Terahertz (THz) communication has been envisioned as a promising technique for the future sixth generation (6G) and beyond wireless networks because of its tens of gigahertz (GHz) bandwidth. However, wideband THz channel results in an increase in bandwidth, which gives rise to the phenomenon known as beam squint. Additionally, techniques based on the standard multiple-input multiple-output (MIMO) paradigm, such as channel estimation (CE), are rendered inapplicable by beam squint. Several sparse CE algorithms have been proposed in compressed sensing (CS) to accurately estimate the wideband THz massive multiple-input-multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) channel. However, the exploitation of these expert algorithms constitutes a committee machine (CM), which is likely to be superior to that obtained by any one of the committee expert acting separately. In this paper, by leveraging the notion of CM methodology, we address the CE problem in wideband THz mMIMO-OFDM systems with beam squint. To estimate the wideband THz mMIMO sparse channel vectors efficiently, we first present a committee machine technique for CS (CMTCS), which makes use of the estimations from multiple expert algorithms. Next, in order to enhance the wideband THz mMIMO channel estimation performance, we develop the iterative CMTCS (ICMTCS) technique, a CMTCS algorithm extension. Using the restricted isometry property (RIP), the theoretical analysis of the proposed schemes for realizing an improved channel reconstruction performance is presented. Simulation results demonstrate that the proposed schemes are effective and offer better CE performance in terms of normalized mean squared-error (NMSE) than those dictated by other CS-based CE algorithms and the traditional least-squares-based methods.

Sparse Bayesian learning (SBL) and particularly relevant vector machines (RVMs) have drawn much attention to improving the performance of existing machine learning models. The methodology depends on a parameterized prior that enforces models with weight sparsity, where only a few are non-zeros. Wideband mmWave massive multiple-input multiple-output (mMIMO) systems with lens antenna array (LAA), expect to play a key role in future fifth-generation (5G) wireless systems. To provide the beamforming gain required to overcome path loss, we consider a lens antenna array (LAA)-based beamspace mMIMO system. However, the spatial-wideband influence causes the beam squint effect to emerge, making the beamspace channel path components exhibit a unique frequency-dependent sparse structure, and thus nullifies the frequency domain common support assumption. In this paper, we first propose a channel estimation (CE) algorithm, namely a reduced-antenna selection progressive support-detection (RAS-PSD), for the wideband mmWave mMIMO-OFDM systems with LAA, which considers the beam squint effect. Secondly, by exploring Bayesian learning (BL), a Gaussian Process hyperparameter optimization-based CE (GP-HOCE) algorithm is proposed for the considered system, where both its own hyperparameter and the hyperparameters of its adjacent neighbors governs the sparsity of each coefficient. The simulation results show that the wideband beamspace channel coefficients can be estimated more efficiently than those of the existing state-of-the-art algorithms in terms of normalized mean square error (NMSE) of CE for wideband mmWave mMIMO-OFDM systems.

Millimeter-wave (mmWave) massive multi-user multiple input multiple output (MU-MIMO) systems employ hybrid analog-digital precoding/combining design, to reduce the number of radio-frequency (RF) chains without large sum-rate performance loss. Committee machines combine several expert algorithms to yield better reliable estimates to those of its constituent experts. However, several committee machines approach mainly rely on the ‘quality’ of the joint support-set of expert algorithms combine by committee machine. In this paper, we present hybrid analog-digital precoder and combiner designs for mmWave massive MU-MIMO systems by exploiting the intersection of the estimated support-sets, namely, common support-set of ‘expert’ algorithms in a committee machine methodology. With S denoting the sparsity level, the proposed algorithms perform two essential tasks to identify in which subspace, generated by not more than S columns of the matrix of array response vectors, the optimal hybrid analog-digital precoders or combiners lie: Firstly, the selection of the committee machine common support set, which accepts the section where both the ‘experts’ agree. Secondly, if some column indices of the optimal hybrid analog-digital precoders or combiners do not lie in the current common support-set for the correct spanning space, −the algorithm searches over the matrix of array response vectors and selects column indices that exhibit the highest correlation with the signal residual, to acquire the remaining support. We derive the theoretical analysis for performance enhancement of the proposed algorithm. Simulation results demonstrate that the proposed design allows mmWave massive MU-MIMO systems to approach their unconstrained performance limits, and exhibits substantial sum-rate performance improvement over existing regularized channel diagonalization based schemes and the beam steering solution.

Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas (β), the number of iterations (n), and the relaxation parameter (ω). This paper also studies the impact of ω in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that ω has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait.However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly.Therefore, ECG signal denoising is a major preprocessing step and plays a crucial role in ECG-based biometric human identification.ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task.Moreover, the employed success measures and appropriate constitution of the ECG signal database also play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms.In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication.Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems.Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems.Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification.Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

Fifth-generation (5G) cellular networks will almost certainly operate in the high-bandwidth, underutilized millimeter-wave (mmWave) frequency spectrum, which offers the potentiality of high-capacity wireless transmission of multi-gigabit-per-second (Gbps) data rates. Despite the enormous available bandwidth potential, mmWave signal transmissions suffer from fundamental technical challenges like severe path loss, sensitivity to blockage, directivity, and narrow beamwidth, due to its short wavelengths. To effectively support system design and deployment, accurate channel modeling comprising several 5G technologies and scenarios is essential. This survey provides a comprehensive overview of several emerging technologies for 5G systems, such as massive multiple-input multiple-output (MIMO) technologies, multiple access technologies, hybrid analog-digital precoding and combining, non-orthogonal multiple access (NOMA), cell-free massive MIMO, and simultaneous wireless information and power transfer (SWIPT) technologies. These technologies induce distinct propagation characteristics and establish specific requirements on 5G channel modeling. To tackle these challenges, we first provide a survey of existing solutions and standards and discuss the radio-frequency (RF) spectrum and regulatory issues for mmWave communications. Second, we compared existing wireless communication techniques like sub-6-GHz WiFi and sub-6 GHz 4G LTE over mmWave communications which come with benefits comprising narrow beam, high signal quality, large capacity data transmission, and strong detection potential. Third, we describe the fundamental propagation characteristics of the mmWave band and survey the existing channel models for mmWave communications. Fourth, we track evolution and advancements in hybrid beamforming for massive MIMO systems in terms of system models of hybrid precoding architectures, hybrid analog and digital precoding/combining matrices, with the potential antenna configuration scenarios and mmWave channel estimation (CE) techniques. Fifth, we extend the scope of the discussion by including multiple access technologies for mmWave systems such as non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA), with limited RF chains at the base station. Lastly, we explore the integration of SWIPT in mmWave massive MIMO systems, with limited RF chains, to realize spectrally and energy-efficient communications.

Non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) (i.e., mmWave MIMO-NOMA) systems, is a promising technology to significantly enhance the spectrum efficiency of the fifth-generation (5G) mobile communication systems. Furthermore, enabling simultaneous wireless information and power transfer (SWIPT), where the energy-constrained user equipment (UE) equipped with power splitting receiver harvest both information and energy from the ambient radio-frequency (RF) signal, is crucial for energy-efficiency-maximization. In this paper, we initially design a user grouping algorithm, which preferentially groups UEs based on their channel correlation. Then, we design the analog RF precoder based on the selected user grouping for all beams, followed by a low-dimensional digital baseband precoder design, to further mitigate inter-beam interference and maximize the achievable sum-rate for the considered system. Subsequently, we equivalently transform the original optimization problem into a joint power allocation and power splitting maximization problem. Then, we propose to decouple the joint power allocation and power splitting nonconvex optimization problem into four separate optimization problems and then solved iteratively via an alternating optimization (AO) algorithm. Simulation results show that high spectrum and energy efficiency can be realized with the proposed algorithms than those of state-of-the-art designs and the conventional SWIPT-enabled mmWave MIMO-OMA system.

In this paper, we deal with channel estimation (CE) for high-mobility orthogonal frequency division multiplexing (OFDM) systems. To make the numerous (unknown) estimation for the high-mobility OFDM systems practicable, the channels are assumed to be time- and frequency-selective or doubly selective (DS) and approximated by a basis expansion model (BEM). As the DS channel requires the distributed acquisition of multiple correlated signals in the delay-Doppler channel domain, we proceed to estimate jointly sparse BEM coefficient vectors over a DS channel as against numerous channel coefficients. On account of channel time- variation, the resulting channel matrix in the frequency domain exhibits (approximately banded) pseudo-circular structure, which gives rise to a diagonally dominant yet full matrix rather than a diagonal matrix and thus induces inter-channel interference (ICI). On the premise of this observation, we propose a new pilot design scheme that identifies the optimal pilot placement and values for each pilot cluster to combat ICI. Furthermore, to obtain a channel estimator consistent with the jointly sparse delay-Doppler [i.e., two dimensional (2D)] channel model, an algorithm namely, distributed compressed sensing (DCS)-based stage determined matching pursuit (DCS-SdMP), is proposed. Our claims are supported by simulation results, which are obtained considering Jakes' channels with fairly high Doppler spreads, which show the superiority of the proposed schemes over other different methods of CE.

In multi-user millimeter wave (mmWave) multiple input multiple output (MIMO) systems, obtaining accurate information/knowledge regarding the channel state is crucial to achieving multi-user interference cancellation and reliable beamforming (BF)-to compensate for severe path loss. This knowledge is nonetheless very challenging to acquire in practice since large antenna arrays experience a low signal-to-noise ratio (SNR) before BF. In this paper, a multi-user channel estimation (CE) scheme namely generalized-block compressed sampling matching pursuit (G-BCoSaMP), is proposed for multi-user mmWave MIMO systems over frequency selective fading channels. This scheme exploits the cluster-structured sparsity in the angular and delay domain of mmWave channels determined by the actual spatial frequencies of each path. As the corresponding spatial frequencies of multi-user mmWave MIMO systems with Hybrid BF often fall between the discrete Fourier transform (DFT) bins due to the continuous Angle of Arrival (AoA)/Angle of Departure (AoD), the proposed G-BCoSaMP algorithm can address the resulting power leakage problem. Simulation results show that the proposed algorithm is effective and offer a better CE performance in terms of MSE when compared to the generalized block orthogonal matching pursuit (G-BOMP) algorithm that does not possess a pruning step.

Millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems will almost certainly use hybrid precoding to realize beamforming with few numbers of RF chains to reduce energy consumption, but require low complexity technique to improve spectral efficiency. While energy-efficient hybrid analog/digital precoders and combiners designs can subdue the high pathloss inherent in mmWave channels, they assume the use of infinite- (or high-) resolution phase shifters to realize the analog precoder and combiner pair which results in high hardware cost and power consumption. One promising solution is to employ the use of low-resolution phase shifters. In this paper, we first diverse the exploration of multiple candidates of array response vectors, to propose low-complexity hybrid precoder and combiner (LcHPC) design via stage-determined matching pursuit (SdMP) namely, LcHPC-SdMP for pursuing better achievable rate for mmWave MIMO systems. We initially decouple the joint optimization over hybrid precoders and combiners into two separate sparse recovery problems. Specifically, LcHPC-SdMP algorithm revises the identification step of orthogonal matching pursuit (OMP) to the selection of multiple “correct” column indices of the matrix of array response vectors, per iteration. Then adds a pruning step -after satisfying a sparsity level condition, to iteratively refine the sparse solution which aids in further accelerating the algorithm, by requiring fewer iterations. We then propose an algorithm which iteratively designs low-resolution (two-bit) hybrid analog-digital precoder and combiner (LrHPC), for pursuing efficiency while maximizing spectral efficiency. Simulation results demonstrate that the proposed LcHPC-SdMP algorithm performs very close to its full-digital precoding and achieves better spectral efficiency over state-of-the-art algorithms with a substantially reduced number of iteration than the recently proposed schemes. In addition, simulation results also reveal that the achievable rate of the proposed LrHPC algorithm is higher than those of the existing algorithms under consideration.

As a sampling paradigm to recover the sparse or compressible signals from very few incoherent linear measurements, compressed sensing (CS) has spurred much interest in recent years. Since tractable recovery algorithm is a crucial and major issue of CS, the greedy pursuit (GP) algorithms are generally preferred to enable accurate reconstruction of sparse or compressible signals from very few noisy incoherent measurements. While selecting multiple "correct" indices per iteration to improve the running time of orthogonal matching pursuit algorithm, the chosen indices are usually not the optimal one due to the iterative short-sighted decisions. In this paper, since the demand for fast reconstruction algorithms-possibly operating in linear time-is of significant interest, an algorithm namely, stage-determined matching pursuit (SdMP) is proposed. The SdMP algorithm exploits the selection of few indices (below the target signal sparsity level) per iteration, and then combines a backtracking or pruning step in some later iterations-albeit after satisfying a sparsity level conditions to refine the selected set. Using the restricted isometry property, the theoretical analysis of the SdMP algorithm and the sufficient conditions (guarantees) for realizing an improved reconstruction performance are presented. Through numerical simulations, it is shown that SdMP outperforms many GP algorithms that select multiple indices per iteration in terms of reconstruction accuracy and the running speed.

Most existing works on the deterministic pilot design for sparse Channel Estimation (CE) in Orthogonal Frequency Division Multiplexing (OFDM) system are based on the assumption that the pilot symbols are equally-powered. This assumption may not necessarily exhibit low coherence compressed CE. This, therefore, calls for the optimization of pilot symbols and their placement which in the literature is considered as a disjoint optimization problem. In this paper, the joint pilot placement and symbol design optimization problem for sparse CE in OFDM systems is considered based on minimizing the mutual coherence of the Fourier submatrix associated with the pilot subcarriers. In order to avoid the disjoint optimization of the pilot symbol values and their placements, a joint pilot placement and pilot symbol design scheme is proposed that optimizes over both the pilot symbol values and their placements as a single design optimization problem. Simulation results demonstrate that the proposed scheme is effective and offer a better CE performance — in terms of Mean Square Error (MSE) and Bit Error Rate (BER), when compared to former pilot placement schemes that assume the equally powered pilot symbols and other schemes that jointly design the pilot symbols and their placement. It was also observed that the proposed scheme can realize 18.75% improvement in bandwidth efficiency with the same CE performance compared with the least squares (LS) CE.

For proper matrix ensembles, it has been known that the greedy pursuit (GP) algorithms are computationally efficient and fast to reconstruct sparse signals from far fewer linear measurements. In considering several parameters such as sparsity level, sparse signal ambient dimension and the number of linear measurements, the GP algorithms have been shown to perform differently in estimating sparse signals. According to data fusion principle, fusing completely the estimated support set of different reconstruction algorithms can improve signal recovery performance. It can, however, lead to the increased probability of estimating incorrect support indices, and thus degrades the signal reconstruction accuracy. In this paper, a new fusion framework, namely collaborative framework of algorithms (CoFA), is proposed to pursue accurate reconstruction of the sparse signals from far fewer linear measurements. The two main ingredients of the proposed scheme that control the estimation of incorrect support indices are pre-selection support of orthogonal matching pursuit (OMP) algorithm and Thresholding -to eliminate unpromising indices from the identied support set of any participating algorithm. Using the restricted isometry property, the theoretical analysis of the CoFA scheme and the sufficient conditions (guarantees) for realizing an improved reconstruction performance are presented. Simulation results demonstrate that the proposed scheme is effective and offer a better channel estimation performance in terms of mean-squared-error (MSE) and bit-error-rate (BER) when compared to other reconstruction algorithms, without the significant increase in computational complexity.

In this paper, the problem of the deterministic pilot allocation for sparse channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) system is investigated. This method is based on mutual coherence minimization of the measurement matrix associated with the OFDM system pilot subcarriers. It is known that if the set of pilot pattern is a Cyclic Difference Set (CDS), the mutual coherence of the measurement matrix is minimized. However, CDS in most practical OFDM system is not available. Few research efforts have tackled the problem of pilot allocation by proposing methods that lead to suboptimal solutions in order to ignore the computationally complex exhaustive search method. This contribution, however, proposes two pilot allocation design schemes for the construction of deterministic partial Fourier matrices satisfying the Restricted Isometry Property (RIP) namely, the Generic Random Search (GRS) and Progressive Search (PS) based on bounding the mutual coherence between different columns of the measurement matrix. Simulation results show that the two proposed pilot allocation design schemes are effective and offer a better channel estimation performance in terms of MSE when compared to former pilot allocation design methods.

Channel estimation is one of the most essential requirements in Orthogonal Frequency Division Multiplexing (OFDM) system and its exactness severely impacts the performance of the entire system. The most significant tap (MST) selection approach requires prior knowledge of channel statistics which increases complexity and a reduced spectral efficiency. Therefore, in order to tackle the approach of sparse channel estimation in OFDM systems, an improved time domain threshold (ITDT) for sparse channel estimation is thus proposed. The proposed ITDT aims at achieving an improved channel estimation performance with no prior knowledge of channel state information and noise standard deviation. At the start, an initial channel impulse response is obtained by Least Square (LS) approach. Next, a novel ITDT is proposed to obtain the estimated coefficient of noise by extracting the non-zero channel taps. Simulation results indicate a better normalized mean square error (NMSE) of 20-dB gain for a same NMSE for the proposed ITDT compare to the non-sparse LS time domain estimator.