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The Large Intelligent Surface (LIS) is a promising technology in the areas of wireless communication, remote sensing and positioning. It consists of a continuous radiating surface located in the proximity of the users, with the capability to communicate by transmission and reception (replacing base stations). Despite its potential, there are numerous challenges from an implementation point of view, with the interconnection data-rate, computational complexity, and storage the most relevant ones. In order to address these challenges, hierarchical architectures with distributed processing techniques are envisioned to be relevant for this task, while ensuring scalability. In this work we perform algorithm-architecture codesign to propose two distributed interference cancellation algorithms, and a tree-based interconnection topology for uplink processing. We also analyze the performance, hardware requirements, and architecture trade-offs for a discrete LIS, in order to provide concrete case studies and guidelines for efficient implementation of LIS systems. [ABSTRACT FROM AUTHOR]

It is an essential technique for the moving user nodes (UNs) with clock offset and clock skew to resolve the joint localization and synchronization (JLAS) problem. Existing iterative maximum likelihood methods using sequential one-way time-of-arrival (TOA) measurements from the anchor nodes’ (AN) broadcast signals require a good initial guess and have a computational complexity that grows with the number of iterations, given the size of the problem. In this paper, we propose a new closed-form JLAS approach, namely CFJLAS, which achieves the asymptotically optimal solution in one shot without initialization when the noise is small, and has a low computational complexity. After squaring and differencing the sequential TOA measurement equations, we devise two intermediate variables to reparameterize the non-linear problem, and convert it to a simpler one of solving two simultaneous quadratic equations. We then solve the equations analytically to obtain a raw closed-form JLAS estimation. Finally, we apply a weighted least squares (WLS) step to optimize the estimation. We derive the Cramér-Rao lower bound (CRLB), analyze the estimation error, and show that the estimation accuracy of the CFJLAS reaches the CRLB under the small noise condition. The complexity of the new CFJLAS is only determined by the size of the problem, unlike the conventional iterative method, whose complexity is additionally multiplied by the number of iterations. Simulations in a 2D scene verify that the estimation accuracies of the new CFJLAS method all reach the CRLB under the small noise condition. Compared with the conventional iterative method, the proposed new CFJLAS method does not require initialization, obtains the optimal solution under the small noise condition, and has a low computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents a low-complexity linear minimum mean-squared-error-based iterative soft interference cancellation (LMMSE-ISIC) scheme for multiple-input multiple-output (MIMO) systems. To avoid direct matrix inversion in the conventional LMMSE-ISIC scheme, expressions are derived to obtain the filtering vector and estimate of the conventional scheme; the direct inverse operation is then replaced by the vector-based operations in the proposed scheme. Thus, for the worst-case scenario in the proposed scheme, the complexity of estimating each transmit symbol is approximately proportional to the square of the number of receive antennas; in the conventional scheme, it is approximately proportional to the cube of the number of receive antennas. In addition, because there are no approximations for deriving the filtering vector and estimate, the proposed LMMSE-ISIC scheme achieves near-optimum performance identical to that of the conventional scheme, which is close to the matched filter bound of the channel. Further, the extension of the proposed scheme for iterative detection and decoding is developed for coded systems. The simulated results confirm that the conventional and proposed schemes outperform the approximated matrix inversion based schemes and achieve the identical error performance in both uncoded and coded systems, while the complexity order of the proposed scheme is similar to or even lower than those of the approximation schemes. Therefore, the proposed scheme can be considered an effective near-optimum LMMSE-based iterative detection approach for MIMO systems, especially for massive MIMO systems with a high load factor. [ABSTRACT FROM AUTHOR]

In this work, we study the combination of adaptive filters in colored noise environments. First, a combination framework using delayed weights is introduced to tackle the colored noise. Based on this, delayed convex and affine combinations of two LMS filters are developed, resulting in the so-called Dcvx-LMS and Daff-LMS algorithms. Then, the convergence behaviors of the two algorithms are investigated using standard mean-square deviation analysis. In addition, to speed up the convergence and reduce the computational complexity, we propose delayed combination with periodic feedback, delayed combined-step-size and block implementation methods. Finally, simulation results demonstrate the superiority of our algorithms over previously reported techniques in the presence of colored noise. [ABSTRACT FROM AUTHOR]

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction. [ABSTRACT FROM AUTHOR]

Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspace. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum a posteriori criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification (HSI) has been widely used in many fields. However, image noise, atmospheric conditions, material distribution and other factors seriously degrade the classification accuracy of HSIs. To alleviate these issues, a new approach, namely adaptive weighted quaternion Zernike moments (AWQZM), is proposed, which extracts effective spatial-spectral features for pixels in HSI classification. The main contributions and novelties of the method are as follows: 1) the AWQZM can adaptively set weights for each pixel in the neighborhood, which not only can flexibly search for homogeneous regions of HSIs, but also can strengthen the similarity of pixels from the same class and the distinctiveness of pixels from different classes; 2) the AWQZM can be constructed in a small subset of bands through a grouping strategy, thereby reducing the computational complexity; and 3) the introduction of quaternions can preserve the spatial correlation among bands and reduce the loss of data information, and the use of quaternion phase information makes the extracted features more informative and discriminative. Moreover, the spectral features and spatial features are combined to achieve better HSI classification results. Experimental results on three benchmark data sets demonstrate that the proposed approach achieves better classification performance than other related approaches. [ABSTRACT FROM AUTHOR]

As a new member of convex universal learning machines (CULMs), an augmented space linear model (ASLM) demonstrates strong learning and tracking capabilities in the fields of signal process and machine learning. Unfortunately, the ASLM is only suitable for offline learning, and its estimated accuracy and noise immunity highly depend on the estimated error learned from a generated error table. To address these two issues and apply ASLM into adaptive filtering, online ASLM algorithms with a weighted $k$ nearest neighbor ($k$ -NN) method, based on an updated orthogonal error table, are therefore proposed in this paper. To further improve the robustness of ASLM to heavy-tail noises, a robust ASLM (RASLM) algorithm is proposed with the help of kernel function, and thus a novel robust adaptive filtering algorithm named maximum correntropy criterion RASLM (MCC-RASLM) is developed based the maximum correntropy criterion. However, the linear growth error table existing in MCC-RASLM incurs an ever-growing computational burden. Therefore, to reduce the computational complexity of MCC-RASLM, another novel online variable-centroid clustering is proposed to generate a maximum correntropy criterion cluster RASLM (MCC-C-RASLM) algorithm. Finally, the theoretical analyses regarding robustness and the error bound of RASLM with weighted $k$ -NN regression are performed to verify the effectiveness of MCC-RASLM. Simulation results based on synthetic and real-world data validate the superiorities of the proposed algorithms from aspects of filtering accuracy and robustness. [ABSTRACT FROM AUTHOR]

In practical hardware platforms such as FPGA orDSP, the FFT operation is based on a length of $2^{L}$. However, due to the system limitation, the length of sampled signal would be not equal to $2^{L}$. Thus, zero-padding is performed on the signal before FFT operation. Most traditional sinusoidal frequency estimation methods are for signals without zero-padding. When applying these methods to the zero-padded signal, the performances would degrade. To solve this problem, a novel frequency estimation method for zero-padded signal is proposed in this paper. In the proposed method, by using the analytical expression of the DFT signal after zero-padding, an amplitude ratio function is defined between the two DFT samples nearest to the true frequency. Based on the monotonic property of the ratio function, Newton-Raphson method or bisection search is used to estimate the residual frequency. Two implementation flows are proposed, with similar performance and computational complexity. Experimental results show that the proposed method achieves better RMSE performance than the traditional methods. More importantly, the proposed estimation method can be applied to both the zero-padded signal and the non-padding signal. [ABSTRACT FROM AUTHOR]

While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems. Based on a proximal primal-dual approach, this paper presents a new (stochastic) distributed algorithm with Nesterov momentum for accelerated optimization of non-convex and non-smooth problems. Theoretically, we show that the proposed algorithm can achieve an $\epsilon$ -stationary solution under a constant step size with $\mathcal {O}(1/\epsilon ^2)$ computation complexity and $\mathcal {O}(1/\epsilon)$ communication complexity when the epigraph of the non-smooth term is a polyhedral set. When compared to the existing gradient tracking based methods, the proposed algorithm has the same order of computation complexity but lower order of communication complexity. To the best of our knowledge, the presented result is the first stochastic algorithm with the $\mathcal {O}(1/\epsilon)$ communication complexity for non-convex and non-smooth problems. Numerical experiments for a distributed non-convex regression problem and a deep neural network based classification problem are presented to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A computationally efficient method for online joint state inference and dynamical model learning is presented. The dynamical model combines an a priori known, physically derived, state-space model with a radial basis function expansion representing unknown system dynamics and inherits properties from both physical and data-driven modeling. The method uses an extended Kalman filter approach to jointly estimate the state of the system and learn the unknown system dynamics, via the parameters of the basis function expansion. The key contribution is a computational complexity reduction compared to a similar approach with globally supported basis functions. By using compactly supported radial basis functions and an approximate Kalman gain, the computational complexity is considerably reduced and is essentially determined by the support of the basis functions. The approximation works well when the system dynamics exhibit limited correlation between points well separated in the state-space domain. The method is exemplified via two intelligent vehicle applications where it is shown to: (i) have competitive system dynamics estimation performance compared to the globally supported basis function method, and (ii) be real-time applicable to problems with a large-scale state-space. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is a classical topic in source localization. Notably, a reliable grid-free sparse representation algorithm, called FRI (finite rate of innovation) algorithm, was proposed to recover the finite number of Dirac pulses from a stream of 1D temporal samples, which also offers a efficient solution to the DOA estimation problem. Typically, FRI method assumes uniform sampling with single snapshot. However, the actual situation is richer and more diverse. Motivated by the requests of practical applications (e.g. array deployment, algorithm run-speed, etc.), a generic FRI method is proposed to tackle the more general case in practice, i.e. non-uniform sampling with multiple snapshots. Instead of annihilating the measured sensor data, a model-fitting method is used to robustly retrieve the sparse representation (i.e. DOAs and associated amplitudes) of the 1D samples. We demonstrate that our algorithm can handle challenging DOA tasks with high-resolution, which we validate in various conditions, such as multiple coherent sources, insufficient signal snapshots, low signal-to-noise ratio (SNR), etc. Moreover, we show that the computational complexity of our algorithm mainly scales with the number of sources and varies very slowly with the number of samples and snapshots, which meets the needs of a wider range of practical applications. [ABSTRACT FROM AUTHOR]

Tensor completion aims to recover missing entries given incomplete multi-dimensional data by making use of the prior low-rank information, and has various applications because many real-world data can be modeled as low-rank tensors. Most of the existing methods are designed for noiseless or Gaussian noise scenarios, and thus they are not robust to outliers. One popular approach to resist outliers is to employ $\ell _p$ -norm. Yet nonsmoothness and nonconvexity of $\ell _p$ -norm with $0< p\leq 1$ bring challenges to optimization. In this paper, a new norm, named $\ell _{p,\epsilon }$ -norm, is devised where $\epsilon >0$ can adjust the convexity of $\ell _{p,\epsilon }$ -norm. Compared with $\ell _p$ -norm, $\ell _{p,\epsilon }$ -norm is smooth and convex even for $0< p\leq 1$ , which converts an intractable nonsmooth and nonconvex optimization problem into a much simpler convex and smooth one. Then, combining tensor ring rank and $\ell _{p,\epsilon }$ -norm, a robust tensor completion formulation is proposed, which achieves outstanding robustness. The resultant robust tensor completion problem is decomposed into a number of robust linear regression (RLR) subproblems, and two algorithms are devised to tackle RLR. The first method adopts gradient descent, which has a low computational complexity. While the second one employs alternating direction method of multipliers to yield a fast convergence rate. Numerical simulations show that the two proposed methods have better performance than those based on the $\ell _p$ -norm in RLR. Experimental results from applications of image inpainting, video restoration and target estimation demonstrate that our robust tensor completion approach outperforms state-of-the-art methods in terms of recovery accuracy. [ABSTRACT FROM AUTHOR]

Sparse Bayesian learning (SBL) has become a popular approach for direction-of-arrival (DOA) estimation, but its computational complexity for Bayesian inference is quite high because calculating inverse of a large complex matrix per iteration is required. It is known that the computational load can be reduced by transforming the complex-valued problem into a real-valued one. However, the commonly used real-valued transformation works for uniform linear arrays (ULAs) only. In this paper, we propose a new real-valued transformation for DOA estimation with arbitrary linear arrays by exploiting the virtual steering of linear arrays. Then, we introduce an alternating optimization algorithm based on the variational Bayesian inference (VBI) methodology to iteratively obtain a stationary solution to the real-valued sparse representation problem. Because of utilizing the additional real-valued structure, the VBI scheme can achieve a better performance in terms of both estimation accuracy and computational complexity. Moreover, we embed the generalized approximate message passing (GAMP) into the VBI-based method for further complexity reduction. Although there may be a performance loss for the GAMP variant, simulation results reveal its substantial performance improvement over existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient algorithm for robust sensor placement with the purpose of recovering a source signal from noisy measurements. To model uncertainty on the spatially-variant sensors gain and on the spatially correlated noise, we assume that both are realizations of Gaussian processes. Since the signal to noise ratio (SNR) is also uncertain in this context, to achieve a robust signal extraction, we propose a new placement criterion based on the maximization of the probability that the SNR exceeds a given threshold. This criterion can be easily evaluated using the Gaussian process assumption. Moreover, to reduce the computational complexity of the joint maximization of the criterion with respect to all sensor positions, we suggest a sequential maximization approach, where the sensor positions are chosen one at a time. Finally, we present numerical results showing the superior robustness of the proposed approach when compared to standard sensor placement criteria aimed at interpolating the spatial gain and to a recently proposed criterion aimed at maximizing the average SNR. [ABSTRACT FROM AUTHOR]

In this paper, we extend the bilinear generalized approximate message passing (BiG-AMP) approach, originally proposed for high-dimensional generalized bilinear regression, to the multi-layer case for the handling of cascaded problem such as matrix-factorization problem arising in relay communication among others. Assuming statistically independent matrix entries with known priors, the new algorithm called ML-BiGAMP could approximate the general sum-product loopy belief propagation (LBP) in the high-dimensional limit enjoying a substantial reduction in computational complexity. We demonstrate that, in large system limit, the asymptotic MSE performance of ML-BiGAMP could be fully characterized via a set of simple one-dimensional equations termed state evolution (SE). We establish that the asymptotic MSE predicted by ML-BiGAMP’ SE matches perfectly the exact MMSE predicted by the replica method, which is well-known to be Bayes-optimal but infeasible in practice. This consistency indicates that the ML-BiGAMP may still retain the same Bayes-optimal performance as the MMSE estimator in high-dimensional applications, although ML-BiGAMP's computational burden is far lower. As an illustrative example of the general ML-BiGAMP, we provide a detector design that could estimate the channel fading and the data symbols jointly with high precision for the two-hop amplify-and-forward relay communication systems. [ABSTRACT FROM AUTHOR]

We study robust network localization for realistic mixed line-of-sight and non-line-of-sight (LOS/NLOS) scenarios, where (i) NLOS identification is not performed, (ii) no statistical knowledge of the LOS/NLOS measurement error is available, and (iii) no experimental campaign is affordable. We treat the bias term of each range measurement, both for LOS and NLOS, as an unknown parameter. Based on this, we indicate that the ranging biases possess a sparsity property in LOS-heavy scenarios. To exploit this sparsity, we propose the inclusion of a sparsity-promoting term into the conventional cost functions, giving rise to a generic sparsity-promoting regularized formulation. By bounding the cost function, we further develop an alternative generic bound-constrained regularized formulation. To ensure global optimality, we specify the residual error function in these formulations so that they are conveniently solved via relaxation as two semidefinite programs (SDPs). It is also shown that the two SDPs can be equivalent in the sense that they share the same optimal solution. Compared with the sparsity-promoting regularized SDP, the bound-constrained regularized SDP has the advantage that it allows us to develop one data-driven strategy for selecting an appropriate regularization parameter. Numerical results, based on both synthetic- and experimental data, demonstrate the overall enhanced performance of the devised approach, both in terms of localization accuracy and computational efficiency. The remarkable ability of the proposed data-driven method for parameter selection, at the cost of a slight increase in computational complexity, is also shown. [ABSTRACT FROM AUTHOR]

The first moment and second central moments of the portfolio return, a.k.a. mean and variance, have been widely employed to assess the expected profit and risk of the portfolio. Investors pursue higher mean and lower variance when designing the portfolios. The two moments can well describe the distribution of the portfolio return when it follows the Gaussian distribution. However, the real world distribution of assets return is usually asymmetric and heavy-tailed, which is far from being a Gaussian distribution. The asymmetry and the heavy-tailedness are characterized by the third and fourth central moments, i.e., skewness and kurtosis, respectively. Higher skewness and lower kurtosis are preferred to reduce the probability of extreme losses. However, incorporating high-order moments in the portfolio design is very difficult due to their non-convexity and rapidly increasing computational cost with the dimension. In this paper, we propose a very efficient and convergence-provable algorithm framework based on the successive convex approximation (SCA) algorithm to solve high-order portfolios. The efficiency of the proposed algorithm framework is demonstrated by the numerical experiments. [ABSTRACT FROM AUTHOR]

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). This approach, allowing the base station (BS) and all users to exchange information simultaneously, can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas. [ABSTRACT FROM AUTHOR]

In this paper we consider maximum-likelihood (ML) MIMO detection under one-bit quantized observations and binary symbol constellations. This problem is motivated by the recent interest in adopting coarse quantization in massive MIMO systems—as an effective way to scale down the hardware complexity and energy consumption. Classical MIMO detection techniques consider unquantized observations, and many of them are not applicable to the one-bit MIMO case. We develop a new non-convex optimization algorithm for the one-bit ML MIMO detection problem, using a strategy called homotopy optimization. The idea is to transform the ML problem into a sequence of approximate problems, from easy (convex) to hard (close to ML), and with each problem being a gradual modification of its previous. Then, our attempt is to iteratively trace the solution path of these approximate problems. This homotopy algorithm is well suited to the application of deep unfolding, a recently popular approach for turning certain model-based algorithms into data-driven, and performance enhanced, ones. While our initial focus is on one-bit MIMO detection, the proposed technique also applies naturally to the classical unquantized MIMO detection. We performed extensive simulations and show that the proposed homotopy algorithms, both non-deep and deep, have satisfactory bit-error probability performance compared to many state-of-the-art algorithms. Also, the deep homotopy algorithm has attractively low computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel neural network architecture for MIMO symbol detection, the Recurrent Equivariant MIMO detector (RE-MIMO). It incorporates several important considerations in wireless communication systems, such as robustness to channel misspecification, the ability to handle a varying number of users with a single model, and invariance to the (sequential) order in which the users interact with the system. The decoder consists of three main blocks; one block to inform the decoder of the channel model, one permutation equivariant block based on the successful transformer architecture to model transmitter channel interactions, and one fully connected feedforward network to predict the demodulated symbols for each user. These blocks are chained together into an iterative decoder that is trained through end-to-end backpropagation. RE-MIMO is compared against a broad range of existing methods and the results confirm the ability of the network to achieve the state of the art demodulation accuracy. In particular, RE-MIMO efficiently handles a variable number of transmitters with only a single trained model, is capable of dealing with correlated channels, and is robust to channel misspecification. In terms of computational complexity, RE-MIMO scales favorably with the number of transmitters. [ABSTRACT FROM AUTHOR]

Grant-free random access is a promising protocol to support massive access in beyond fifth-generation (B5G) cellular Internet-of-Things (IoT) with sporadic traffic. Specifically, in each coherence interval, the base station (BS) performs joint activity detection and channel estimation (JADCE) before data transmission. Due to the deployment of a large-scale antennas array and the existence of a huge number of IoT devices, JADCE usually has high computational complexity and needs long pilot sequences. To solve these challenges, this paper proposes a dimension reduction method, which projects the original device state matrix to a low-dimensional space by exploiting its sparse and low-rank structure. Then, we develop an optimized design framework with a coupled full column rank constraint for JADCE to reduce the size of the search space. However, the resulting problem is non-convex and highly intractable, for which the conventional convex relaxation approaches are inapplicable. To this end, we propose a logarithmic smoothing method for the non-smoothed objective function and transform the interested matrix to a positive semidefinite matrix, followed by giving a Riemannian trust-region algorithm to solve the problem in complex field. Simulation results show that the proposed algorithm is efficient to a large-scale JADCE problem and requires shorter pilot sequences than the state-of-art algorithms which only exploit the sparsity of device state matrix. [ABSTRACT FROM AUTHOR]

In recent years, the sources localization has noticed an increase in research conducted on the problem of mixed far-field sources (FFSs) and near-field sources (NFSs). The main assumption of the existing researches is that the signals should be uncorrelated. Therefore, they cannot be used for multipath environments. The present paper provides a method called components separation algorithm (CSA) for the localization of multiple mixed FFSs and NFSs, including uncorrelated, lowly correlated and coherent signals. Firstly, by constructing one special cumulant matrix, and using a MUSIC-based technique, the noncoherent DOA vector (NDOAV) is extracted. By constructing another special cumulant matrix, and with respect to NDOAV, an estimate of the range, as well as a signal classification is obtained for noncoherent sources. Then, by estimating their kurtosis, the noncoherent component and consequently the coherent one of the second cumulant matrix is obtained. Finally, by introducing a novel approach based on squaring, projection, spatial smoothing, array interpolation transform and coherent component restoring, the parameters of coherent signals in each coherent group are estimated separately. The CSA prevents severe loss of the aperture. Furthermore, it does not require any pairing. The simulation results validate its satisfactory performance in terms of estimation accuracy, resolution, computational complexity, reasonable classification, and also its robustness against lowly correlated sources. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) algorithms with coprime arrays can provide good clutter suppression potential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, the performance of these algorithms is limited by the training samples support in practical applications. To address this issue, a robust two-stage reduced-dimension (RD) sparsity-aware STAP algorithm is proposed in this work. In the first stage, an RD virtual snapshot is constructed using all spatial channels but only $m$ adjacent Doppler channels around the target Doppler frequency to reduce the slow-time dimension of the signal. In the second stage, an RD sparse measurement modeling is formulated based on the constructed RD virtual snapshot, where the sparsity of clutter and the prior knowledge of the clutter ridge are exploited to formulate an RD overcomplete dictionary. Moreover, an orthogonal matching pursuit (OMP)-like method is proposed to recover the clutter subspace. In order to set the stopping parameter of the OMP-like method, a robust clutter rank estimation approach is developed. Compared with recently developed sparsity-aware STAP algorithms, the size of the proposed sparse representation dictionary is much smaller, resulting in low complexity. Simulation results show that the proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sample support. [ABSTRACT FROM AUTHOR]

In this paper, we develop a kernel adaptive filter for quaternion data, using stochastic information gradient (SIG) cost function based on the information theoretic learning (ITL) approach. The new algorithm (QKSIG) is useful for quaternion-based kernel applications of nonlinear filtering. Adaptive filtering in quaterion domain intrinsically incorporates component-wise real valued cross-correlation or the coupling within the dimensions of the quaternion input. We apply generalized Hamilton-real (GHR) calculus that is applicable to quaternion Hilbert space for evaluating the cost function gradient. The QKSIG algorithm minimizes Shannon's entropy of the error between the filter output and desired response and minimizes the divergence between the joint densities of input-desired and input-output pairs. The SIG technique reduces the computational complexity of the error entropy estimation. Here, ITL with SIG approach is applied to quaternion adaptive filtering for three different reasons. First, it reduces the algorithm computational complexity compared to our previous work quaternion kernel minimum error entropy algorithm (QKMEE). Second, it improves the filtering performance by considering the coupling within the dimensions of the quaternion input. Third, it performs better in biased or non-Gaussian signal and noise environments due to ITL approach. We present convergence analysis and steady-state performance analysis results of the new algorithm (QKSIG). Simulation results are used to show the behavior of the new algorithm QKSIG in quaternion non-Gaussian signal and noise environments compared to the existing ones such as quadruple real-valued kernel stochastic information gradient (KSIG) and quaternion kernel LMS (QKLMS) algorithms. [ABSTRACT FROM AUTHOR]

Many resource allocation tasks are challenging global (i.e., non-convex) optimization problems. The main issue is that the computational complexity of these problems grows exponentially in the number of variables instead of polynomially as for many convex optimization problems. However, often the non-convexity stems only from a subset of variables. Conventional global optimization frameworks like monotonic optimization or DC programming treat all variables as global variables and require complicated, problem specific decomposition approaches to exploit the convexity in some variables. To overcome this challenge, we develop an easy-to-use algorithm that inherently differentiates between convex and non-convex variables, preserving the low computational complexity in the number of convex variables. Another issue with these widely used frameworks is that they may suffer from severe numerical problems. We discuss this issue in detail and provide a clear motivating example. The solution to this problem is to replace the traditional approach of finding an $\varepsilon $ -approximate solution by the novel concept of $\varepsilon $ -essential feasibility. The underlying algorithmic approach is called successive incumbent transcending (SIT) algorithm and builds the foundation of our developed algorithm. A further highlight of this algorithm is that it inherently treats fractional objectives making the use of Dinkelbach's iterative algorithm obsolete. Numerical experiments show a speed-up of four orders of magnitude over state-of-the-art algorithms and almost three orders of magnitude of additional speed-up over Dinkelbach's algorithm for fractional programs. [ABSTRACT FROM AUTHOR]

Orthogonal greedy algorithms are popular sparse signal reconstruction algorithms. Their principle is to select atoms one by one. A series of unconstrained least-square subproblems of gradually increasing size is solved to compute the approximation coefficients, which is efficiently performed using a fast recursive update scheme. When dealing with non-negative sparse signal reconstruction, a series of non-negative least-squares subproblems have to be solved. Fast implementation becomes tricky since each subproblem does not have a closed-form solution anymore. Recently, non-negative extensions of the classical orthogonal matching pursuit and orthogonal least squares algorithms were proposed, using slow (i.e., non-recursive) or recursive but inexact implementations. In this paper, we revisit these algorithms in a unified way. We define a class of non-negative orthogonal greedy algorithms and exhibit their structural properties. We propose a fast and exact implementation based on the active-set resolution of non-negative least-squares and exploiting warm start initializations. The algorithms are assessed in terms of accuracy and computational complexity for a sparse spike deconvolution problem. We also present an application to near-infrared spectra decomposition. [ABSTRACT FROM AUTHOR]

Nonlinear signal processing is important in various applications, however it usually requires high computational cost. This paper proposes a low-complexity nonlinear adaptive least mean squares (LMS) filter based on decomposable Volterra kernels. The decomposability condition comes from a well-posed approximation problem, which imposes a rank-one structure on the full Volterra model, resulting in a system equivalent to a product of ordinary linear filters. A mean-square estimation problem is posed over such a decomposable Volterra model (DVM) and a solution via a steepest descent algorithm is introduced, subsequently motivating an adaptive implementation. The resulting DVM-LMS filter is nonlinear in the input signal as well as in its parameters, however leading to an exponential decrease in computational complexity, as compared to the full Volterra kernel. The nonlinearity in the parameters may introduce instabilities, which are tackled via normalizing strategies, introduced together with heuristics for proper initialization. Necessary conditions for stability are derived and serve as guidance to design the step-size, while simulations show that the algorithm is unbiased after convergence. Additionally, an expression for the steady-state excess mean square error is derived. Several simulations show the new filter's performance and competitiveness against celebrated methods in the literature. [ABSTRACT FROM AUTHOR]

Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. Such algorithms, however, typically rely on centralized processing at the base station (BS) which results in 1) excessive interconnect and chip input/output (I/O) data rates and 2) high computational complexity. Decentralized baseband processing (DBP) partitions the BS antenna array into independent clusters that are associated with separate radio-frequency circuits and computing fabrics in order to overcome the limitations of centralized processing. In this paper, we investigate decentralized equalization with feedforward architectures that minimize the latency bottlenecks of existing DBP solutions. We propose two distinct architectures with different interconnect and I/O bandwidth requirements that fuse the local equalization results of each cluster in a feedforward network. For both architectures, we consider maximum ratio combining, ZF, L-MMSE, and a nonlinear equalization algorithm that relies on approximate message passing. For these algorithms and architectures, we analyze the associated post-equalization signal-to-noise-and-interference-ratio. We provide reference implementation results on a multigraphics processing unit system which demonstrate that decentralized equalization with feedforward architectures enables throughputs in the Gb/s regime and incurs no or only a small performance loss compared to centralized solutions. [ABSTRACT FROM AUTHOR]

In sketched clustering, a dataset of $T$ samples is first sketched down to a vector of modest size, from which the centroids are subsequently extracted. Its advantages include 1) reduced storage complexity and 2) centroid extraction complexity independent of $T$. For the sketching methodology recently proposed by Keriven et al., which can be interpreted as a random sampling of the empirical characteristic function, we propose a sketched clustering algorithm based on approximate message passing. Numerical experiments suggest that our approach is more efficient than the state-of-the-art sketched clustering algorithm “CL-OMPR” (in both computational and sample complexity) and more efficient than k-means++ when $T$ is large. [ABSTRACT FROM AUTHOR]

In this paper, we design constant modulus probing waveforms with good correlation properties for collocated multi-input multi-output radar systems. The main content is as follows: first, we formulate the design problem as a fourth-order polynomial minimization problem with constant modulus constraints. Then, by exploiting introduced auxiliary variables and their inherent structures, the polynomial optimization model is equivalent to a non-convex consensus minimization problem. Second, a customized alternating direction method of multipliers (ADMM) algorithm is proposed to solve the non-convex problem approximately. In the algorithm, all the subproblems can be solved analytically. Moreover, all subproblems except one subproblem can be performed in parallel. Third, we prove that the customized ADMM algorithm is theoretically guaranteed convergent if proper parameters are chosen. Finally, two variant ADMM algorithms, based on stochastic block coordinate descent and accelerated gradient descent, are proposed to reduce computational complexity and speed up the convergence rate. Numerical examples show the effectiveness of the proposed consensus-ADMM algorithm and its variants. [ABSTRACT FROM AUTHOR]

We provide a rigorous account of the equivalence between the complex-valued widely linear estimation method and the quaternion involution widely linear estimation method with their vector-valued real linear estimation counterparts. This is achieved by an account of degrees of freedom and by providing matrix mappings between a complex variable and an isomorphic bivariate real vector, and a quaternion variable versus a quadri-variate real vector. Furthermore, we show that the parameters in the complex-valued linear estimation method, the complex-valued widely linear estimation method, the quaternion linear estimation method, the quaternion semi-widely linear estimation method, and the quaternion involution widely linear estimation method include distinct geometric structures imposed on complex numbers and quaternions, respectively, whereas the real-valued linear estimation methods do not exhibit any structure. This key difference explains, both in theoretical and practical terms, the advantage of estimation in division algebras (complex, quaternion) over their multivariate real vector counterparts. In addition, we discuss the computational complexities of the estimators of the hypercomplex widely linear estimation methods. [ABSTRACT FROM AUTHOR]

This paper presents a flexible array response control algorithm via oblique projection, abbreviated as FARCOP, and its application to array pattern synthesis. The proposed FARCOP algorithm stems from the adaptive array theory, and it can flexibly, precisely and simultaneously adjust the array response levels at multiple angles based on an arbitrarily given weight vector. Different from the existing approaches, the proposed FARCOP algorithm controls multi-point responses by linearly transferring the given weight vector, with a transformation matrix containing a set of parameters, each of which can be very easily determined by the desired response level (at the control angle). Owing to the fact that those parameters are independent of each other, the response levels at the control angles can be either individually or jointly and, therefore, flexibly adjusted. Since the parameter phases can be arbitrary, we take the beampattern into account and propose to uniquely choose the optimal parameters under the typical criterion of maximum white noise gain (WNG). Accordingly, a gradient projection (GP) algorithm is devised to achieve the optimal solution. Moreover, a closed-form solution is derived for the centro-symmetric array. In addition, the application of the FARCOP algorithm to pattern synthesis is discussed. Comparing to the state-of-the-art methods like multi-point accurate array response control (${\text {MA}}^2{\text {RC}}$), the proposed FARCOP algorithm controls the array responses more flexibly with lower computational complexity. Representative examples are presented to demonstrate the effectiveness and superiority of the FARCOP algorithm under various situations. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic method for extraction of useful information content from time-frequency distributions of nonstationary signals heavily corrupted by additive noise. The proposed method, which does not require prior knowledge of the signal, initially performs a one-dimensional clustering of the time-frequency distribution aimed at segmenting it into a fixed number of classes. This procedure points out basic structural differences of noise and signal components (i.e., useful information) in the time-frequency plane. In fact, noise presents a flat spectrum with large time-frequency supports, while signal components are narrow energy ridges. The time-frequency supports are estimated for each of the obtained classes and used as input for an algorithm aimed at discriminating “noise” affine classes, containing mainly non-zero coefficients located outside the components time-frequency supports, from “useful information” affine classes, containing mainly coefficients inside time-frequency supports of the signal components. Simulations show the superiority of the proposed algorithm, mainly in terms of error rate reduction of classified time-frequency coefficients, compared to hard amplitude thresholding methods, as well as to a recently proposed method for information content extraction based on adaptive thresholding, which is also outperformed in terms of computational complexity and execution time (by up to 43 and 28 times, respectively). [ABSTRACT FROM AUTHOR]

Full-duplex transmission comprises the ability to transmit and receive at the same time on the same frequency band. It allows for more efficient utilization of spectral resources, but raises the challenge of strong self-interference (SI). Cancellation of SI is generally implemented as a multi-stage approach. This paper proposes a novel adaptive SI cancellation algorithm in the digital domain based on Kalman filter theory that creates the following advances: first, the number of unknowns of the nonlinear SI model in cascade structure is significantly reduced compared to the conventional Hammerstein parallel model since it decouples the identification of linear and nonlinear elements; second, the remote signal-of-interest (SoI) is explicitly considered in the algorithm since the Kalman filter approach tunes its adaptation by the SoI power or performs successive cancellation; third, temporal variations of the SI channel are covered by a composite state-space model. In our simulation results, we analyze the performance by evaluating residual interference, system identification accuracy, and communication rate. We show that our Kalman filter solution in cascade structure delivers good performance with low computational complexity. In this configuration, the performance lines up with that of the monolithic (parallel) Kalman filter or the recursive-least squares (RLS) algorithms with parallel Hammerstein models. We implement our algorithm as a component of a practical prototype that demonstrates the benefits of a slender algorithmic design like the proposed cascade structure. [ABSTRACT FROM AUTHOR]

Filter-bank-based waveform processing has been suggested as an alternative for the plain cyclic-prefix (CP) orthogonal frequency-division multiplexing (OFDM) based schemes in fifth-generation and future wireless communication systems. This is because of the new requirements, such as asynchronous and mixed numerology scenarios supporting multi-service operation in a common framework, including enhanced mobile broadband, low-latency high-reliability communications, and low-rate machine-type communications (MTC). Nevertheless, advanced multicarrier waveforms impose significantly increased computational complexity compared to the CP-OFDM scheme. Multirate fast-convolution (FC) processing has recently been proposed as an effective implementation for advanced waveforms, such as filtered OFDM (F-OFDM) and filter-bank multicarrier (FBMC) schemes, providing extreme flexibility in the subband spectral control. In this paper, we investigate the computational complexity of FC-based waveform processing and propose two computationally efficient schemes using the idea of circular convolution decomposition. The first scheme targets at narrow bandwidth scenarios, such as MTC. The second scheme considers dense spectral use of non-overlapping subbands. Both schemes achieve significant reduction in the computational complexity compared to direct FC and polyphase filter-bank-based implementations. This reduction in the complexity is achieved without performance loss with respect to direct FC processing. Mathematical analyses are provided for both schemes, along with evaluation and comparison of the computational complexities considering F-OFDM and FBMC waveforms in long-term-evolution-like scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a regular vine copula based methodology for the fusion of correlated decisions. Regular vine copula is an extremely flexible and powerful graphical model to characterize complex dependence among multiple modalities. It can express a multivariate copula by using a cascade of bivariate copulas, the so-called pair copulas. Assuming that local detectors are single threshold binary quantizers and taking complex dependence among sensor decisions into account, we design an optimal fusion rule using a regular vine copula under the Neyman–Pearson framework. In order to reduce the computational complexity resulting from the complex dependence, we propose an efficient and computationally light regular vine copula based optimal fusion algorithm. Numerical experiments are conducted to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output multiuser network, which is applicable for users with either separate or overlapped angle-of-arrival (AoA) regions. For each user, we first introduce the angle-constraining matrix (ACM), which consists of a set of selected match-filter beamformers pointing to the AoAs of the interested user. Then, the adaptive beamformers can be acquired by appropriately designing the ADAF vectors. Such beamformer can achieve two-stage adaptive multiuser interference (MUI) suppression, i.e., inherently suppressing the MUI from non-overlapping users by ACM in the first stage and substantially mitigating the MUI from adjacent overlapping users by ADAF vectors in the second stage. For both separate and mutually overlapping users, the carrier frequency offset (CFO) estimation and subsequent data detection can be performed independently for each user, which significantly reduces computational complexity. Moreover, ADAF is rather robust to imperfect AoA knowledge and side cluster of scatterers, making itself promising for multiuser uplink transmission. We also analyze the performance of CFO estimation and obtain an approximated closed-form expression of mean-squared error. The effectiveness of the proposed method and its superiority over the existing methods are demonstrated by numerical results. [ABSTRACT FROM AUTHOR]

Time sequence averaging under dynamic time warping (DTW) is a non-trivial problem, and its exact solution requires unrealistic computational complexity in practice. The DTW barycenter averaging (DBA) method is one of the most effective iterative approximation solutions to date. However, there are still a few drawbacks in the DBA method. First, the length of the resulting average sequence depends on the selected initial average sequence; second, the discrepancy distance between the resulting average sequence and target sequence set is highly sensitive to the initialization, as we have demonstrated through the experiments described here. In this study, we propose an adaptive DBA (ADBA) algorithm to address these drawbacks. We define a scaling coefficient based on the DTW alignments such that the temporal aberrations between the average sequence and target sequence set can be qualitatively captured. The algorithm is realized by an iterative process. For each iteration, the temporary average sequence and target sequence set are partitioned into several aligned subsequence sets according to the variation in the signs of the scaling coefficients. Then, these partitioned average subsequences are adaptively compressed or stretched such that the average discrepancy distance and overall temporal aberration can be locally minimized. The comparison experiments carried out on the standard datasets illustrate that the proposed algorithm achieves lower average discrepancy distance, overall temporal aberration, and higher robustness than the available methods. Additionally, the proposed algorithm is verified by an accelerometer-based hand gesture recognition system, where ADBA produces more effective gesture templates. [ABSTRACT FROM AUTHOR]

Estimation of multiple common remainders from a sequence of erroneous residue sets is an important step for the robust generalized Chinese Remainder Theorem (CRT). This paper considers the problem of how to estimate the two common remainders from their residue sets modulo a set of moduli. To measure the errors properly under the modular operation, we introduce two type circular distances. Based on these circular distances, two estimation methods are proposed by properly grouping the erroneous remainders into two ordered clusters. Both of the two methods perform better with lower computational complexities than the existing methods. In this paper, theoretical analysis as well as analytical results for the two proposed methods are obtained. For the first method, the two optimal estimates are proved to be in a finite set with no more than $L$ candidates, where $L$ is the number of the given moduli. The second method have closed forms. Simulation results show that the two proposed methods have nearly the same performance. These optimal estimates can improve the performance of the robust generalized CRT significantly. [ABSTRACT FROM AUTHOR]

Motivated by the many applications associated with estimation of sparse multivariate models, the estimation of sparse directional connectivity between the imperfectly measured nodes of a network is studied. Node dynamics and interactions are assumed to follow a multivariate autoregressive model driven by noise, and the observations are a noisy linear combination of the underlying node activities. The corresponding maximum a posteriori (MAP) problem is derived to estimate system parameters. Due to the intractability of the MAP problem, the expectation maximization (EM) framework is used to iteratively implement the MAP estimation. To impose sparsity, the EM algorithm is augmented with an $\ell _1$ regularization of the connectivity matrix. Multiple techniques have been used to lower the computational complexity. Importantly, an efficient coordinate descent algorithm utilizing a closed-form solution is designed to solve the $\ell _1$ -regularized sub-problem of the EM. Cholesky factors of the unknown covariance matrices are used directly in the optimization process in order to impose positive definiteness and guarantee the functionality of the $\ell _1$ optimization. The algorithm is first applied to synthetic data to evaluate the estimation accuracy. A comparison with the previous work over an extensive set of configurations shows that our method is superior under moderate to high sparsity. Finally, the algorithm is applied to real temperature data obtained from 98 stations across the U.S. mainland to identify the predictive interactions between the time series. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization–maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the reconstruction of a discrete-valued random vector from possibly underdetermined linear measurements using sum-of-absolute-value (SOAV) optimization. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), is based on the idea of approximate message passing (AMP), which has been originally proposed for compressed sensing. The DAMP algorithm has low computational complexity and its performance in the large system limit can be predicted analytically via state evolution framework, where we provide a condition for the exact reconstruction with DAMP in the noise-free case. From the analysis, we also propose a method to determine the parameters of the SOAV optimization. Moreover, based on the state evolution, we provide Bayes optimal DAMP, which has the minimum mean-square-error at each iteration of the algorithm. Simulation results show that the DAMP algorithms can reconstruct the discrete-valued vector from underdetermined linear measurements and the empirical performance agrees with our theoretical results in large-scale systems. When the problem size is not large enough, the SOAV optimization with the proposed parameters can achieve better performance than the DAMP algorithms for high signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

In this paper, we provide a Rapid Orthogonal Approximate Slepian Transform (ROAST) for the discrete vector that one obtains when collecting a finite set of uniform samples from a baseband analog signal. The ROAST offers an orthogonal projection, which is an approximation to the orthogonal projection onto the leading discrete prolate spheroidal sequence (DPSS) vectors (also known as Slepian basis vectors). As such, the ROAST is guaranteed to accurately and compactly represent not only oversampled bandlimited signals but also the leading DPSS vectors themselves. Moreover, the subspace angle between the ROAST subspace and the corresponding DPSS subspace can be made arbitrarily small. The complexity of computing the representation of a signal using the ROAST is comparable to the fast Fourier tranform, which is much less than the complexity of using the DPSS basis vectors. We also give non-asymptotic results to guarantee that the proposed basis not only provides a very high degree of approximation accuracy in a mean squared error sense for bandlimited sample vectors, but also that it can provide high-quality approximations of all sampled sinusoids within the band of interest. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is a statistical tool that decomposes an observed random vector into components that are as statistically independent as possible. ICA over finite fields is a special case of ICA, in which both the observations and the decomposed components take values over a finite alphabet. This problem is also known as minimal redundancy representation or factorial coding. In this paper, we focus on linear methods for ICA over finite fields. We introduce a basic lower bound that provides a fundamental limit to the ability of any linear solution to solve this problem. Based on this bound, we present a greedy algorithm that outperforms all currently known methods. Importantly, we show that the overhead of our suggested algorithm (compared with the lower bound) typically decreases as the scale of the problem grows. In addition, we provide a sub-optimal variant of our suggested method that significantly reduces the computational complexity at a relatively small cost in performance. Finally, we discuss the universal abilities of linear transformations in decomposing random vectors, compared with existing non-linear solutions. [ABSTRACT FROM AUTHOR]

This paper presents an approximate minimum mean squared error (MMSE) adaptive beam-pattern design (ABD) method for MIMO active sensing systems. The proposed approximate MMSE ABD method leverages the physics of the MIMO arrays to provide a linear-Gaussian approximation that is specific to MIMO active sensing systems, and yields a computationally simple optimization problem. Computational complexity analysis confirms this theoretical reduction in the number of floating-point operations required, most notably that evaluation of the proposed approximate optimization cost function grows polynomially with the number of targets being tracked, whereas for evaluation of the exact cost the growth is exponential. Additionally, numerical results indicate that, even for a simple scenario with a single target being tracked, the proposed approximate MMSE ABD method does indeed reduce the mean squared error of target parameter estimation compared to the nonadaptive case, with a reduction in computation time of four orders of magnitude compared to exact MMSE ABD. [ABSTRACT FROM AUTHOR]

An algorithm for the estimation of multiple targets from partial and corrupted observations is introduced based on the concept of a partially distinguishable multitarget system. It combines the advantages of engineering solutions like multiple hypothesis tracking with the rigor of point-process-based methods. It is demonstrated that under intuitive assumptions and approximations, the complexity of the proposed multitarget estimation algorithm can be made linear in terms of the number of tracks and the number of observations, while naturally preserving distinct tracks for detected targets, unlike point-process-based methods. [ABSTRACT FROM AUTHOR]

In this paper, a framework of beamspace channel estimation in millimeter wave massive MIMO system is proposed. The framework includes the design of hybrid precoding and combining matrix as well as the search method for the largest entry of over-sampled beamspace receiving matrix. Then based on the framework, three channel estimation schemes including identity matrix approximation (IA) based scheme, scattered zero off-diagonal (SZO) based scheme and concentrated zero off-diagonal (CZO) based scheme are proposed. These schemes together with the existing channel estimation schemes are compared in terms of computational complexity, estimation error and total time slots for channel training. Simulation results show that the proposed schemes outperform the existing schemes and can approach the performance of the ideal case. In particular, total time slots for channel training can be substantially reduced. [ABSTRACT FROM AUTHOR]

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, and multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested. [ABSTRACT FROM AUTHOR]

Products of time-series signals have found wide-spread application in many fields of signal processing. For example, they are often used in modeling and compensating distortions generated by analog and mixed-signal components. Without excess bandwidth, the products of time-series signals will produce aliased artifacts that do not represent the physical phenomenology of the components being modeled. To ameliorate the effects of these potentially unwanted, aliased distortion products, we compare two multidimensional filters: the first is a simple one-dimensional (1-D) polyphase filter that is used to generate excess bandwidth; the second is a multidimensional (M-D) filter that is convolved with the time-series product. We demonstrate that the 1-D polyphase filter, although computationally simple, still produces unwanted aliasing when applied to signals near Nyquist rate, which can adversely affect modeling and/or compensation performance. By contrast, the M-D filter is computationally expensive, but is capable of most closely matching the ideal antialias filter response. [ABSTRACT FROM AUTHOR]