğine göre listele

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    Channel modeling for 5G and beyond
    (Institution of Engineering and Technology, 2020) Nazzal, Mahmoud; Aygül, Mehmet Ali; Arslan, Hüseyin
    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 models. In this chapter, we revise the key requirements of efficient channel modeling for 5G and beyond, highlight the outstanding and expected challenges, and present the main efforts made in this domain by leading industrial and research entities. We review channel modeling through machine learning (ML) as a promising channel modeling approach and revise the compressed sensing (CS)-based channel modeling and estimation framework.
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    Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification
    (Institute of Electrical and Electronics Engineers Inc., 2019) Nazzal, Mahmoud; Hasekio?lu, Orkun; Ekti, Ali Rıza; Görçin, Ali; Arslan, Hüseyin
    Despite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper proposes an algorithm for sub-Nyquist wide-band spectrum sensing addressing this shortcoming. The proposed algorithm divides the spectrum into narrow, contagious frequency subbands and learns a subband dictionary for each subband. A subband dictionary is well-suited for the representation of signals in its corresponding subband. A compressed version of the received signal is sparsely coded over each subband dictionary. We show that the convergence patterns over a specific dictionary can be used for identifying the occupancy of its underlying subband. Therefore, the convergence patterns obtained by the gradient operator are used as distinctive classifying features. Then, a machine learning-based classifier is trained over these features and used to make the decision about spectrum occupancy. As the interest is only to characterize sparse coding convergence patterns, we alleviate the need for a specific or an estimated sparsity level. Besides, using subband dictionaries at different frequencies omits the need for a frequency-splitting filterbank. The proposed algorithm achieves significant performance improvements in terms of the probability-of-detection and false-alarm-rate measures. This result is validated through simulations with various operating scenarios.
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    Deep learning-assisted detection of PUE and jamming attacks in cognitive radio systems
    (Institute of Electrical and Electronics Engineers Inc., 2020) Aygül, Mehmet Ali; Furqan, Haji Muhammad; Nazzal, Mahmoud; Arslan, Hüseyin
    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 (PUE) and jamming attacks. Machine learning has been recently applied to the detection of these attacks. Still, the need for feature extraction required by machine learning techniques restrains the full exploitation of raw data. To alleviate this need, this paper proposes one-dimensional deep learning as a framework for identifying such attacks. Simulations show the ability of the proposed algorithm to detect these attacks with high performance.
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    Deep learning-based optimal ris interaction exploiting previously sampled channel correlations
    (IEEE - Institute of Electrical and Electronics Engineers, Inc, 2021) Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin
    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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    Deep RL-based spectrum occupancy prediction exploiting time and frequency correlations
    (Institute of Electrical and Electronics Engineers Inc., 2022) Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin
    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 users. The usage of the spectrum bands is highly correlated over both time and frequency. Recently, machine learning algorithms are used to predict spectrum occupancy by exploiting such correlations. However, this approach primarily assumes a supervised learning setting. Despite its outstanding performance, this setting requires the availability of sufficiently large datasets (of labeled data) and is not adaptive to environment changes. In this paper, different from the existing literature, a deep reinforcement learning (RL) algorithm is used to alleviate those shortcomings. In this algorithm, we define the reward functions of the deep RL setting and its state and action spaces such that it is applicable to work dynamically, in an online fashion, in real world settings. Extensive experiments validate the capability of the proposed algorithm in predicting spectrum occupancies as examined over real world spectrum measurements. These are carried out in the 832-862 megahertz frequency bands, which are used by the leading Turkish telecom providers as private uplink bands. This is a significant step towards realizing a standalone spectrum occupancy prediction operation without any control from the operator and minimizing memory requirements while alleviating the need for the labeled dataset.
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    Dictionary learning-based beamspace channel estimation in millimeter-wave massive mimo systems with a lens antenna array
    (Institute of Electrical and Electronics Engineers Inc., 2019) Nazzal, Mahmoud; Aygül, Mehmet Ali; Görçin, Ali; Arslan, Hüseyin
    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 estimation in the beamspace. Along this line, beamspace sparsity is an efficient regularizer to this problem. In this paper, we propose using a dictionary trained over a set of example beam selection matrices, as a beam selection tool. In this context, a learned dictionary can more effectively guarantee the sparsity of the representation at the specified sparsity level, owing to the dictionary learning process. This means that it gives a better sparse representation, and, consequently, a better channel estimation quality. Simulations validate that using a trained dictionary improves the quality of channel estimation, as tested over two channel models with different operating scenarios.
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    Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models
    (MDPI, 2021) Aygül, Mehmet Ali; Nazzal, Mahmoud; Sağlam, Mehmet İzzet; da Costa, Daniel Benevides; Ateş, Hasan Fehmi; Arslan, Hüseyin
    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. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions, which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.
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    Estimating multi-dimensional sparsity level for spectrum sensing
    (Institute of Electrical and Electronics Engineers Inc., 2023) Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin
    Identifying spectrum opportunities is a crucial element of efficient spectrum utilization for future wireless networks. Spectrum sensing offers a convenient means for revealing such opportunities. Studies showed that usage of the spectrum has a high correlation over multi-dimensions, including time and frequency. However, multi-dimensional spectrum sensing requires high-cost processes. Applying compressive sensing allows for subNyquist sampling. This reduces associated training, feedback, and computation overheads of a spectrum sensing method. However, the accuracy of the signal sparsity assumption and knowledge of the precise sparsity level are necessary for the applicability of compressive sensing. It is common practice to assume a level of known sparsity. On the other hand, in reality, this presumption is incorrect. This paper proposes a method for estimating the multidimensional sparsity for spectrum sensing. By extrapolating it from its counterpart with respect to a compact discrete Fourier basis, the proposed method calculates the sparsity level over a dictionary. A machine learning estimation method achieves this inference. Extensive simulations validate a high-quality sparsity estimation. To validate this observation, real-world measurements are used, where one of the biggest Turkish telecom operators has private uplink bands in the frequency range between 852-856 MHz.
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    Estimation and exploitation of multidimensional sparsity for MIMO-OFDM channel estimation
    (Institute of Electrical and Electronics Engineers Inc., 2022) Nazzal, Mahmoud; Aygül, Mehmet Ali; Arslan, Hüseyin
    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, the exploitation of channel sparsity has led to sub-Nyquist channel sampling thereby reducing the channel training overhead. Still, there is a growing belief in channel sparsity appearance in many dimensions; time, frequency, angle, and space. Accordingly, this paper proposes an algorithm for channel estimation where sparsity in multidimensions is simultaneously exploited. Also, the applicability of sparse coding relies on the validity of a signal sparsity assumption and knowing the exact sparsity level. However, 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 also propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries. Simulations validate a substantial channel estimation quality attained by the proposed algorithm as compared to the existing algorithms. The simulations also validate a high quality of sparsity estimation leading to performances close to the impractical case of assuming known sparsity.
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    Exploiting sparsity recovery for compressive spectrum sensing: A machine learning approach
    (Institute of Electrical and Electronics Engineers Inc., 2019) Nazzal, Mahmoud; Ekti, Ali Rıza; Görçin, Ali; Arslan, Hüseyin
    Sub-Nyquist sampling for spectrum sensing has the advantages of reducing the sampling and computational complexity burdens. However, determining the sparsity of the underlying spectrum is still a challenging issue for this approach. Along this line, this paper proposes an algorithm for narrowband spectrum sensing based on tracking the convergence patterns in sparse coding of compressed received signals. First, a compressed version of a received signal at the location of interest is obtained according to the principle of compressive sensing. Then, the signal is reconstructed via sparse recovery over a learned dictionary. While performing sparse recovery, we calculate the sparse coding convergence rate in terms of the decay rate of the energy of residual vectors. Such a decay rate is conveniently quantified in terms of the gradient operator. This means that while compressive sensing allows for sub-Nyquist sampling thereby reducing the analog-to-digital conversion overhead, the sparse recovery process could be effectively exploited to reveal spectrum occupancy. Furthermore, as an extension to this approach, we consider feeding the energy decay gradient vectors as features for a machine learning-based classification process. This classification further enhances the performance of the proposed algorithm. The proposed algorithm is shown to have excellent performances in terms of the probability-of-detection and false-alarm-rate measures. This result is validated through numerical experiments conducted over synthetic data as well as real-life measurements of received signals. Moreover, we show that the proposed algorithm has a tractable computational complexity, allowing for real-time operation.
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    FDD massive MIMO downlink channel estimation via selective sparse coding over AOA/AOD cluster dictionaries
    (Institute of Electrical and Electronics Engineers Inc., 2018) Nazzal, Mahmoud; Furqan, Haji Muhammad; Arslan, Hüseyin
    Sparse coding over a redundant dictionary has recently been used as a framework for downlink channel estimation in frequency division duplex massive multiple-input multiple-output antenna systems. This usage allows for efficiently reducing the inherently high training and feedback overheads. We present an algorithm for downlink channel estimation via selective sparse coding over multiple cluster dictionaries. A channel training set is divided into clusters based on the angle of the arrival/departure of the majority physical subpaths corresponding to each channel tap. Then, a compact dictionary is trained in each cluster. Channel estimation is done by first identifying the channel cluster and then using its dictionary for reconstruction. This selective sparse coding allows for adaptive regularization via sparse model selection, thereby offering additional regularization to the ill-posed channel estimation problem. We empirically validate the selectivity of the cluster dictionaries. Simulation results show the advantage of the proposed algorithm in achieving better estimation quality at lower computational cost, as compared the case of using standard sparse coding.
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    Iterative tap pursuit for channel shortening equalizer design
    (Institute of Electrical and Electronics Engineers Inc., 2018) Furqan, Haji Muhammad; Nazzal, Mahmoud; Arslan, Hüseyin
    In this work, an iterative tap pursuit algorithm for designing channel shortening equalizers is proposed. Similar to pursuit algorithms, a residual vector is initialized with a desired target impulse response, which is iteratively approximated by one-tap sub-filters. In each iteration, the algorithm selects the location and weight of a one-tap sub-filter. This is proceeded by updating the residual vector by subtracting its already-represented portions by selected sub-filters. The advantage of this algorithm lies in its simplicity in alleviating the need for performing an exhaustive search thus reducing the computational complexity. Convergence of the proposed algorithm is guaranteed by the fact that the energy of the residual decreases with iteration. We show that the proposed algorithm has a significantly reduced computational complexity. Experiments conducted on Rayleigh fading wireless channels validate the effectiveness of the proposed algorithm in designing channel shortening filters in terms of the shortening signal-to-noise ratio measure and complexity.
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    Primary user emulation and jamming attack detection in cognitive radio via sparse coding
    (Springer, 2020) Furqan, Haji Muhammad; Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin
    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 the spectrum utilization. In this paper, an algorithm for the detection of primary user emulation and jamming attacks in cognitive radio is proposed. The proposed algorithm is based on the sparse coding of the compressed received signal over a channel-dependent dictionary. More specifically, the convergence patterns in sparse coding according to such a dictionary are used to distinguish between a spectrum hole, a legitimate primary user, and an emulator or a jammer. The process of decision-making is carried out as a machine learning-based classification operation. Extensive numerical experiments show the effectiveness of the proposed algorithm in detecting the aforementioned attacks with high success rates. This is validated in terms of the confusion matrix quality metric. Besides, the proposed algorithm is shown to be superior to energy detection-based machine learning techniques in terms of receiver operating characteristics curves and the areas under these curves.
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    Sparse coding with enhanced atom selection for FDD massive MIMO channel estimation
    (Institute of Electrical and Electronics Engineers Inc., 2021) Nazzal, Mahmoud; Aygül, Mehmet Ali; Arslan, Hüseyin
    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 of minimizing the channel estimation error. This calls for better ways of atom selection. Accordingly, we propose an algorithm for improved atom selection in sparse coding for frequency division duplex (FDD) massive multiple-input-multiple-output (MIMO) downlink channel estimation. The proposed algorithm performs iterative atom selection based on two residuals. First is the received signal residual used to guide on a small selection pool of candidate atoms. Second is the residual of an initial channel estimate which is used to pick the best atom within the selection pool. Simulation results show the advantage of the proposed algorithm over standard sparse coding-based channel estimation. Moreover, the proposed algorithm eliminates the need for cell-specific trained dictionaries without sacrificing the performance. Furthermore, the proposed sparse coding can be applied in the process of dictionary learning to train for improved dictionaries achieving further performance enhancement.
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    Sparsifying dictionary learning for beamspace channel representation and estimation in millimeter-wave massive MIMO
    (Institute of Electrical and Electronics Engineers Inc., 2023) Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin
    Millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in 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 optimal for many reasons. For example, it can cause a power leakage problem. Accordingly, this paper proposes using a learned sparsifying dictionary as the transformation operator leading to another beamspace for channel representation. Since a dictionary is obtained by training over actual channel measurements in an end-to-end manner, 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, thus reducing the underlying power leakage phenomenon. Consequently, this allows for improved channel estimation and facilitates beam selection in mmWave mMIMO. In addition, a learned dictionary is used as the channel estimation 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 beam selectivity, thus improving spectral efficiency.
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    Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM
    (Institute of Electrical and Electronics Engineers Inc., 2020) Aygül, Mehmet Ali; Nazzal, Mahmoud; Ekti, Ali Rıza; Görçin, Ali; da Costa, Daniel Benevides; Ateş, Hasan Fehmi; Arslan, Hüseyin
    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 the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.
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    Using OMP and SD algorithms together in mm-Wave mMIMO channel estimation
    (Springer London Ltd, 2022) Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin
    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 of the beam selection paradigm. Recently, compressive sensing-based algorithms are used to utilize existing sparsity for CE in these systems. Among them, orthogonal matching pursuit (OMP) and support detection (SD) are the most popular ones. These two popular algorithms have their own advantages and disadvantages. In this paper, we propose to use OMP and SD together for better CE. Simulations validate that the proposed algorithm enhances the CE quality over the conventional algorithms. These simulations are tested over two popularly used channel models.