Simultaneous Orthogonal Matching Pursuit Research Articles

Spatial-frequency parallel subsampling for distributed compressive sensing in ultrasonic imaging inspection

To address the problem of the high hardware requirements and insufficient data storage capacity in current ultrasonic imaging testing, a novel approach is developed using a programmable device, which combines spatial-frequency parallel subsampling with the distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm to achieve fast and high-quality ultrasonic imaging inspection with a small amount of subsampled data. The spatial sparse measurement method was employed to achieve spatial subsampling and minimize the count of signals. Additionally, frequency subsampling was utilized to significantly reduce the data volume of time-domain signals while ensuring signal quality by truncating the primary testing frequency components. The subsampled data was then reconstructed using distributed compressive sensing (DCS) for multi-channel data reconstruction. The experiment of ultrasonic scanning imaging was conducted on a carbon steel specimen containing six transverse through-holes with a diameter of Ф1.5 mm at different depths. The ultrasonic signals were acquired using the spatial-frequency parallel subsampling method, and subsequently reconstructed using the DCS-SOMP algorithm. The results show that the proposed method achieves comparable image quality to that obtained with complete data, using only 1/8 of the complete data, while accurately locating and quantifying defects.

Artificial neural network-based sparse channel estimation for V2V communication systems

Abstract Artificial neural networks (ANNs) have gained a lot of attention from researchers in the past few years and have been employed on a large scale. They have also been gaining momentum in wireless communication systems. For efficient vehicle-to-vehicle (V2V) channel communication, a sparse multipath channel issue must be studied. To minimize the multipath effect, a time reversal (TR) operation and time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) have been appealing because of their fast synchronization and active spectral efficiency. To improve the transceiver's execution in a frequency-selective fading channel environment, an OFDM system is used to reduce inter- symbol interference (ISI). Simultaneous Orthogonal Matching Pursuit (SOMP) channel state estimator algorithm suffer from high computational cost and high computational complexity. The ANN algorithm has better performance than SOMP algorithm. The proposed neural network technologies have lower complexity than the SOMP algorithm. The application of ANN is capable of solving complex problems, such as those encountered in image, signal processing and have been implemented for channel estimation in OFDM. The proposed ANN outperformed the SOMP algorithm with regard to signal compensation. Overall, the ANN algorithm achieved the best performance. This study proposes an ANN-based sparse channel state estimator. Regarding the bit error rate (BER) metric, the proposed estimator outperforms the channel estimation approach based on the SOMP. The simulation results confirm the efficacy of the proposed approach.

Compressive Sensing-Based Channel Estimation for Uplink and Downlink Reconfigurable Intelligent Surface-Aided Millimeter Wave Massive MIMO Systems

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally.

Learned-SBL-GAMP based hybrid precoders/combiners in millimeter wave massive MIMO systems.

In Millimeter-Wave (mm-Wave) massive Multiple-Input Multiple-Output (MIMO) systems, hybrid precoders/combiners must be designed to improve antenna gain and reduce hardware complexity. Sparse Bayesian learning via Expectation Maximization (SBL-EM) algorithm is not practically feasible for high signal dimensions because estimating sparse signals and designing optimal hybrid precoders/combiners using SBL-EM still provide high computational complexity for higher signal dimensions. To overcome the issues of high computational complexity along with making it suitable for larger data sets, in this paper, we propose Learned-Sparse Bayesian Learning with Generalized Approximate Message Passing algorithm (L-SBL-GAMP) algorithm for designing optimal hybrid precoders/combiners suitable for mmWave Massive MIMO systems. The L-SBL-GAMP algorithm is an extension of the SBL-GAMP algorithm that incorporates a Deep Neural Network (DNN) to improve the system performance. Based on the nature of the training data, the L-SBL-GAMP can design the optimal Hybrid precoders/combiners, which enhances the spectral efficiency of mmWave massive MIMO systems. The proposed L-SBL-GAMP algorithm reduces the iterations, training overhead, and computational complexity compared to the SBL-EM algorithm. The simulation results unveil that the proposed L-SBL-GAMP provides higher achievable rates, better accuracy, and low computational complexity compared to the existing algorithm, such as Orthogonal Matching Pursuit (OMP), Simultaneous Orthogonal Matching Pursuit (SOMP), SBL-EM and SBL-GAMP for mmWave massive MIMO architectures.

RIS-Aided mmWave MIMO Channel Estimation Using Deep Learning and Compressive Sensing

Reconfigurable intelligent surface (RIS) assisted wireless systems require accurate channel state information (CSI) to control wireless channels and improve both the bandwidth and energy efficiency. However, CSI acquisition is non-trivial for two reasons: 1) the passive nature of RIS does not allow transceiving and processing pilot signals, and 2) the dimensions of the cascaded channel between transceivers increases with the large number of RIS elements, which yields high training overhead and computational complexity. While prior art has mainly focused on frequency-flat channel estimation, this paper proposes novel data-driven and compressive sensing based approaches for estimating both frequency-flat and frequency-selective cascaded channels of RIS-assisted multi-user millimeter-wave large multiple input multiple output (MIMO) systems with limited training overhead. The proposed methods exploit the common sparsity property among the different subcarriers and the double-structured sparsity property of the angular cascaded channel matrices as different angular cascaded channels observed by different users share completely common non-zero rows and user-specific column supports. The proposed data-driven cascaded channel estimation approaches use denoising neural networks to accurately detect channel supports. Alternatively, when data-training capabilities are not available, the compressive sensing based orthogonal matching pursuit (OMP) approach relies on sparsity properties and applies simultaneous OMP to detect the channel supports. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes. When compared to other OMP approaches that achieve an NMSE gap of 5 to 6 dB with respect to the Oracle least square lower bound, the proposed algorithms reduce the lower bound gap to only 1 dB, while reducing complexity by more than two orders of magnitude.

A novel image recognition using Fuzzy C-Means and content-based fabric image retrieval

ABSTRACT Due to the growth of image exploitation, there is a requirement of systemizing the images in several ways. Time consumption and accuracy are the most important problem in the CBIR method. Therefore, a Content-Based Fabric Image Retrieval (CBFIR) method is proposed in this paper that is based on texture and colour features extraction. The Fuzzy C-Means (FCM) algorithm creates primary clusters and the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm updates every cluster in the dictionary. To find the sparse representation dictionary, the test image is compared by the proposed method with the dictionaries generated. The input images are obtained from the fabric dataset. Several distance based similarity measures are utilized for comparison. It can be seen that the proposed method’s performance is sufficiently good concerning the rate of recall, accuracy, and precision with the values of 97%, 91%, and 88% respectively.

Research on Distributed Compressed Sensing With Dynamic Block Sparsity for Underwater Acoustic Channel Estimation

Channel estimation plays a crucial role in Internet of Underwater Things networks. Traditional channel estimation algorithms have limited performance improvement under the block-sparsity underwater acoustic channel, and the distributed compressed sensing (DCS) method has not been researched under block-sparsity recovery. In this article, we propose DCS with block sparsity for underwater acoustic channel estimation under short observation length and low SNR scenarios. Specifically, the underwater acoustic channels are partitioned some sub-blocks that have the identical size, and the simultaneous block orthogonal matching pursuit algorithm (SBOMP) is proposed to enhance the channel estimates that have common delays. However, the number of nonzero taps located in a partitioned sub-block does not equal to the size of partitioned sub-blocks, and the channel between adjacent two data blocks may have slightly difference, when the SBOMP algorithm is applied, there would be much estimated noise. In order to address this problem, in this article, we also propose dynamic block sparsity-based SBOMP which is referred to as DSBOMP. The proposed DSBOMP consists of the SBOMP algorithm, first-order derivative of the residual method, and parallel comparison strategy. The first-order derivative is used to remove some negligible taps and dynamically measure the number of significant taps; the parallel comparison strategy is used to check whether the current taps are available. Both simulation and sea trial data indicate that, our proposed SBOMP and DSBOMP algorithms have better channel estimation performance than tradition algorithms under short observation length and low SNR. Moreover, our proposed DSBOMP achieves the best communication performance.

MmWave extra-large-scale MIMO based active user detection and channel estimation for high-speed railway communications

The current High-Speed Railway (HSR) communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs. To this end, this paper investigates millimeter-Wave (mmWave) extra-large scale (XL)-MIMO-based massive Internet-of-Things (IoT) access in near-field HSR communications, and proposes a block simultaneous orthogonal matching pursuit (B-SOMP)-based Active User Detection (AUD) and Channel Estimation (CE) scheme by exploiting the spatial block sparsity of the XL-MIMO-based massive access channels. Specifically, we first model the uplink mmWave XL-MIMO channels, which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays. By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels, the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing (MMV-CS) problem. Based on the designed sensing matrix, a B-SOMP algorithm is proposed to achieve joint AUD and CE. Finally, simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.

Triple-Structured Compressive Sensing-Based Channel Estimation for RIS-Aided MU-MIMO Systems

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation for RIS-aided systems is still a critical challenge due to the excessive amount of parameters in the cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we analyze and exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Furthermore, a novel on-grid Multi-user Triple-Structured-Compressive-Sensing simultaneous orthogonal matching pursuit (MTSCS-SOMP) algorithm along with an enhanced super-resolution (gridless) generalized iterative reweighted (MTSCS-IR) scheme are successively proposed. The former is practical and can be easily employed with low computational complexity, and the latter is further proposed to handle the severe power leakage problem encountered in mmWave channel estimations. Besides, we extend the sparsity property and algorithms from uniform linear array (ULA) configuration to uniform planar array (UPA), by transforming cascaded channel from matrix to tensor. Simulation results show that our approaches can significantly reduce pilot overhead over 50% and achieve enhanced performance on estimation accuracy.

Channel Estimation for Extremely Large-Scale MIMO: Far-Field or Near-Field?

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is promising to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel state information is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in the angular domain, which is achieved by the classical far-field planar-wavefront assumption. However, due to the non-negligible near-field spherical-wavefront property in XL-MIMO, this channel sparsity in the angular domain is not achievable. Therefore, existing far-field channel estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angular-domain representation that only considers the angular information, we propose a polar-domain representation, which simultaneously accounts for both the angular and distance information. In this way, the near-field channel also exhibits sparsity in the polar domain, based on which, we propose on-grid and off-grid near-field XL-MIMO channel estimation schemes. Firstly, an on-grid polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm is proposed to efficiently estimate the near-field channel. Furthermore, an off-grid polar-domain simultaneous iterative gridless weighted (P-SIGW) algorithm is proposed to improve the estimation accuracy. Finally, simulations are provided to verify the effectiveness of our schemes.

Efficient Distributed Multi-Task Schemes for mmWave MIMO Channel Estimation

In this study, the problem of sparse channel estimation is investigated with the employment of a fully distributed approach. We exploit the spatially joint sparsity structure of the involved channels to formulate the channel estimation problem in the angular domain. The devices collaboratively estimate their channel sparsity support sets before the local estimation of the channel values, assuming the existence of global and common support subsets. The combination of the proposed distributed scheme with the classical Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm is calledWeighted Distributed Simultaneous Orthogonal Matching Pursuit (WDiSOMP). The performance of WDiSOMP is assessed under a multi-task scheme considering a new weighted voting method. The new mechanism is applied at each support set index and enhances its estimation accuracy and, thus, the eventual estimation of the overall channel. The mean squared error (MSE) is utilized to derive the performance bounds and assess the efficacy of the WDiSOMP estimator. Finally, the performance and the theoretical findings are evaluated via the comparison of WDiSOMP with Distributed Simultaneous Orthogonal Matching Pursuit (DiSOMP), local SOMP, and a centralized approach based on Structured SOMP (SSOMP) in terms of the MSE.

Exploiting Rapidly Time-Varying Sparsity for Underwater Acoustic Communication

Due to the combined effects of different acoustic propagation, reflection patterns in different time-scale, time-varying underwater acoustic (UWA) channel generally exhibits hybrid sparsity, i.e., contains not only rapidly time-varying elements, but also stationary or slowly time-varying ones. While the classic sparse reconstruction algorithms such as orthogonal matching pursuit (OMP) generally do not take it into account, the existence of static multipath will unavoidably hinder the tracking of rapid time variations. In this paper, a sequential adaptive observation length orthogonal matching pursuit (SAOLOMP) approach is proposed to sequentially explore rapidly time-varying sparsity in a two-step sequential manner. Specifically, the static components are firstly separated via simultaneous orthogonal matching pursuit (SOMP) among continuous measurements. Upon the residual error of the SOMP, the measurement-wise OMP is applied for estimating the remained dynamic components. As the variation of the SOMP residual error indicates how rapidly the remained dynamic multipath components vary, the observation length of the OMP is adjusted according to the gradient of residual error to adapt to rapidly time-varying components. Finally, both types of sparse components are summed up to obtain the whole channel response. Numerical simulations as well as experimental results from field UWA communication data show that the proposed algorithm exhibits better performance in exploiting rapidly time-varying sparsity than the state-of-the-art compressive methods do under UWA channel.

Sparse Parameter Estimation and Imaging in mmWave MIMO Radar Systems With Multiple Stationary and Mobile Targets

This work conceives novel target detection and parameter estimation schemes in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their radar cross-section (RCS) coefficients, angle, range locations together with the number of targets. Next, mMR systems with mobile targets and platform are considered, followed by development of the simultaneous OMP (SOMP)-based mMR (SmMR) algorithm for RCS, angle/range estimation together with their Doppler velocities. The proposed algorithms lead to a significant improvement in performance since they exploit the inherent sparsity of the mMR scattering scene in contrast to the conventional schemes. Two-dimensional (2D) mMR imaging procedures are also presented for both scenarios in the angle, range, and Doppler dimensions. Analytical expressions are derived for the Cramér-Rao bounds (CRBs) for the mean-squared error (MSE) of joint estimation of the RCS coefficients and Doppler velocities. Simulation results demonstrate that proposed schemes perform well even in low signal-to-noise ratio (SNR) scenarios with a few snapshots of the scattering environment and yield improved performance in comparison to existing sparse as well as non-sparse schemes.

A Full-Polarization Radar Image Reconstruction Method with Orthogonal Coding Apertures

Imaging radar is widely applied in both military and civil fields, including remote sensing. In recent years, polarization information has attracted more and more attention in the imaging radar. The orthogonality between different channels is always the core problem for the full-polarization imaging radar. To solve this problem, an image reconstruction method using orthogonal coding apertures technique is proposed for full-polarization imaging radar in this paper. Firstly, the signal model of the orthogonal coding apertures is proposed. This model realizes the ideal time-domain orthogonality between switching two channels by the apertures with two trains of orthogonal codes. Then, a multichannel joint reconstruction method based on compressed sensing is proposed for the imaging processing, which is named the coded aperture simultaneous orthogonal matching pursuit (CAS-OMP) algorithm. The proposed algorithm combines the information of all polarization channels so as to ensure the consistency of the scattering point position obtained by each polarization channel and also improves the reconstruction accuracy. Finally, the simulation experiments using both the simple scaled model of the satellite and measured data of an unmanned aerial vehicle (UAV) are conducted, and the effectiveness of the proposed method is verified.

Optimization of Precoder and Combiner in mmWave Hybrid Beamforming Systems for Multi-user Downlink Scenario

ABSTRACT Beamforming at millimeter wave (mmWave) band, promises to significantly support 5G networks in achieving their performance goals. The conventional digital beamforming uses a separate RF chain for each antenna element, while it leads to high cost and hardware complexity in mmWave massive MIMO antenna systems. Beamforming with multiple data streams called precoding improves the system’s spectral efficiency and one of its kind hybrid beamforming reduces the cost and overcomes the hardware limitation by using reduced number of RF chains. This work considers, transmit precoding, receive combining in mmWave hybrid beamforming systems and constructs a dictionary matrix containing array response vectors. This paper proposes an extended simultaneous orthogonal matching pursuit (ESOMP) algorithm to compute the block-sparse matrix. The nonzero rows of block-sparse matrix and dictionary matrix are further processed to achieve precoder/combiner optimization in multi-user downlink scenario. Simulation results reveal that the proposed method performs close to the ideal digital beamforming scheme while improving the spectral efficiency when compared to the state-of-the-art algorithm.

Sparse Bayesian Learning Kalman Filter-based Channel Estimation for Hybrid Millimeter Wave MIMO Systems: A Frequency Domain Approach

In this paper, channel estimation for Millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) communication system is presented. In the frequency domain, the Sparse Bayesian Learning-based Kalman Filter (SBL-KF) scheme is developed to estimate the sparse channel vector of hybrid mmWave MIMO architecture. The mmWave MIMO hybrid architecture provides reduced power loss and achievable data rates at mmWave frequencies. Frequency-selective channels are sparse at mmWave frequencies. The recovery of the sparse channel is estimated using different channel estimation methods. The hybrid precoding technique achieves both multiplexing gain and array gain in mmWave channels. Simulation results of the proposed SBL-KF scheme show low estimation error over the existing Orthogonal Matching Pursuit, Simultaneous Orthogonal Matching Pursuit, Simultaneous Weighted Orthogonal Matching Pursuit algorithm and SBL scheme. The complexity of the online estimation SBL-KF algorithm is low and it is ideally suited for implementation in an efficient mmWave MIMO system.

A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification

In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images. Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual network (3DDRN). Specifically, a SAE neural network was first built to reduce the dimensions of original HSIs. A 3D convolutional neural network (3DCNN) was then designed and the residual network module was introduced to build a 3DDRN. The dimension-reduced 3D HSI cubes were input into the 3DDRN to extract identifiable joint spectral-spatial features. Finally, the deep features continuously identified by the 3DDRN were input to Softmax classification layer to realize the classification. In addition, Batch Normalization (BN) and Dropout were used during the learning process to avoid overfitting on training data. The training and test sets of Indian Pines (IP), Pavia University (PU) and Salinas (SA) hyperspectral data sets were selected as the modeling and verification data sources. Six classical classification algorithms were adopted for comparing our proposed method, specifically including conventional machine learning algorithms of Radial Basis Function-Support Vector Machine (RBF-SVM), Kernel Simultaneous Orthogonal Matching Pursuit (KSOMP) and Local Binary Pattern-K-Nearest Neighbor (LBP-KNN), and mainstream deep learning algorithms of Variational Autoencoder (VAE), Convolutional Neural Network (CNN) and Spectral-Spatial Residual Network (SSRN). The results showed that the overall accuracy (OA) reached 98.97%, 99.69% and 99.24%, respectively, only based on 10%, 5% and 1% of training samples for IP, PU and SA. Consequently, the proposed method shows a better classification performance, even in the case of limited samples.

Hyperspectral image spectral-spatial classification via weighted Laplacian smoothing constraint-based sparse representation.

As a powerful tool in hyperspectral image (HSI) classification, sparse representation has gained much attention in recent years owing to its detailed representation of features. In particular, the results of the joint use of spatial and spectral information has been widely applied to HSI classification. However, dealing with the spatial relationship between pixels is a nontrivial task. This paper proposes a new spatial-spectral combined classification method that considers the boundaries of adjacent features in the HSI. Based on the proposed method, a smoothing-constraint Laplacian vector is constructed, which consists of the interest pixel and its four nearest neighbors through their weighting factor. Then, a novel large-block sparse dictionary is developed for simultaneous orthogonal matching pursuit. Our proposed method can obtain a better accuracy of HSI classification on three real HSI datasets than the existing spectral-spatial HSI classifiers. Finally, the experimental results are presented to verify the effectiveness and superiority of the proposed method.

Detection of epileptic seizures from compressively sensed EEG signals for wireless body area networks

Wireless body area networks (WBANs) are gaining popularity for tele-monitoring of biomedical signals such as the electroencephalogram (EEG), with diagnosing and monitoring of epileptic seizures being one of the most important applications. Most seizure-detection algorithms cannot be applied directly to the compressed data in WBANs as they require full reconstruction of original EEG signals. In this study, we propose a novel feature for real-time automatic single-channel seizure detection which does not require complete reconstruction of original EEGs. The feature is based on iteratively applying the orthogonal matching pursuit (OMP) algorithm on the compressed EEG data and computing the rate by which the energies of partially reconstructed signals are increased. The feature, i.e. partial energy difference (PED), is then used for classifying seizure and non-seizure states. We also extend this method to the case for multichannel EEG. In multichannel case, the simultaneous OMP (SOMP) with a low number of iterations (1 and 15 iteration) is applied to the compressed data and the difference between the Frobenius norms of partially reconstructed signals is used as a multivariate feature for the classification of seizure and non-seizure states. The proposed features are used to detect seizure intervals in the EEG database provided by the University of Bonn and the CHB-MIT database. The results show that the proposed features can classify seizure epochs from non-seizures even for compression ratios as small as 0.05. The results also show that the proposed single-channel method achieves improvement of up to 4% for the area under the curve (AUC) with significantly less execution time compared to the benchmark matrix determinant (MD) classification approach. The results of applying the proposed multivariate feature to seizure and non-seizure segments of length 5 s from the CHB-MIT database show that it achieves mean AUC values of 0.941, 0.941, and 0.939 with mean execution times of 9.5, 10.7, and 13.1 s for compression ratios of 0.05, 0.1, and 0.2, respectively. Applying a threshold to the PED in a leave-one-out cross-validation (LOO-CV) scenario generates a sensitivity of 0.873,specificity of 0.710, and accuracy of 0.791 at the compression ratio of 0.05. The proposed single- and multichannel features have the potential for deployment in WBANs for the real-time tele-monitoring of epilepsy patients in healthcare applications.

Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems

This work conceives techniques for the design of hybrid precoders/combiners for optimal bit allocation in frequency selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, toward transmission rate maximization. Initially, the optimal fully digital ideal precoder/ combiner design is derived together with a closed-form expression for the optimal bit allocation in the above system. This is followed by the development of a framework for optimal transceiver design and bit allocation in a practical mmWave MIMO-OFDM implementation with a hybrid architecture. It is demonstrated that the pertinent problem can be formulated as a multiple measurement vector (MMV)-based sparse signal recovery problem for joint design of the RF and baseband components across all the subcarriers, and an explicit algorithm is derived to solve this using the simultaneous orthogonal matching pursuit (SOMP). To overcome the shortcomings of the SOMP-based greedy approach, an MMV sparse Bayesian learning (MSBL)-based state-of-the-art algorithm is subsequently developed, which is seen to lead to improved performance due to the superior sparse recovery properties of the Bayesian learning framework. Simulation results verify the efficacy of the proposed designs and also demonstrate that the performance of the hybrid transceiver is close to that of its fully-digital counterpart.