Yazar "Mishra, Kumar Vijay" seçeneğine göre listele

Listeleniyor 1 - 17 / 17
  • Küçük Resim
    Öğe
    CNN-Based Cognitive Radar Array Selection
    (Ieee, 2019) Elbir, Ahmet M.; Mishra, Kumar Vijay; Eldar, Yonina C.
    In cognitive radar, it may be desired to select an optimal subarray from a full antenna array in each scan to reduce the cost and computational complexity. Previous works on antenna selection rely on mostly optimization or greedy search methods. In this paper, we introduce a deep learning approach for antenna selection in a cognitive radar scenario. We design a deep convolutional neural network (CNN) to select the best subarray for direction-of-arrival estimation for each scan. The CNN accepts the array covariance matrix as its input and, unlike previous works, does not require prior knowledge about the target location. The performance of the proposed CNN approach is evaluated through numerical simulations. In particular, we show that it provides more accurate results than conventional support vector machines.
  • Küçük Resim Yok
    Öğe
    CNN-based cognitive radar array selection
    (Institute of Electrical and Electronics Engineers Inc., 2019) Elbir, Ahmet Musab; Mishra, Kumar Vijay; Eldar, Yonina
    In cognitive radar, it may be desired to select an optimal subarray from a full antenna array in each scan to reduce the cost and computational complexity. Previous works on antenna selection rely on mostly optimization or greedy search methods. In this paper, we introduce a deep learning approach for antenna selection in a cognitive radar scenario. We design a deep convolutional neural network (CNN) to select the best subarray for direction-of-arrival estimation for each scan. The CNN accepts the array covariance matrix as its input and, unlike previous works, does not require prior knowledge about the target location. The performance of the proposed CNN approach is evaluated through numerical simulations. In particular, we show that it provides more accurate results than conventional support vector machines. © 2019 IEEE.
  • Küçük Resim
    Öğe
    Cognitive Learning-Aided Multi-Antenna Communications
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Elbir, Ahmet M.; Mishra, Kumar Vijay
    Cognitive communications have emerged as a promising solution to enhance, adapt, and invent new tools and capabilities that transcend conventional wireless networks. Deep learning (DL) is critical in enabling essential features of cognitive systems because of its fast prediction performance, adaptive behavior, and model-free structure. These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. Multiple antennas may provide multiplexing, diversity, or antenna gains that improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio, respectively. In practice, multi-antenna cognitive communications encounter challenges in terms of data complexity and diversity, hardware complexity, and wireless channel dynamics. DL solutions such as federated learning, transfer learning, and online learning tackle these problems at various stages of communications processing, including multi-channel estimation, hybrid beamforming, user localization, and sparse array design. This article provides a synopsis of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications for improved robustness and adaptation to the environmental changes while providing satisfactory spectral efficiency and computation times. We discuss DL design challenges from the perspective of data, learning and transceiver architectures. In particular, we suggest quantized learning models, data/model parallelization, and distributed learning methods to address the aforementioned challenges.
  • Küçük Resim
    Öğe
    Cognitive radar antenna selection via deep learning
    (Inst Engineering Technology-Iet, 2019) Elbir, Ahmet Musab; Mishra, Kumar Vijay; Eldar, Yonina
    Direction-of-arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is a recent interest in thinning the antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the antenna selection problem. Specifically, they construct a convolutional neural network (CNN) as a multi-class classification framework, where each class designates a different subarray. The proposed network determines a new array every time data is received by the radar, thereby making antenna selection a cognitive operation. Their numerical experiments show that the proposed CNN structure provides 22% better classification performance than a support vector machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.
  • Küçük Resim
    Öğe
    Deep learning design for joint antenna selection and hybrid beamforming in massive MIMO
    (Institute of Electrical and Electronics Engineers Inc., 2019) Elbir, Ahmet Musab; Mishra, Kumar Vijay
    In this paper, we propose a deep-learning-based for joint antenna selection and hybrid beamformer design problem in mmWave massive MIMO systems. In this respect, we treat both problems as a classification problem. We design two convolutional neural networks (CNNs) which accept the input as the channel matrix and it yields the output as the optimum antenna subarray. The selected part of channel matrix is fed to the second CNN which gives the output as the analog and baseband beamformers. We evaluate the performance of the proposed approach through numerical simulations and show that our CNN framework provides significantly better performance as compared to the conventional techniques such as orthogonal matching pursuit. © 2019 IEEE.
  • Küçük Resim
    Öğe
    Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO
    (Ieee, 2019) Elbir, Ahmet M.; Mishra, Kumar Vijay
    In this paper, we propose a deep-learning-based for joint antenna selection and hybrid beamformer design problem in mmWave massive MIMO systems. In this respect, we treat both problems as a classification problem. We design two convolutional neural networks (CNNs) which accept the input as the channel matrix and it yields the output as the optimum antenna subarray. The selected part of channel matrix is fed to the second CNN which gives the output as the analog and baseband beamformers. We evaluate the performance of the proposed approach through numerical simulations and show that our CNN framework provides significantly better performance as compared to the conventional techniques such as orthogonal matching pursuit.
  • Küçük Resim
    Öğe
    A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Elbir, Ahmet M.; Mishra, Kumar Vijay; Shankar, M. R. Bhavani; Ottersten, Bjorn
    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. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.
  • Küçük Resim
    Öğe
    FEDERATED CHANNEL LEARNING FOR INTELLIGENT REFLECTING SURFACES WITH FEWER PILOT SIGNALS
    (Ieee, 2022) Elbir, Ahmet M.; Cöleri, Sinem; Mishra, Kumar Vijay
    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. To deal with these uncertainties, deep learning (DL) approaches have been proposed. Previous works consider centralized learning (CL) approach for model training, which entails the collection of the whole training dataset from the users at the base station (BS), hence introducing huge transmission overhead for data collection. To address this challenge, this paper proposes a federated learning (FL) framework to jointly estimate both direct and cascaded channels in IRSassisted wireless systems. We design a single convolutional neural network trained on the local datasets of the users without sending them to the BS. We show that the proposed FL-based channel estimation approach requires approximately 60% fewer pilot signals and it exhibits 12 times lower transmission overhead than CL, while maintaining satisfactory performance close to CL. In addition, it provides lower estimation error than the state-of-the-art DL-based schemes.
  • Küçük Resim
    Öğe
    Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Elbir, Ahmet M.; Mishra, Kumar Vijay
    In millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems employ hybrid beamformers to reduce power consumption associated with fully digital beamforming in large arrays. Further savings in cost and power are possible through the use of subarrays. Unlike prior works that resort to large latency methods such as optimization and greedy search for subarray selection, we propose a deep-learning-based approach in order to overcome the complexity issue without causing significant performance loss. We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). For antenna selection, the CNN accepts the channel matrix as input and outputs a subarray with optimal spectral efficiency. The resultant subarray channel matrix is then again fed to a CNN to obtain analog and baseband beamformers. We train the CNNs with several noisy channel matrices that have different channel statistics in order to achieve a robust performance at the network output. Numerical experiments show that our CNN framework provides an order better spectral efficiency and is 10 times faster than the conventional techniques. Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices.
  • Küçük Resim
    Öğe
    Low-Complexity Limited-Feedback Deep Hybrid Beamforming for Broadband Massive MIMO
    (Ieee, 2020) Elbir, Ahmet M.; Mishra, Kumar Vijay
    The broadband millimeter-wave (mm-Wave) systems use hybrid beamformers with common analog beamformer for the entire band while employing different baseband beamformers in different frequency sub-bands. Furthermore, the performance mostly relies on the perfectness of the channel information. In this paper, we propose a deep learning (DL) framework for hybrid beamformer design in broadband mmWave massive MIMO systems. We design a convolutional neural network (CNN) that accepts the channel matrix of all subcarriers as input and the output of CNN is the hybrid beamformers. The proposed CNN architecture is trained with imperfect channel matrices in order to provide robust performance against the deviations in the channel data. Hence, the proposed precoding scheme can handle the imperfect or limited feedback scenario where the full and exact knowledge of the channel is not available. We show that the proposed DL framework is more robust and computationally less complex than the conventional optimization and phase-extraction-based approaches.
  • Küçük Resim Yok
    Öğe
    The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Elbir, Ahmet M.; Mishra, Kumar Vijay; Shankar, M. R. Bhavani; Chatzinotas, Symeon
    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. Recently, integrated sensing and communications (ISAC) has been widely studied as a means to efficiently utilize spectrum and thereby save cost and power. This article investigates the role of IRS in the future ISAC paradigms. While there is a rich heritage of recent research into IRS-assisted communications, the IRS- assisted radars and ISAC remain relatively unexamined. We discuss the putative advantages of IRS deployment, such as coverage extension, interference suppression, and enhanced parameter estimation, for both communications and radar. We introduce possible IRS- assisted ISAC scenarios with common and dedicated surfaces. The article provides an overview of related signal processing techniques and the design challenges, such as wireless channel acquisition, waveform design, and security.
  • Küçük Resim
    Öğe
    Robust Hybrid Beamforming with Quantized Deep Neural Networks
    (IEEE Computer Society, 2019) Elbir, Ahmet Musab; Mishra, Kumar Vijay
    Hybrid beamforming is integral to massive multiple-input multiple-output (MIMO) communications in reducing the training overhead and hardware cost associated with large antenna arrays. Prior works have employed optimization and greedy search to jointly estimate the precoder and combiner weights. High computational complexity of these methods apart, their performance strongly relies on accurate channel information. In this paper, we propose a computationally efficient, deep learning approach that also provides robust performance against the deviations in the channel characteristics. Further, we employ a convolutional neural network with quantized weights (Q-CNN) so that it is deployable in mobile devices that have less memory resources and low overhead requirements. We show that the proposed Q-CNN, saved in at least 6 bits, yields superior performance over conventional massive MIMO hybrid beamforming. © 2019 IEEE.
  • Küçük Resim
    Öğe
    ROBUST HYBRID BEAMFORMING WITH QUANTIZED DEEP NEURAL NETWORKS
    (Ieee, 2019) Elbir, Ahmet M.; Mishra, Kumar Vijay
    Hybrid beamforming is integral to massive multiple-input multiple-output (MIMO) communications in reducing the training overhead and hardware cost associated with large antenna arrays. Prior works have employed optimization and greedy search to jointly estimate the precoder and combiner weights. High computational complexity of these methods apart, their performance strongly relies on accurate channel information. In this paper, we propose a computationally efficient, deep learning approach that also provides robust performance against the deviations in the channel characteristics. Further, we employ a convolutional neural network with quantized weights (Q-CNN) so that it is deployable in mobile devices that have less memory resources and low overhead requirements. We show that the proposed Q-CNN, saved in at least 6 bits, yields superior performance over conventional massive MIMO hybrid beamforming.
  • Küçük Resim
    Öğe
    Sparse Array Selection Across Arbitrary Sensor Geometries With Deep Transfer Learning
    (Ieee-Inst Electrical Electronics Engineers Inc, 2021) Elbir, Ahmet M.; Mishra, Kumar Vijay
    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. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated labeled training data are unavailable and, more so, for arbitrary array configurations. To address this, we adopt a deep transfer learning (TL) approach, wherein we train a deep convolutional neural network (CNN) with data of a source sensor array for which calibrated data are readily available and reuse this pre-trained CNN for a different, data-insufficient target array geometry to perform sparse array selection. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL-CNN on the target model than the CNN trained with insufficient data from the same model. In particular, our TL framework provides approximately 20% higher sensor selection accuracy and 10% improvement in the direction-of-arrival estimation error.
  • Küçük Resim Yok
    Öğe
    Terahertz-Band Direction Finding With Beam-Split and Mutual Coupling Calibration
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Elbir, Ahmet M.; Mishra, Kumar Vijay; Chatzinotas, Symeon
    Terahertz (THz) band is currently envisioned as the key building block for achieving the future sixth generation wireless systems. The ultrawide bandwidth and very narrow beamwidth of THz systems offer the next order of magnitude in user densities and multifunctional behavior. However, wide bandwidth results in a frequency-dependent beampattern causing the beams generated at different subcarriers split and point to different directions. Furthermore, mutual coupling degrades the system's performance. This letter studies the compensation of both beam-split and mutual coupling for direction-of-arrival (DoA) estimation by modeling the beam-split and mutual coupling as an array imperfection. We propose a subspace-based approach using multiple signal classification with CalibRated for bEAm-split and Mutual coupling and MUltiple SIgnal Classification (CREAM-MUSIC) algorithm for this purpose. Via numerical simulations, we show that the proposed CREAM-MUSIC approach accurately estimates the DoAs in the presence of beam-split and mutual coupling.
  • Küçük Resim
    Öğe
    Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming
    (Ieee-Inst Electrical Electronics Engineers Inc, 2021) Elbir, Ahmet M.; Mishra, Kumar Vijay; Chatzinotas, Symeon
    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. In order to address the extremely high attenuation at THz, ultra-massive multiple-input multiple-output (MIMO) antenna systems have been proposed for THz communications to compensate propagation losses. However, the cost and power associated with fully digital beamformers of these huge antenna arrays are prohibitive. In this paper, we develop wideband hybrid beamformers based on both model-based and model-free techniques for a new group-of-subarrays (GoSA) ultra-massive MIMO structure in low-THz band. Further, driven by the recent developments to save the spectrum, we propose beamformers for a joint ultra-massive MIMO radar-communications system, wherein the base station serves multi-antenna user equipment (RX), and tracks radar targets by generating multiple beams toward both RX and the targets. We formulate the GoSA beamformer design as an optimization problem to provide a trade-off between the unconstrained communications beamformers and the desired radar beamformers. To mitigate the beam split effect at THz band arising from frequency-independent analog beamformers, we propose a phase correction technique to align the beams of multiple subcarriers toward a single physical direction. Additionally, our design also exploits second-order channel statistics so that an infrequent channel feedback from the RX is achieved with less channel overhead. To further decrease the ultra-massive MIMO computational complexity and enhance robustness, we also implement deep learning solutions to the proposed model-based hybrid beamformers. Numerical experiments demonstrate that both techniques outperform the conventional approaches in terms of spectral efficiency and radar beampatterns, as well as exhibiting less hardware cost and computation time.
  • Küçük Resim Yok
    Öğe
    Twenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniques
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Elbir, Ahmet M.; Mishra, Kumar Vijay; Vorobyov, Sergiy A.; Heath Jr, Robert W. W.
    Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic (EM) wave using an array of sensors toward a desired direction. It has been used in many engineering applications, such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advent of multiantenna technologies in, say, radar and communication, there has been a great interest in designing beamformers by exploiting convex or nonconvex optimization methods. Recently, machine learning (ML) is also leveraged for obtaining attractive solutions to more complex beamforming scenarios. This article captures the evolution of beamforming in the last 25 years from convex to nonconvex optimization and optimization to learning approaches. It provides a glimpse into these important signal processing algorithms for a variety of transmit-receive architectures, propagation zones, propagation paths, and multidisciplinary applications.