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Recently developed monitoring-systems show the need as also the possibilities for online- and offline-onsite-diagnostics. The limit in sensitivity for electrical pd-measurements according to IEC 60270 which is reached using filters for sinusoidal disturbances and compensation for corona impulses is still not satisfactory. The acoustic partial discharge measurement (pd-measurement) is also a well known and reliable method often used by transformer manufacturers in testing. In laboratory setups a gain in sensitivity of the acoustic in comparison to the electrical pd measurements could be established. This is reached by adapting the sensors to the propagation path, a modern but moderately priced acquisition hardware and the introduction of new signal processing algorithms. The developed sophisticated measuring, systems show remarkable results in online acoustic pd-measurements onsite. [ABSTRACT FROM AUTHOR]

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters. [ABSTRACT FROM AUTHOR]

Common spatial pattern (CSP) is a classic method commonly used in multichannel electroencephalogram (EEG) signal processing, which aims to extract effective features for binary classification by solving spatial filters that maximize the ratio of filtered dispersion between two classes. The aim of this paper is to improve the performance of the conventional CSP method, which will be badly influenced by noises. The recently proposed quantized minimum error entropy (QMEE) criterion is applied to structure a new objective function instead of the ${L} _{2}$ -norm in the conventional CSP. Quantization is utilized to reduce the computational complexity. The new objective function is optimized by a gradient-based iterative algorithm. The desirable performance of the QMEE-based CSP method, namely CSP-QMEE, is demonstrated with a toy example and two real EEG datasets, including Dataset IIb of the brain–computer interfaces (BCIs) Competition IV (three channels) and Dataset IIIa of the BCI Competition III (60 channels). The new method can achieve satisfactory performance compared to existing methods on all datasets. The promising results in this paper suggest that the CSP-QMEE may become a powerful tool for BCIs. [ABSTRACT FROM AUTHOR]

Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design. [ABSTRACT FROM AUTHOR]

–This paper proposes an efficient and optimal reduced control set model predictive flux control (RCS-MPFC) for a three-level neutral-point-clamped voltage source inverter (3L-NPC VSI) fed induction motor. The proposed algorithm reduces the computational time in the prediction stage without causing any suboptimality. The optimal voltage vector selected by the proposed method produces the same cost function value as that of the conventional FCS-MPFC which requires enumerating all 27 voltage vectors. Therefore, the proposed algorithm achieves the same performance as the conventional method in the entire range of operation of IM drives while the computational effort is significantly reduced. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the existing RCS-MPFC scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the policy evaluation problem in reinforcement learning with agents on a decentralized and directed network. In order to evaluate the quality of a fixed policy in this decentralized setting, one option is for agents to run decentralized temporal-difference (TD) collaboratively. To account for the practical scenarios where the state and action spaces are large and malicious attacks emerge, we focus on the decentralized TD learning with linear function approximation in the presence of malicious agents (often termed as Byzantine agents). We propose a trimmed mean-based Byzantine-resilient decentralized TD algorithm to perform policy evaluation in this setting. We establish the finite-time convergence rate, as well as the asymptotic learning error that depends on the number of Byzantine agents. Numerical experiments corroborate the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large. [ABSTRACT FROM AUTHOR]

Line spectral estimation is a classical signal processing problem that aims to estimate the line spectra from their signal which is contaminated by deterministic or random noise. Despite a large body of research on this subject, the theoretical understanding of this problem is still elusive. In this paper, we introduce and quantitatively characterize the two resolution limits for the line spectral estimation problem under deterministic noise: one is the minimum separation distance between the line spectra that is required for exact detection of their number, and the other is the minimum separation distance between the line spectra that is required for a stable recovery of their supports. The quantitative results imply a phase transition phenomenon in each of the two recovery problems, and also the subtle difference between the two. We further propose a sweeping singular-value-thresholding algorithm for the number detection problem and conduct numerical experiments. The numerical results confirm the phase transition phenomenon in the number detection problem. [ABSTRACT FROM AUTHOR]

Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique–Deep Belief Network (DBN) – to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at 10−3 Bit Error Rate (BER). [ABSTRACT FROM AUTHOR]

Recently, maximum correntropy criterion (MCC) has been widely and successfully used in robust signal processing and machine learning, in which the correntropy is maximized instead of minimizing the popular mean square error (MSE) to improve the robustness with respect to outliers or impulsive noises. A lot of efforts have been devoted to derive different adaptive algorithms under MCC, but to date, little insight has been gained as to how the MCC solution will be influenced by outliers. In this paper, we investigate this problem and our focus is mainly on the parameter estimation of a simple linear errors-in-variables (EIVs) model with scalar variables. Under some conditions, we derive an upper bound on the absolute value of the estimation error and show that the MCC solution can get very close to the true value of the unknown parameter even with arbitrarily large outliers in both the input and output variables. Illustrative examples are provided to verify and clarify the theory. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of maximizing the minimum power gain in a wide main lobe for an array antenna. To obtain the desired power gain pattern, current literatures solve the problem known as shaped beam pattern synthesis (SBPS) by setting the beam pattern expression as the cost function. The power gain in a certain direction of the array antenna is comprehensively decided by the array radiated power density and the total radiated power. Both these two parameters are affected by the selection of array elements’ weights. However, the SBPS-based methods do not take the total radiated power into account. Therefore, the SBPS-based methods cannot obtain the power gain pattern directly, and the optimal power gain pattern, consequently, cannot be guaranteed. To overcome this deficiency, this paper maximizes the minimum power gain in the desired wide main lobe by directly setting the power gain expression as the cost function. Aiming at optimizing the cost function, this paper first sets up a power gain optimization problem which has concave form. Termed this optimization problem as the power gain pattern synthesis (PGPS) problem. The concave PGPS problem is then converted to a series of convex subproblems and an iterative algorithm is proposed to solve it efficiently. Solution to the PGPS problem is proven to be able to achieve higher minimum power gain in a desired wide main lobe than the results from the SBPS-based algorithms. Simulations are carried out with both linear array antenna and planar array antenna. Comparisons with the existed SBPS-based algorithms validate the effectiveness and advantages of the proposed PGPS-based algorithm. In addition, further improvement on suppressing the sidelobe level for the PGPS problem is discussed. [ABSTRACT FROM AUTHOR]

Optimized and custom arithmetic circuits are widely used in embedded systems such as multimedia applications, cryptography systems, signal processing and console games. Debugging of arithmetic circuits is a challenge due to increasing complexity coupled with non-standard implementations. Existing algebraic rewriting techniques produce a remainder to indicate the presence of a potential bug. However, bug localization remains a major bottleneck. Simulation-based validation using random or constrained-random tests are not effective for complex arithmetic circuits due to bit-blasting. In this paper, we present an automated test generation and bug localization technique for debugging arithmetic circuits. This paper makes four important contributions. We propose an automated approach for generating directed tests by suitable assignments of input variables to make the remainder non-zero. The generated tests are guaranteed to activate bugs. We also propose an automatic bug fixing technique by utilizing the patterns of the remainder terms as well as by analyzing the regions activated by the generated tests to detect and correct the error(s). We also propose an efficient debugging algorithm that can handle multiple dependent as well as independent bugs. Finally, our proposed framework, consisting of directed test generation, bug localization and bug correction, is fully automated. In other words, our framework is capable of producing a corrected implementation of arithmetic circuits without any manual intervention. Our experimental results demonstrate that the proposed approach can be used for automated debugging of large and complex arithmetic circuits. [ABSTRACT FROM AUTHOR]

Timing skews among channels degrade seriously the time-interleaved analog-to-digital converter (TIADC) performance, which can be improved by the blind timing skew estimation (TSE) technique. In this paper, we proposed the all-phase fast Fourier transform (ApFFT) based on spectrum sparsity signal phase relationship blind TSE (ApFFT-SSPR-BLTSE) algorithm. The ApFFT-SSPR-BLTSE algorithm reduces computational complexity based on the phase relationship of the total output from TIADC and the corresponding reference channel output compared with the existing spectrum sparsity blind TSE (SS-BLTSE) algorithm. We also utilized the ApFFT technique to increase the accuracy of phase spectral estimation. Simulation results show that the proposed ApFFT-SSPR-BLTSE algorithm, which as a reduced number of fast Fourier transforms (FFTs) and low hardware complexity, has higher accuracy for blind TSE compared to the existing SS-BLTSE algorithm. In addition, this paper presents an efficient hardware architecture of the ApFFT-SSPR-BLTSE algorithm on the Xilinx Virtex-6 vlx550tff1759 field-programmable gate array (FPGA) chip for the blind TSE of the four-channel 400-MHz 14-bit TIADC real system. The validation results show that the proposed algorithm uses only a few percent of the hardware resources of the FPGA chip, and the mismatch spurs were suppressed to better than −81.54 dB. [ABSTRACT FROM AUTHOR]

Two-dimensional magnetic recording (TDMR) is a promising technology for boosting areal densities (ADs) using sophisticated signal processing algorithms within a systems framework. The read/write channel architectures have to effectively tackle 2-D inter-symbol interference (ISI), 2-D synchronization errors, media and electronic noise sources, as well as thermal asperities resulting in burst erasures. The 1-D low-density parity check (LDPC) codes are well studied to correct large 1-D burst errors/erasures. However, such 1-D LDPC codes are not suitable for correcting 2-D burst errors/erasures due to the 2-D span of errors. In this paper, we propose construction of a native 2-D LDPC code to effectively correct 2-D burst erasures. We also propose a joint detection and decoding engine based on the generalized belief propagation algorithm to simultaneously handle 2-D ISI, as well as correct bit/burst errors for TDMR channels. This paper is novel in two aspects: 1) we propose the construction of native 2-D LDPC codes to correct large 2-D burst erasures and 2) we develop a 2-D joint signal detection-decoder engine that incorporates 2-D ISI constraints, and modulation code constrains along with LDPC decoding. The native 2-D LDPC code can correct >20% more burst erasures compared with the 1-D LDPC code over a 128 $\times $ 256 2-D page of detected bits. Also, the proposed algorithm is observed to achieve a signal-to-noise ratio gain of >0.5 dB in bit error rate performance (translating to 10% increase in ADs around the 1.8 Tb/in2 regime with grain sizes of 9 nm) as compared with a decoupled detector-decoder system configuration over a small 2-D LDPC code of size 16 $\times $ 16. The efficacy of our proposed algorithm and system architecture is evaluated by assessing AD gains via simulations for a TDMR configuration comprising of a 2-D generalized partial response over the Voronoi media model assuming perfect 2-D synchronization. [ABSTRACT FROM PUBLISHER]

In this paper, a novel augmented complex-valued least-mean kurtosis (ACLMK) algorithm is proposed for processing complex-valued signals. The negated kurtosis of the complex-valued error signal is defined as a cost function by using augmented statistics. As a result of the minimization of this cost function, the ACLMK algorithm containing all second-order statistical properties is obtained for processing noncircular complex-valued signals. Moreover, in this paper, convergence and misadjustment conditions of the proposed ACLMK algorithm are derived from the steady-state analysis. The simulation results on complex-valued system identification, prediction, and adaptive noise cancelling problems show that the use of the cost function defined by the negated kurtosis of the complex-valued error signal based on augmented statistics enables the processing of the noncircular complex-valued signals, and significantly improves the performance of the proposed ACLMK algorithm in terms of the mean square deviation, the mean square error, the prediction gain and the convergence rate when compared to other algorithms. [ABSTRACT FROM AUTHOR]

The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multitarget tracking problem due to their low complexity and ability to estimate the number and states of targets in cluttered environments. The PHD filter propagates the first-order moment (i.e. mean) of the number of targets while the CPHD propagates the cardinality distribution in the number of targets, albeit for a greater computational cost. Introducing the Panjer point process, this paper proposes a Second-Order PHD (SO-PHD) filter, propagating the second-order moment (i.e., variance) of the number of targets alongside its mean. The resulting algorithm is more versatile in the modeling choices than the PHD filter, and its computational cost is significantly lower compared to the CPHD filter. This paper compares the three filters in statistical simulations which demonstrate that the proposed filter reacts more quickly to changes in the number of targets, i.e., target births and target deaths, than the CPHD filter. In addition, a new statistic for multiobject filters is introduced in order to study the correlation between the estimated number of targets in different regions of the state space, and propose a quantitative analysis of the spooky effect for the three filters. [ABSTRACT FROM PUBLISHER]

This paper addresses the estimation and compensation of I/Q imbalance, one of the most prominent impairments found in wideband zero-intermediate frequency transceivers (TRxs). The I/Q imbalance encountered in this kind of TRx comprises not only frequency-selective gain and phase imbalance but also delay imbalance. Unless appropriate compensation is applied, the I/Q imbalance significantly degrades the performance of a communication system. This paper presents a novel compensation technique for transmitter I/Q imbalance based on built-in-self-calibration, a low cost and robust compensation technique that enables manufacturing as well as in-field calibration with low computational complexity. The method’s performance is evaluated in a TRx with 64-quadratic-amplitude modulation and 2 GHz of bandwidth implemented with real hardware. The measurements show that the proposed technique achieves an image rejection ratio greater than 35 dB in the entire 2 GHz bandwidth and an error vector magnitude lower than 3%. [ABSTRACT FROM PUBLISHER]

In this paper, weighted sum energy efficiency (WSEE) in uplink and downlink of a multi-user non-orthogonal multiple access (NOMA) system is considered. we adopt a more realistic power consumption model where signal processing power is modeled as a linear function of transmit power. Rather than the well-known network-centric global energy efficiency (GEE) metric, which is a pseudo-concave (PC) function, the weighted sum energy efficiency metric is considered, which is not PC in general. To find the optimum user power allocation Dinkelbach-like algorithm is adopted, by which each individual fractional EE function is converted to a parametric function where under some conditions on the weights falls into a class of convex optimization problems and it is solved in the dual domain. The dual variables are updated using a sub-gradient and cutting plane-based algorithm, which here ellipsoid method is used. Since the optimum solution restricts user weights, a low complexity suboptimum algorithm that does not consider any condition on user's weights is proposed. The problem is non-convex in general; hence, epigraph form followed by successive convex approximation (SCA) is used to deal with that problem. Results demonstrate that with the user-oriented metric, one can provide different priorities to users, and by choosing proper weights entail fairness among users. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, the user-centric networks with the baseband unit pool have attracted a great deal of attentions in academic and industrial fields. However, limited fronthaul capacity and computation resource have become the bottlenecks inevitably. Thus, this paper investigates the power-efficient transmission in user-centric networks by considering both fronthaul capacity and computation resource constraints, where multiple access points (APs) and user equipments (UEs) are distributed. Specifically, a joint optimization of the beamforming vectors, AP-UE association strategy and transmission time is proposed to minimize the total power consumption (TPC). The formulated mixed integer non-linear problem (MINLP) is NP-hard. To address this problem, the MINLP is first transformed into a convex one via the successive convex approximation and semidefinite relaxation methods. Then, an iterative but effective algorithm is designed by using the property of the solution and applying the Lagrangian dual method. Simulation results show that the proposed algorithm converges rapidly and outperforms benchmark algorithms in terms of TPC. [ABSTRACT FROM AUTHOR]

In this paper, the problem that the polyphase signal may not satisfy the orthogonality requirement in the condition of limited bandwidth is investigated. The use of waveforms that lack orthogonality leads to possible harmful effects in multiple-input and multiple-output (MIMO) radar and distributed coherent aperture radar (DCAR). For the purpose of enhancing the orthogonality of the signal, a multi-pulse joint transmission strategy of polyphase signal and the corresponding echo signal processing algorithm based on sliding window are proposed. We further analyze the relationship between the degradation of detection performance and the step size of the sliding window. In addition, the Doppler tolerance of the signal is also derived. Finally, the enhancement of the orthogonality by using the multi-pulse joint processing strategy and the relationship between Doppler tolerance and coherent time are verified via numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid beamforming design algorithm in multi-user massive multiple-input-multiple-output millimeter-wave channels. In the proposed structure, we employ variable radio frequency attenuators at each phase shifter which flexibly controls the transmit signal amplitude to achieve the performance of fully digital (FD) precoders. We formulate a power loss minimization problem which can be easily solved via the alternating optimization process. Simulation results demonstrate that the proposed architecture achieves the same performance as the FD scheme with the reduced number of radio frequency chains and the proposed scheme exhibits a 37.4% power gain over conventional methods. [ABSTRACT FROM AUTHOR]

Online sensing of sheet modulus of elasticity in the dry-end spans of printing grade paper machines (PMs) will permit the machine operators to control the mechanical properties of the produced paper. This paper develops an integrated sensorless tension control and sheet modulus of elasticity estimation algorithm, which is tested using field-recorded signals from a production PM and using a full model of the dry-end sections of this machine. Results show the ability of the proposed estimation algorithm to predict the sheet tension (tensile force) and sheet modulus of elasticity even in the presence of rapid changes in the speed and/or in other process operating conditions. The developed algorithms offer the papermaker new insights into the papermaking process. The ability to understand how modulus changes in real time provides a pathway to optimizing the operation of the PM drive system. [ABSTRACT FROM PUBLISHER]

Recovering low-rank and sparse components from missing observations is an essential problem in various fields. In this paper, we have proposed a method to address the missing low-rank and sparse decomposition problem. We have used the smoothed nuclear norm and the $L_{1}$ norm to impose the low-rankness and sparsity constraints on the components, respectively. Furthermore, we have suggested a linear modeling for the corrupted observations. The problem has been solved with the aid of alternating minimization. Moreover, some simplifications have been applied to the relations to reduce the computational complexity, which makes the algorithm suitable for large-scale problems. To evaluate the proposed method, different simulation scenarios have been devised. The superiority of the suggested scheme over its counterparts has been confirmed on both the recovery accuracy and the convergence speed in various applications. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we study the reconfigurable intelligent surface (RIS) based downlink multi-user system where a multi-antenna base station (BS) sends signals to various users assisted by the RIS reflecting the incident signals of the BS towards the users. Unlike most existing works, we consider the practical case where only the large-scale fading gain is required at the BS and a limited number of phase shifts can be realized by the finite-sized RIS. To maximize the sum rate, we propose a hybrid beamforming scheme where the continuous digital beamforming and discrete RIS-based analog beamforming are performed at the BS and the RIS, respectively. An iterative algorithm is designed for beamforming and theoretical analysis is provided to evaluate how the size of RIS influences the achievable rate. Simulation results show that the RIS-based system can achieve a good sum-rate performance by setting a reasonable size of RIS and a small number of discrete phase shifts. [ABSTRACT FROM AUTHOR]

This paper investigates joint secure beamforming design and receive antenna selection problem for large-scale multiple-input-multiple-output (MIMO) wiretap channels. A Branch-And-Bound (BAB) based algorithm is utilized to search the optimal receive antenna subset under both perfect and imperfect channel state information (CSI) scenarios. First, for the perfect CSI scenario, we formulate the secrecy rate optimization problem, and an equivalent monotonically decreasing objective function is established to make the problem suitable for efficient BAB search. In each search, we propose using the weighted mean squared error minimization (WMMSE) method to tackle the optimization problem. Then, we generalize the framework to the imperfect CSI case, and the robust problem is designed to maximize the worst-case secrecy rate, the problem is challenging to solve due to the channel uncertainties. A cutting-set method is devised to solve this non-convex problem by updating the worst-case secrecy rate and channel uncertainties alternately. To reduce the design complexity, we also present an antenna selection based on the alternating optimization (AO) method. The validity of the proposed antenna selection algorithms is evaluated through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics. [ABSTRACT FROM AUTHOR]

We propose a novel adaptive kernel-based regression method for complex-valued signals: the generalized complex-valued kernel least-mean-square (gCKLMS). We borrow from the new results on widely linear reproducing kernel Hilbert space (WL-RKHS) for nonlinear regression and complex-valued signals, recently proposed by the authors. This paper shows that in the adaptive version of the kernel regression for complex-valued signals we need to include another kernel term, the so-called pseudo-kernel. This new solution is endowed with better representation capabilities in complex-valued fields since it can efficiently decouple the learning of the real and the imaginary part. Also, we review previous realizations of the complex KLMS algorithm and its augmented version to prove that they can be rewritten as particular cases of the gCKLMS. Furthermore, important conclusions on the design of the kernels are drawn that help to greatly improve the convergence of the algorithms. In the experiments, we revisit the nonlinear channel equalization problem to highlight the better convergence of the gCKLMS compared to previous solutions. Also, the flexibility of the proposed generalized approach is tested in a second experiment with non-independent real and imaginary parts. The results illustrate the significant performance improvements of the gCKLMS approach when the complex-valued signals have different properties for the real and imaginary parts. [ABSTRACT FROM AUTHOR]

Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. As a benchmark for these NC methods, we derive the closed-form deterministic $R$ -D NC Cramér-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources in this paper. Assuming a separable centro-symmetric $R$-D array, we show that in some special cases, the deterministic $R$-D NC CRB reduces to the existing deterministic $R$-D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved under the deterministic data assumption in these cases. For more general scenarios, finding an analytical expression of the NC gain for an arbitrary number of sources is very challenging. Thus, in this paper, we simplify the derived NC CRB and the existing CRB for the special case of two closely-spaced strictly non-circular sources captured by a uniform linear array (ULA). Subsequently, we use these simplified CRB expressions to analytically compute the maximum achievable asymptotic NC gain for the considered two source case. The resulting expression only depends on the various physical parameters and we find the conditions that provide the largest NC gain. Our analysis is supported by extensive simulation results. [ABSTRACT FROM PUBLISHER]

The real-time analysis of power quality (PQ) disturbances and islanding detection for distributed generation (DG) system using signal processing algorithms are still at a premature stage. Quick and reliable detection of islanding and PQ disturbances needs renewed focus. This paper is concentrated on the implementation of advanced signal processing techniques in field-programmable gate array (FPGA) to identify PQ and islanding events in real time. The proposed scheme is based on orthogonal empirical mode decomposition (OEMD) implemented in FPGA. This technique does not suffer from the loss of signal characteristics with successive decomposition levels. The performance of said technique is demonstrated for different PQ and islanding events simulated on IEEE 13 bus DG system and experiments conducted on a laboratory setup. The detection ability has been compared with discrete wavelet transform (DWT) and basic empirical mode decomposition (EMD). It is found that DWT completely fails for the signal with noise content, while the performance of basic EMD deteriorates with an increased level of noise content. On the other hand, OEMD reliably and accurately detects the disturbances conducted in the study, both simulated and experimental. [ABSTRACT FROM AUTHOR]

We deal with the classical problem of testing two simple statistical hypotheses but, as a new element, it is assumed that the data vector is observed after an unknown permutation of its entries. What is the fundamental limit for the detection performance in this case? How much information for detection is contained in the entry values and how much in their positions? In the first part of this paper, we answer these questions. In the second part, we focus on practical algorithms. A low-complexity detector solves the detection problem without attempting to estimate the permutation. A modified version of the auction algorithm is then considered, and two greedy algorithms with affordable worst case complexity are presented. The detection operational characteristics of these detectors are investigated by computer experiments. The problem we address is referred to as unlabeled detection and is motivated by large sensor network applications, but applications are also foreseen in different fields, including image processing, social sensing, genome research, and molecular communication. [ABSTRACT FROM AUTHOR]

In cellular heterogeneous networks (HetNets), a number of distributed base stations cooperatively provide services to multiple mobile users. This paper addresses joint base-station activation and coordinated beamforming design for downlink transmission in HetNets. To this end, a mixed integer program is formulated to optimize the total power consumption of a HetNet. A novel approach based on Benders’ decomposition is then put forth to obtain the optimal solution for the problem of interest with guaranteed convergence. Building on our new formulation, a dual-subgradient algorithm is also proposed to find an approximate solution in polynomial time. The proposed approaches can be generalized to more general setups, including robust beamforming designs under channel uncertainty, and coordinated beamforming for multi-cell scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a processing framework to separate moving targets from the clutter, under multichannel synthetic aperture radar (SAR) scenarios, and addresses the moving target imaging and velocity estimation problems for ground moving target indication (GMTI) applications. A practical implementation is introduced to break the SAR/GMTI problem into two processing stages, and the sparsity of the moving targets in the observed scene is exploited throughout the stages. The two-stage process extracts the moving targets from the monitored region via a sparsity-based iterative decomposition algorithm and subsequently estimates the complete velocity vectors of moving targets by enforcing sparsity constraints. The model is sufficiently versatile to incorporate digital elevation map information, which further improves the moving target relocation accuracy. The effectiveness of the presented framework is demonstrated using the Air Force Research Laboratory Gotcha GMTI challenge data. [ABSTRACT FROM AUTHOR]

The detection of groups of individuals is attracting the attention of many researchers in diverse fields, from automated surveillance to human-computer interaction, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures inherited from the people detection field, other from clustering, other designed specifically for a particular approach, thus lacking in generalization and making the comparisons between different approaches hard to be carried out. Moreover, most of the existent metrics are scarcely expressive, addressing groups as they are atomic entities, ignoring that they may have different cardinalities, and that group detection approaches may fail in capturing the exact number of individuals that compose it. This paper fills this gap presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting, or of better dealing with specific group cardinalities. The GRODE metrics have been evaluated first on controlled scenarios, where the differences with alternative metrics are evident. Then, the metrics have been applied to eight approaches of group detection, on eight public datasets, providing a fresh-new panorama of the state-of-the-art, discovering interesting strengths and pitfalls of the recent approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new constellation design for a non-coherent massive single input multiple output (m-SIMO) uplink system based on M-DPSK suitable for Rician channels. Under Rician propagation, the non-coherent systems proposed until now cannot completely remove the interference. This new design takes into account the inter user and inter symbol interferences in order to convert them into useful terms that help us in the non-coherent detection. We propose an algorithm to non coherently detect the joint symbol with the information from all users leveraging the characteristics of the new constellation. In addition, the signal-to-interference-plus-noise ratio is derived and is used to present analytical bounds for the symbol error rate. It is shown that the analytical results tightly match the numerical simulation. We show that with the proposed constellation and detection algorithm we are able to leverage the line-of-sight component of the Rician propagation, obtaining a better performance than with Rayleigh channels. [ABSTRACT FROM AUTHOR]

This paper proposes a widely linear processing framework for multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) downlink systems. In the framework, a widely linear MIMO-NOMA (WL-MIMO-NOMA) model is derived by assuming that the base station transmits real-valued downlink signals. WL-MIMO-NOMA adopts complex-valued power allocation coefficients to stagger the user signals in phase. The main features of WL-MIMO-NOMA are the following: first, in general case, WL-MIMO-NOMA can remove all the inter-cluster interference and at least half of the intra-cluster interference; and second, in user pairing case, both interferences can be completely eliminated. This is distinct from the existing work where real power coefficients are used, which cannot guarantee the complete separation of the paired user signals because the signals transmitted to the paired users are overlapped in phase. In addition, the closed-form expressions of outage probabilities are derived. The phase difference of the complex power coefficients is optimized to minimize the outage probability. It is proven that, with the optimal phase difference, successive interference cancellation is unnecessary in user pairing case. Finally, the framework is extended to the mixed case of real/complex circular signals. Simulation results show that the proposed framework outperforms the existing work, and the numerical results agree well with the analytical analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for impulsive source separation (deinterleaving) in multi-input single-output systems based purely on impulse spacing. The impulse spacing for each source is modeled as a renewal process. To classify sources, an algorithm is developed to maximize the likelihood of impulse assignment based on the impulse spacing distributions. A theoretical lower bound on performance is derived and the algorithm is shown to get reasonably close to the bound. The algorithm is extended for use when the impulse spacing distribution parameters are unknown. Performance is evaluated on multiple application examples. [ABSTRACT FROM AUTHOR]

Next generation wireless networks will exploit the large amount of spectrum available at millimeter wave (mmWave) frequencies. The design of mmWave systems, however, is challenging due to the strict power, cost, and hardware constraints at higher bandwidths. To achieve a good SNR for communication, mmWave systems use large antenna arrays. Beamforming with highly directional beams is one way to use the antennas. As the channel changes over time, the beams that maximize the SNR have to be estimated quickly to reduce the training overhead. Prior research has exploited the observation that mmWave channels are sparse to perform compressed sensing (CS) based beam alignment with few channel measurements. Most of the existing CS-based algorithms, however, assume perfect synchronization and fail in the presence of carrier frequency offset (CFO). This paper presents Swift-Link, a fast beam alignment algorithm that is robust against the offset. Swift-Link includes a novel randomized beam training sequence that minimizes the beam alignment errors due to CFO and a low-complexity algorithm that corrects these errors. Even with strict hardware constraints, our algorithm uses fewer channel measurements than comparable CS algorithms and has analytical guarantees. Swift-Link requires a small output dynamic range at the analog-to-digital converter compared to beam-scanning techniques. [ABSTRACT FROM AUTHOR]

Direct geolocation of radio emitters has traditionally been formulated using short measurement intervals over which delay and Doppler frequency shift are approximated as constant. In this paper, we consider maximum likelihood position estimation using long measurement intervals for which the Doppler is time varying. A series of assumptions and approximations are used to develop an efficient solution involving coherent combining of traditional, complex ambiguity function processing outputs. Simulation results are used to demonstrate potential gains. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to demonstrate that a new algorithm for estimating arterial oxygen saturation can be effective even with data corrupted by motion artifacts (MAs). Methods: OxiMA, an algorithm based on the time-frequency components of a photoplethysmogram (PPG), was evaluated using 22-min datasets recorded from 10 subjects during voluntarily-induced hypoxia, with and without subject-induced MAs. A Nellcor OxiMax transmission sensor was used to collect an analog PPG while reference oxygen saturation and pulse rate (PR) were collected simultaneously from an FDA-approved Masimo SET Radical RDS-1 pulse oximeter. Results: The performance of our approach was determined by computing the mean relative error between the PR/oxygen saturation estimated by OxiMA and the reference Masimo oximeter. The average estimation error using OxiMA was 3 beats/min for PR and 3.24% for oxygen saturation, respectively. Conclusion: The results show that OxiMA has great potential for improving the accuracy of PR and oxygen saturation estimation during MAs. Significance: This is the first study to demonstrate the feasibility of a reconstruction algorithm to improve oxygen saturation estimates on a dataset with MAs and concomitant hypoxia. [ABSTRACT FROM AUTHOR]

Multifractal analysis has become a powerful signal processing tool that characterizes signals or images via the fluctuations of their pointwise regularity, quantified theoretically by the so-called multifractal spectrum. The practical estimation of the multifractal spectrum fundamentally relies on exploiting the scale dependence of statistical properties of appropriate multiscale quantities, such as wavelet leaders, that can be robustly computed from discrete data. Despite successes of multifractal analysis in various real-world applications, current estimation procedures remain essentially limited to providing concave upper-bound estimates, while there is a priori no reason for the multifractal spectrum to be a concave function. This paper addresses this severe practical limitation and proposes a novel formalism for multifractal analysis that enables nonconcave multifractal spectra to be estimated in a stable way. The key contributions reside in the development and theoretical study of a generalized multifractal formalism to assess the multiscale statistics of wavelet leaders, and in devising a practical algorithm that permits this formalism to be applied to real-world data, allowing for the estimation of nonconcave multifractal spectra. Numerical experiments are conducted on several synthetic multifractal processes as well as on a real-world remote-sensing image and demonstrate the benefits of the proposed multifractal formalism over the state of the art. [ABSTRACT FROM AUTHOR]

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our simulation results on both synthetic and real-world datasets show that our strategies achieve a desired accuracy in inferring the underlying structure while spending a small budget on communication. [ABSTRACT FROM AUTHOR]

In this paper, we describe the idea and practical realization of an automated calibrator of fiber-optic UTC(k) distribution system, along with a high-resolution shifter of 1 PPS signal, which allows us to cancel the propagation delay and, thus, to produce the 1 PPS at the remote system output with practically zero offset. The solution was experimentally verified with 10 different optical paths, up to 300 km long. The rms offset of the output timescale in our experiments was 7.1 ps, and maximum absolute value did not exceed 15 ps. [ABSTRACT FROM AUTHOR]

Convolution is widely used in scientific computational fields such as digital image processing and machine learning. However, these applications are difficult to execute in realtime because they are computationally intensive. This paper introduces a high-speed convolution solution that runs on our self-developed multicore digital signal processor (DSP). To optimize the convolution capability, we propose a convolution instruction and a convolution micro architecture in the design of a subcore. As a coprocessor, the designed subcore is integrated into a network-on-chip (NoC)-based multicore DSP. In the implementation of the multicore parallel convolution, an independent convolution task-partitioning and mapping scheme is proposed. Datablock storage and software prefetching mechanisms are used to hide the data transmission time during the calculation, improving the computing efficiency. We also develop a data reuse strategy that effectively reduces the data bandwidth requirements of multicore parallel convolution. The proposed methods are applied to correlation-based template matching, with the results showing that our convolution computing approach greatly improves the performance compared with the same operations run on a personal computer, a TMS320C6678 processor and an NVIDIA Quadro 1000M graphics processing unit (GPU). [ABSTRACT FROM AUTHOR]

Channel state information (CSI) at the base station (BS) is crucial to achieve beamforming and multiplexing gains in multiple-input multiple-output (MIMO) systems. State-of-the-art limited feedback schemes require feedback overhead that scales linearly with the number of BS antennas, which is prohibitive for 5G massive MIMO. This paper proposes novel limited feedback algorithms that lift this burden by exploiting the inherent sparsity in double-directional MIMO channel representation using overcomplete dictionaries. These dictionaries are associated with angle of arrival and angle of departure that specifically account for antenna directivity patterns at both ends of the link. The proposed algorithms achieve satisfactory channel estimation accuracy using a small number of feedback bits, even when the number of transmit antennas at the BS is large—making them ideal for 5G massive MIMO. Judicious simulations reveal that they outperform a number of popular feedback schemes and underscore the importance of using angle dictionaries matching the given antenna directivity patterns, as opposed to uniform dictionaries. The proposed algorithms are lightweight in terms of computation, especially on the user equipment side, making them ideal for actual deployment in 5G systems. [ABSTRACT FROM AUTHOR]

The linear canonical transform (LCT) has been shown to be one of the most powerful tools in signal processing, and in this paper, we propose an adaptive approach for the computation of the discrete LCT (DLCT), termed the sliding discrete linear canonical transform (SDLCT). First, we introduce a scheme for the single-point DLCT, which can effectively calculate a single or a few linear canonical spectra. Second, the SDLCT is proposed based on an iterative algorithm to meet the requirements of online spectral analysis when only a subset of $N$ frequencies are required from an $\tilde{N}\hbox{--}$ point discrete LCT ($N\leq \tilde{N}$). The additivity and reversibility properties of the proposed algorithms are also discussed in detail. Third, the DLCT convolution operation is obtained to reduce the spectral leakage of the proposed algorithm, and time-domain windowing is implemented via frequency-domain convolution. Finally, we present two methods to assess performance with regard to computational complexity and precision and to show the correctness of the derived results. [ABSTRACT FROM AUTHOR]

In this paper, we consider the case in which one needs to carry out Gabor analysis and synthesis on large signals using a short support analysis window and its corresponding, possibly longer canonical dual window, respectively. In this asymmetric context, we propose a novel factorization of the Gabor frame operator that exploits its strong and well-known structure and leads to a computational cost for synthesis, which is comparable to the one needed for short support analysis. The proposed factorization applies to any Gabor system with very mild conditions and leads to a potentially promising alternative to current synthesis algorithms in the case of short analysis windows whose support is slightly above the number of frequency channels. [ABSTRACT FROM AUTHOR]

Source localization and spectral estimation are among the most fundamental problems in statistical and array signal processing. Methods that rely on the orthogonality of the signal and noise subspaces, such as Pisarenko's method, MUSIC, and root-MUSIC, are some of the most widely used algorithms to solve these problems. As a common feature, these methods require both a priori knowledge of the number of sources and an estimate of the noise subspace. Both requirements are complicating factors to the practical implementation of the algorithms and, when not satisfied exactly, can potentially lead to severe errors. In this paper, we propose a new localization criterion based on the algebraic structure of the noise subspace that is described for the first time to the best of our knowledge. Using this criterion and the relationship between the source localization problem and the problem of computing the greatest common divisor (GCD), or more practically approximate GCD, for polynomials, we propose two algorithms, which adaptively learn the number of sources and estimate their locations. Simulation results show a significant improvement over root-MUSIC in challenging scenarios such as closely located sources, both in terms of detection of the number of sources and their localization over a broad and practical range of signal-to-noise ratios. Furthermore, no performance sacrifice in simple scenarios is observed. [ABSTRACT FROM AUTHOR]