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The time division multiple access (TDMA) technique has been applied in automotive multiple-input multiple-output (MIMO) radar. However, it suffers from the transmit energy loss, and as a result the parameter estimation performance degradation when the number of transmit elements increases. To tackle these problems, a transmit beamspace (TB) Doppler division multiple access (DDMA) approach is proposed. First, a phase modulation matrix with empty Doppler spectrum is introduced. By exploiting the empty Doppler spectrum, a test function based on sequential detection is developed to mitigate the Doppler ambiguity in DDMA waveform. Then, a discrete Fourier transform (DFT)-based TB in slow-time is formed. The proposed method can achieve waveform diversity in Doppler domain and generate a TB in slow-time that concentrates the transmitted power in a fixed spatial region to improve the transmit energy distribution for automotive MIMO radar, which is favored by medium/long range radar (MRR/LRR) applications. As compared to the conventional TDMA technique, the proposed TB DDMA approach can fully exploit the transmission capabilities of all transmit elements to ensure that the emitted power is efficiently used and inherits easy implementation.Moreover, the proposed TB DDMA method avoids the trade-off between the active time for each transmit antenna element and the frame time due to Doppler ambiguity mitigation. Simulation results verify the effectiveness of the proposed TB DDMA approach for automotive MIMO radar. [ABSTRACT FROM AUTHOR]

Existing sparse array (SA) geometries, such as coprime and nested arrays, can be regarded as a concatenation of two uniform linear arrays (ULAs). Such arrays lead to a significant increase in the number of degrees of freedom (DOF) when the second-order information is utilized, i.e., they provide long virtual difference coarrays (DCAs). Thus, the idea of this paper is based on the observation that SAs can be fitted through the concatenations of sub-ULAs. A corresponding SA design principle, called ULA fitting, is then proposed. It aims to design SAs from multiple sub-ULAs. Towards this goal, a polynomial model for arrays is used, and based on it, a DCA structure is analyzed for the general case when an SA is composed of an arbitrary number of sub-ULAs. ULA fitting enables to transfer SA design requirements, such as hole–free, low mutual coupling, and other requirements, into pseudo-polynomial equation, and hence, find particular solutions. We mainly focus on designing SAs with low mutual coupling and large uniform DOF. Two examples of SAs with closed-form expressions are then developed based on ULA fitting. Numerical experiments verify the superiority of the proposed SAs in the presence of heavy mutual coupling. [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]

• Accelerated first-order method for solving large-scale optimization problems with composite objective structure. • New composite estimating functions constructed by using the gradient mapping technique and a tighter lower bound on the objective function. • Efficient line-search strategy and novel convergence analysis results. • Initialization of the proposed method can be made robust to the imperfect knowledge of the strong convexity parameter. A plethora of problems arising in signal processing, machine learning and statistics can be cast as large-scale optimization problems with a composite objective structure. Such problems are typically solved by utilizing iterative first-order algorithms. In this work, we devise a new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite objective structure. Specifically, we introduce a new class of estimating functions, which are obtained by utilizing both a tight lower bound on the objective function, as well as the gradient mapping technique. Then, using the proposed estimating functions, we construct a class of Composite Objective Multi-step Estimating-sequence Techniques (COMET), which are endowed with an efficient line-search procedure. We prove that our proposed COMET enjoys the accelerated convergence rate, and our newly established convergence results allow for step-size adaptation. Our theoretical findings are supported by extensive computational experiments on various problem types and real-world datasets. Moreover, our numerical results show evidence of the robustness of the proposed method to the imperfect knowledge of the smoothness and strong convexity parameters. [ABSTRACT FROM AUTHOR]

We develop new fast and efficient algorithms for designing single or multiple unimodular waveforms with good auto- and cross-correlation or weighted correlation properties, which are highly desired in radar and communication systems. The waveform design is based on the minimization of the integrated sidelobe level (ISL) and weighted ISL (WISL) of waveforms. As the corresponding problems can quickly grow to a large scale with increasing the code length and the number of waveforms, the main issue turns to be the development of fast large-scale optimization techniques. The difficulty is also that the corresponding optimization problems are nonconvex, but the required accuracy is high. There- fore, we formulate the ISL and WISL minimization problems as nonconvex quartic optimization problems in frequency domain, and then simplify them into quadratic problems via majorization-minimization technique, which is one of the basic techniques for addressing large-scale and/or nonconvex optimization problems. While designing our fast algorithms, we explore and use the inherent algebraic structures in objective functions to rewrite them into quartic forms, and in the case of WISL minimization, to derive additionally an alternative quartic form that allows us to apply the quartic-quadratic transformation. Our algorithms are applicable to large-scale unimodular waveform design problems as they are proved to have lower or comparable computational burden (analyzed theoretically) and faster convergence speed (confirmed by comprehensive simulations) than the state-of-the-art algorithms. In addition, the waveforms designed by our algorithms demonstrate better correlation properties compared to their counterparts. [ABSTRACT FROM AUTHOR]

This paper gives the finite-length analysis of a spectrum estimation method for the case that the samples are obtained at a rate lower than the Nyquist rate. The method is referred to as the averaged correlogram for undersampled data. It is based on partitioning the spectrum into a number of segments and estimating the average power within each spectral segment. This method is able to estimate the power spectrum density of a signal from undersampled data without essentially requiring the signal to be sparse. We derive the bias and the variance of the spectrum estimator, and show that there is a tradeoff between the accuracy of the estimation, the frequency resolution, and the complexity of the estimator. A closed-form approximation of the estimation variance is derived, which clearly shows how the variance is related to different parameters. The asymptotic behavior of the estimator is also investigated, and it is proved that in the case of a white Gaussian process, this spectrum estimator is consistent. Moreover, the estimation made for different spectral segments becomes uncorrelated as the signal length tends to infinity. Finally, numerical examples and simulation results are provided, which approve the theoretical conclusions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-dimensional joint transmit array interpolation and beamspace design for planar array mono-static multiple-input-multiple-output radar for direction-of-arrival (DOA) estimation via tensor modeling. Our underlying idea is to map the transmit array to a desired array and suppress the transmit power outside the spatial sector of interest. In doing so, the signal-to-noise ratio is improved at the receive array. Then, we fold the received data along each dimension into a tensorial structure and apply tensor-based methods to obtain DOA estimates. In addition, we derive a closed-form expression for DOA estimation bias caused by interpolation errors and argue for using a specially designed look-up table to compensate for the bias. The corresponding Cramér–Rao bound (CRB) is also derived. Simulation results are provided to show the performance of the proposed method and compare its performance to CRB. [ABSTRACT FROM PUBLISHER]

In this paper, superimposed pilots are introduced as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a non-iterative scheme for uplink channel estimation based on superimposed pilots and derive an expression for the uplink signal-to-interference-plus-noise ratio (SINR) at the output of a matched filter employing this channel estimate. Based on this expression, we observe that power control is essential when superimposed pilots are employed. Moreover, the quality of the channel estimate can be improved by reducing the interference that results from transmitting data alongside the pilots, and an intuitive iterative data-aided scheme that reduces this component of interference is also proposed. Approximate expressions for the uplink SINR are provided for the iterative data-aided method as well. In addition, we show that a hybrid system with users utilizing both time-multiplexed and superimposed pilots is superior to an optimally designed system that employs only time-multiplexed pilots, even when the non-iterative channel estimate is used to build the detector and precoder. We also describe a simple approach to implement this hybrid system by minimizing the overall inter- and intracell interference. Numerical simulations demonstrating the performance of the proposed channel estimation schemes and the superiority of the hybrid system are also provided. [ABSTRACT FROM PUBLISHER]

This paper discusses performance limits of the segmented compressive sampling (CS) system that collects correlated measurements. It is shown that the effect of correlation among measurements for the segmented CS can be characterized by a penalty term in the corresponding bounds on the measurement rate. Moreover, this penalty term is vanishing as the signal dimension increases. It means that the performance degradation due to the fixed correlation among measurements obtained by the segmented CS (as compared to the standard CS with equivalent size measurement matrix) is negligible for a high-dimensional signal. In combination with the fact that the signal reconstruction quality improves with additional measurements obtained by the segmented CS (as compared to the standard CS without additional measurements), the fact that the additional correlated measurements also provide new information about a signal is a strong argument for the segmented CS. [ABSTRACT FROM PUBLISHER]

In this paper, we derive an ambiguity function (AF) for the transmit beamspace (TB)-based MIMO radar for the case of far-field targets and narrow-band waveforms. The effects of transmit coherent processing gain and waveform diversity are incorporated into the AF definition. To cover all the phase information conveyed by different factors, we introduce the equivalent transmit phase centers. The newly defined AF serves as a generalized AF form for which the phased-array (PA) and traditional MIMO radar AFs are important special cases. We establish relationships among the defined TB-based MIMO radar AF and the existing AF results, including the Woodward’s AF, the AFs defined for traditional colocated MIMO radar, and also the PA radar AF, respectively. Moreover, we compare the TB-based MIMO radar AF with the square-summation-form AF definition and identify two limiting cases to bound its “clear region” in Doppler-delay domain that is free of sidelobes. Corresponding bounds for these two cases are derived, and it is shown that the bound for the worst case is inversely proportional to the number of transmitted waveforms K, whereas the bound for the best case is independent of K. The actual “clear region” of the TB-based MIMO radar AF depends on the array configuration and is in between of the worst- and best-case bounds. We propose a new TB design strategy to reduce the levels of the AF sidelobes and show in simulations that proper design of the TB matrix leads to reduction of the relative sidelobe levels of the TB-based MIMO radar AF. [ABSTRACT FROM PUBLISHER]

• A robust adaptive beamforming problem for general-rank signal model is considered. • The worst-case SINR maximization formulation is established. • The closed-form optimal value of the minimization problem of the least-squares residual over the matrix errors with an induced norm constraint is derived. • With the closed-form result, the worst-case SINR maximization problem is approximated by a sequence of SOCPs. The robust adaptive beamforming (RAB) problem for general-rank signal model with an uncertainty set defined through a matrix induced norm is considered. The worst-case signal-to-interference-plus-noise ratio (SINR) maximization RAB problem is formulated. First, the closed-form optimal value for a minimization problem of the least-squares residual over the matrix errors with an induced l p , q -norm constraint is derived. Then, the maximization problem is reformulated into the maximization of the difference between an l 2 -norm function and an l q -norm function, with a convex quadratic constraint. It is shown that for any q ≥ 1 in the set of rational numbers, the maximization problem can be approximated by a sequence of second-order cone programming problems, with the ascent optimal values. The resultant beamvector for some q in the set with the maximal actual array output SINR, is treated as the candidate making the RAB design improved the most. In addition, a generalized RAB problem of maximizing the difference between an l p -norm function and an l q -norm function with the convex quadratic constraint is studied, and the actual array output SINR is further enhanced by properly selecting p and q. Simulation examples are presented to demonstrate the improved performance of the robust beamformers for certain matrix induced l p , q -norms. [ABSTRACT FROM AUTHOR]

Abstract: A new reweighted penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LMS cost function which forces the solution to be sparse. Our reweighted penalized LMS algorithm introduces in addition a reweighting of the CIR coefficient estimates to promote a sparse solution even more and approximate closer. We provide in depth quantitative analysis of the reweighted penalized LMS algorithm. An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted penalized LMS algorithm outperforms the standard LMS, which is expected. However, our quantitative analysis also answers the question of what is the maximum sparsity level in the channel for which the reweighted penalized LMS algorithm is better than the standard LMS. Simulation results showing the better performance of the reweighted penalized LMS algorithm compared to other existing LMS-type algorithms are given. [Copyright &y& Elsevier]

Abstract: Robustness is typically understood as an ability of adaptive beamforming algorithm to achieve high performance in the situations with imperfect, incomplete, or erroneous knowledge about the source, propagation media, and antenna array. It is also desired to achieve high performance with as little as possible prior information. In the last decade, several fruitful principles to minimum variance distortionless response (MVDR) robust adaptive beamforming (RAB) design have been developed and successfully applied to solve a number of problems in a wide range of applications. Such principles of MVDR RAB design are summarized here in a single paper. Prof. Gershman has actively participated in the development and applications of a number of such MVDR RAB design principles. [Copyright &y& Elsevier]

• We formulate graph error models for the adjacency matrix, which help to quantify the deviation from the true matrix using a few parameters. • We study the structural effects of the proposed error models on the adjacency matrix. • The effects of different type of errors in adjacency matrix specification are illustrated in filtering of graph signal and ICA of graph signals. The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors. A little attention has been paid to modelling such errors in the adjacency matrix, and studying their effects on GSP methods. However, modelling errors in the adjacency matrix will enable both to study the graph error effects in GSP and to develop robust GSP algorithms. In this paper, we therefore introduce practically justifiable graph error models. We also study, both analytically when possible and numerically, the graph error effect on the performance of GSP methods in different types of problems such as filtering of graph signals and independent component analysis of graph signals (graph decorrelation). [ABSTRACT FROM AUTHOR]

A general notion of robustness for robust adaptive beamforming (RAB) problem and a unified principle for minimum variance distortionless response (MVDR) RAB techniques design are formulated. This principle is to use standard MVDR beamformer in tandem with an estimate of the desired signal steering vector found based on some imprecise prior information. Differences between various MVDR RAB techniques occur only because of the differences in the assumed prior information and the corresponding signal steering vector estimation techniques. A new MVDR RAB technique, which uses as little as possible and easy to obtain imprecise prior information, is developed. The objective for estimating the steering vector is the maximization of the beamformer output power, while the constraints are the normalization condition and the requirement that the estimate does not converge to any of the interference steering vectors and their linear combinations. The prior information used is only the imprecise knowledge of the antenna array geometry and angular sector in which the actual steering vector lies. Mathematically, the proposed MVDR RAB is expressed as the well known non-convex quadratically constrained quadratic programming problem with two constraints, which can be efficiently and exactly solved. Some new results for the corresponding optimization problem such as a new algebraic way of finding the rank-one solution from the general-rank solution of the relaxed problem and the condition under which the solution of the relaxed problem is guaranteed to be rank-one are derived. Our simulation results demonstrate the superiority of the proposed method over other previously developed RAB techniques. [ABSTRACT FROM AUTHOR]

The problem of estimating the parameters of a moving target in multiple-input multiple-output (MIMO) radar is considered and a new approach for estimating the moving target parameters by making use of the phase information associated with each transmit–receive path is introduced. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is noncoherent. We model the target motion within a certain processing interval as a polynomial of general order. The first three coefficients of such a polynomial correspond to the initial location, velocity, and acceleration of the target, respectively. A new maximum likelihood (ML) technique for estimating the target motion coefficients is developed. It is shown that the considered ML problem can be interpreted as the classic “overdetermined” nonlinear least-squares problem. The proposed ML estimator requires multidimensional search over the unknown polynomial coefficients. The Cramér–Rao bound (CRB) for the proposed parameter estimation problem is derived. The performance of the proposed estimator is validated by simulation results and is shown to achieve the CRB. [ABSTRACT FROM AUTHOR]

The problem of optimal power allocation among the relays in a two-hop decode-and-forward cooperative relay network with independent Rayleigh fading channels is considered. It is assumed that only the relays that decode the source message correctly contribute in data transmission. Moreover, only the knowledge of statistical channel state information is available. A new simple closed-form expression for the average symbol error probability is derived. Based on this expression, a new power allocation method that minimizes the average symbol error probability and takes into account the constraints on the total average power of all the relay nodes and maximum instant power of each relay node is developed. The corresponding optimization problem is shown to be a convex problem that can be solved using interior-point methods. However, an approximate closed-form solution is obtained and shown to be practically more appealing due to significant complexity reduction. The accuracy of the approximation is discussed. Moreover, the so obtained closed-form solution gives additional insights into the optimal power allocation problem. Simulation results confirm the improved performance of the proposed power allocation scheme as compared to other schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a transmit beamspace energy focusing technique for multiple-input multiple-output (MIMO) radar with application to direction finding for multiple targets. The general angular directions of the targets are assumed to be located within a certain spatial sector. We focus the energy of multiple (two or more) transmitted orthogonal waveforms within that spatial sector using transmit beamformers which are designed to improve the signal-to-noise ratio (SNR) gain at each receive antenna. The subspace decomposition-based techniques such as MUSIC can then be used for direction finding for multiple targets. Moreover, the transmit beamformers can be designed so that matched-filtering the received data to the waveforms yields multiple (two or more) data sets with rotational invariance property that allows applying search-free direction finding techniques such as ESPRIT or parallel factor analysis (PARAFAC). Unlike previously reported MIMO radar ESPRIT/PARAFAC-based direction finding techniques, our method achieves the rotational invariance property in a different manner combined also with the transmit energy focusing. As a result, it achieves better estimation performance at lower computational cost. The corresponding Cramer-Rao bound is derived and its dependence on the number of waveforms used is discussed. Simulation results also show the superiority of the proposed technique over the existing techniques. [ABSTRACT FROM AUTHOR]

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for 1) the maximization of the sum capacity of all users; 2) the maximization of the worst user capacity; and 3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results. [ABSTRACT FROM PUBLISHER]

This paper studies optimal bandwidth and power allocation in a cognitive radio network where multiple secondary users (SUs) share the licensed spectrum of a primary user (PU) under fading channels using the frequency division multiple access scheme. The sum ergodic capacity of all the SUs is taken as the performance metric of the network. Besides the peak/average transmit power constraints at the SUs and the peak/average interference power constraint imposed by the PU, total bandwidth constraint of the licensed spectrum is also taken into account. Optimal bandwidth allocation is derived in closed-form for any given power allocation. The structures of optimal power allocations are also derived under all possible combinations of the aforementioned power constraints. These structures indicate the possible numbers of users that transmit at nonzero power but below their corresponding peak powers, and show that other users do not transmit or transmit at their corresponding peak powers. Based on these structures, efficient algorithms are developed for finding the optimal power allocations. [ABSTRACT FROM AUTHOR]

A new segmented compressed sampling (CS) method for analog-to-information conversion (AIC) is proposed. An analog signal measured by a number of parallel branches of mixers and integrators (BMIs), each characterized by a specific random sampling waveform, is first segmented in time into M segments. Then the subsamples collected on M different segments and K different BMIs are reused so that a larger number of samples (at most K^2) than the number of BMIs is collected. This technique is shown to be equivalent to extending the measurement matrix, which consists of the BMI sampling waveforms, by adding new rows without actually increasing the number of BMIs. We prove that the extended measurement matrix satisfies the restricted isometry property with overwhelming probability if the original measurement matrix of BMI sampling waveforms satisfies it. We also prove that the signal recovery performance can be improved if our segmented CS-based AIC is used for sampling instead of the conventional AIC with the same number of BMIs. Therefore, the reconstruction quality can be improved by slightly increasing (by M \leq K times) the sampling rate per each BMI. Simulation results verify the effectiveness of the proposed segmented CS method and the validity of our theoretical results. Particularly, our simulation results show significant signal recovery performance improvement when the segmented CS-based AIC is used instead of the conventional AIC with the same number of BMIs. [ABSTRACT FROM AUTHOR]

Collaborative beamforming (CB) is a power efficient method for data communications in wireless sensor networks (WSNs) which aims at increasing the transmission range in the network by radiating the power from a cluster of sensor nodes in the directions of the intended base stations or access points (BSs/APs). The CB average beampattern shows a deterministic behavior and the mainlobe of the CB sample beampattern is independent of the particular node locations. However, the CB for a cluster of a finite number of collaborative nodes results in a sample beampattern with sidelobes that severely depend on the particular node locations. High level sidelobes can cause unacceptable interference when they occur at directions of unintended BSs/APs. Therefore, sidelobe control in CB has a potential to decrease the interference at unintended BSs/APs and increase the network transmission rate by enabling simultaneous multilink CB. Traditional sidelobe control techniques are proposed for centralized antenna arrays and are not suitable for WSNs. In this paper, we show that scalable and low-complexity sidelobe control techniques suitable for CB in WSNs can be developed based on a node selection technique which makes use of the randomness of the node locations. A node selection algorithm with low-rate feedback is developed to search over different node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of search trials required for selecting the collaborative nodes, the resulting interference, and the corresponding transmission rate improvements. Our simulation results show that the interference can be significantly reduced and the transmission rate can be significantly increased when node selection is implemented with CB. The simulation results also show close agreement with our theoretical results. [ABSTRACT FROM AUTHOR]

Multiuser diversity (MUDiv) is one of the central concepts in multiuser (MU) systems. In particular, MUDiv allows for scheduling among users in order to eliminate the negative effects of unfavorable channel fading conditions of some users on the system performance. Scheduling, however, consumes energy (e.g., for making users' channel state information available to the scheduler). This extra usage of energy, which could potentially be used for data transmission, can be very wasteful, especially if the number of users is large. In this paper, we answer the question of how much MUDiv is required for energy limited MU systems. Focusing on uplink MU wireless systems, we develop MU scheduling algorithms which aim at maximizing the MUDiv gain. Toward this end, we introduce a new realistic energy model which accounts for scheduling energy and describes the distribution of the total energy between scheduling and data transmission stages. Using the fact that such energy distribution can be controlled by varying the number of active users, we optimize this number by either i) minimizing the overall system bit error rate (BER) for a fixed total energy of all users in the system or ii) minimizing the total energy of all users for fixed BER requirements. We find that for a fixed number of available users, the achievable MUDiv gain can be improved by activating only a subset of users. Using asymptotic analysis and numerical simulations, we show that our approach benefits from MUDiv gains higher than that achievable by generic greedy access algorithm, which is the optimal scheduling method for energy unlimited systems. [ABSTRACT FROM AUTHOR]

Resource allocation problems in multi-user systems, modeled as Nash bargaining (NB) cooperative games, are investigated under different constraints. Using the joint time division multiplexing and frequency division multiplexing (TDM/FDM) scheme as a manner of cooperation in the NB resource allocation game with only spectrum mask constraints (SMCs), it is shown that the efficiency of the corresponding solution depends on both the interference-to-signal and interference-to-noise ratios of the users. Sufficient conditions under which the joint TDM/FDM based NB solution outperforms a noncooperative Nash equilibrium solution are derived and shown to be mild. A semi-distributed algorithm is proposed for finding the NB solution of the corresponding game with SMCs only. The NB based resource allocation game with both SMCs and total power constraints (TPCs) is shown to be a nonconvex optimization problem of high complexity. For such a game, a classification of two-user systems into bandwidth- and power-dominant systems is proposed based on the concept of Pareto-optimality. This classification gives insights to the resource allocation problem by showing that the benefits of both users are guaranteed to increase simultaneously only when the dominant resource in the system increases. Using this classification, efficient suboptimal algorithms of low complexity are derived based on the idea of optimally allocating the dominant resource and suboptimally allocating the other resource. Simulation results demonstrate the efficiency of the proposed cooperative NB resource allocation strategies. [ABSTRACT FROM AUTHOR]

We propose a new technique for multiple-input multiple-output (MIMO) radar with colocated antennas which we call phased-MIMO radar. The new technique enjoys the advantages of the MIMO radar without sacrificing the main advantage of the phased-array radar which is the coherent processing gain at the transmitting side. The essence of the proposed technique is to partition the transmit array into a number of subarrays that are allowed to overlap. Then, each subarray is used to coherently transmit a waveform which is orthogonal to the waveforms transmitted by other subarrays. Coherent processing gain can be achieved by designing a weight vector for each subarray to form a beam towards a certain direction in space. Moreover, the subarrays are combined jointly to form a MIMO radar resulting in higher angular resolution capabilities. Substantial improvements offered by the proposed phased-MIMO radar technique as compared to the phased-array and MIMO radar techniques are demonstrated analytically and by simulations through analyzing the corresponding beam-patterns and the achievable output signal-to-noise-plus-interference ratios. Both analytical and simulation results validate the effectiveness of the proposed phased-MIMO radar. [ABSTRACT FROM AUTHOR]

Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different "cohabitation" scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria. [ABSTRACT FROM AUTHOR]

A class of computationally efficient linear precoders for space- time block coded multiple-input multiple-output wireless systems is derived based on the minimization of the exact symbol error rate (SER) and its upper bound. Both correlations at the transmitter and receiver are assumed to be present, and only statistical channel state information in the form of the transmit and receive correlation matrices is assumed to be available at the transmitter. The convexity of the design based on SER minimization is established and exploited. The advantage of the developed technique is its low complexity. We also find various relationships of the proposed designs to the existing precoding techniques, and derive very simple closed-form precoders for special cases such as two or three receive antennas and constant receive correlation. The numerical simulations illustrate the excellent SER performance of the proposed precoders. [ABSTRACT FROM AUTHOR]

An interesting relationship between the probability-constrained and worst-case optimization based robust minimum variance (MV) beamformers has been discovered. It is shown that both in the cases of circularly symmetric Gaussian and worst-case distributions of the steering vector mismatch, the probability-constrained robust MV beamforming problem can be tightly approximated as a convex second-order cone programming (SOCP) problem. The latter problem is mathematically equivalent to that resulting from the deterministic worst-case approach and, therefore, probability-constrained beamformers can be interpreted and implemented using their deterministic worst-case counterparts. However, an important advantage of the developed probability-constrained MV beamformers with respect to their standard worst-case counterparts is that the former approaches enable to explicitly quantify the parameters of the uncertainty region in terms of the beamformer outage probability. [ABSTRACT FROM AUTHOR]

In the orthogonal frequency-division multiplexing (OFDM) scheme, some subcarriers may be subject to a deep fading. Adaptive techniques can be applied to mitigate this effect if the channel-state information (CSI) is available at the transmitter. In this paper, we study the performance of an OFDM-based communication system whose transmitter has only one bit of CSI per subcarrier, obtained through a low-rate feedback. Three adaptive approaches are considered to exploit such a CSI feedback: adaptive subcarrier selection; adaptive power allocation (APA); and adaptive modulation selection (AMS). Under the conditions of a constant raw data rate and perfect feedback channel, the performance of these approaches are analyzed and compared in terms of raw bit-error rate. It is shown that one-bit CSI feedback can greatly enhance the system performance. Moreover, imperfections of the feedback channel are considered, and their impact on the performance of these techniques is studied. It is shown that by exploiting the knowledge that the feedback channel is imperfect, the performance of the APA and AMS techniques can be substantially improved. [ABSTRACT FROM AUTHOR]

A new linear block precoding technique is proposed to improve the performance of orthogonal frequency division multiplexing (OFDM) communication systems. The design of our precoder is based on the maximization of the mean cutoff rate and requires only the knowledge of the average relative channel multipath powers and delays at the transmitter. Simulation results show an improved performance of the proposed precoder relative to other known linear block precoding techniques. [ABSTRACT FROM AUTHOR]

Parallel factor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher way arrays, also referred to as. It decomposes a given array in a sum of multi- linear terms, analogous to the familiar bilinear vector outer products that appear In matrix decomposition. PARAFAC analysis generalizes and unifies common array processing models, like joint diagonalization and ESPRIT; it has found numerous applications from blind multiuser detection and multidimensional harmonic retrieval, to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on (alternating) least squares, which is optimal for Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. In this paper, we develop two iterative algorithms for the least absolute error fitting of general multilinear models. The first is based on efficient interior point methods for linear programming, employs in an alternating fashion. The second is based on a weighted median filtering iteration, which is particularly appealing from a simplicity viewpoint. Both are guaranteed to converge in terms of absolute error. Performance is illustrated by means of simulations, and compared to the pertinent Cramér-Rao bounds (CRBs). [ABSTRACT FROM AUTHOR]

In this paper, the problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless communications. A time-varying user power loading in the uplink mode is proposed to make the model identifiable and to enable application of PARAFAC analysis. Then, identifiability issues are studied in detail and closed-form expressions for the corresponding modified Cramér-Rao bound (CRB) are obtained. Furthermore, two blind spatial signature estimation algorithms are developed. The first technique is based on the PARAFAC fitting trilinear alternating least squares (TALS) regression procedure, whereas the second one makes use of the joint approximate diagonalization algorithm. These techniques do not require any knowledge of the propagation channel and/or sensor array manifold and are applicable to a more general class of scenarios than earlier approaches to blind spatial signature estimation. [ABSTRACT FROM AUTHOR]

We address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields using sparse sensor arrays composed of multiple widely separated subarrays. In such arrays, intersubarray spacings are substantially larger than the signal wavelength, and therefore, sensor noises can be assumed to be uncorrelated between different subarrays. This leads to a block-diagonal structure of the noise covariance matrix which enables a substantial reduction of the number of nuisance noise parameters and ensures the identifiability of the underlying DOA estimation problem. A new deterministic ML DOA estimator is derived for this class of sparse sensor arrays. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters. In contrast to the analytic concentration used in conventional ML techniques, the implementation of the proposed estimator is based on an iterative procedure, which includes a stepwise concentration of the log-likelihood (LL) function. The proposed algorithm is shown to have a straightforward extension to the case of uncalibrated arrays with unknown sensor gains and phases. It is free of any further structural constraints or parametric model restrictions that are usually imposed on the noise covariance matrix and received signals in most existing ML-based approaches to DOA estimation in spatially correlated noise. [ABSTRACT FROM AUTHOR]

Adaptive beamforming methods are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated. In particular, if the desired signal is present in training snapshots, the adaptive array performance may be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors (spatial signatures). Such mismatches can occur as a result of environmental nonstationarities, look direction errors, imperfect array calibration, distorted antenna shape, as well as distortions caused by medium in homogeneities, near-far mismatch, source spreading, and local scattering. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch. Our approach is based on the optimization of worst-case performance. It turns out that the natural formulation of this adaptive beamforming problem involves minimization of a quadratic function subject to infinitely many non-convex quadratic constraints. We show that this (originally intractable) problem can be reformulated in a convex form as the so-called second-order cone (SOC) program and solved efficiently (in polynomial time) using the well-established interior point method. It is also shown that the proposed technique can be interpreted in terms of diagonal loading where the optimal value of the diagonal loading factor is computed based on the known level of uncertainty of the signal steering vector. Computer simulations with several frequently encountered types of signal steering vector mismatches show better performance of our robust beamformer as compared with existing adaptive beamforming algorithms. [ABSTRACT FROM AUTHOR]

In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Detecting the presence or absence of a known signal is an important aspect of underwater acoustic (UWA) communications as it is a vital first step to process the received data. The influence of impulsive noise and multipath propagation on detection are both considered in this paper. Many robust detectors have been designed to cope with impulsive noise, but they tend to ignore the impact of multipath propagation, which may decrease the detection probability. To emphasize the role of multipath channel, we propose two-stage detection methods. In the first stage, the channel is estimated based on robust orthogonal matching pursuit, while in the second stage, detectors that use the channel estimation from the first stage, namely augmented log-likelihood ratio detector and augmented pseudo-correlation detector, are developed. The former detector is parametric and the latter one is non-parametric. The improved performance is demonstrated in simulated multipath channel and actual UWA channel with both simulated noise and recorded snapping shrimp noise, indicating that the proposed detectors are resistant to impulsive noise. It is also shown that the use of multipath information helps to improve the detection performance. [ABSTRACT FROM AUTHOR]

We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for two-dimensional transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A general higher-order tensor model that enables computationally efficient DOA estimation is designed. Whereas other tensor decomposition-based methods treat all factor matrices as arbitrary, the essence of the proposed DOA estimation method is to fully exploit the Vandermonde structure of the factor matrices to take advantage of the shift-invariance between and within different transmit subarrays. Specifically, the received signal of TB MIMO radar is expressed as a higher-order tensor. A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices. The generators of the Vandermonde factor matrices are computed to estimate the phase rotations between subarrays, which can be utilized as a look-up table for finding target DOAs. The proposed tensor model and the DOA estimation algorithm are also straightforwardly applicable for the one-dimensional TB MIMO radar case. It is further shown that our proposed approach can be used in a more general scenario where the transmit subarrays with arbitrary but identical configuration can be non-uniformly displaced. We also show that both the tensor rank of the signal tensor and the matrix rank of a particular matrix derived from the signal tensor are identical to the number of targets. Thus, the number of targets can be estimated via matrix rank determination. Simulation results illustrate the performance improvement of the proposed DOA estimation method as compared to other tensor decomposition-based techniques for TB MIMO radar. [ABSTRACT FROM AUTHOR]

• Four new methods for estimating the parameters of an autoregressive (AR) process based on observations corrupted by white noise are presented. • The first three methods are iterative, while the last one is non-iterative. • The performance of the proposed methods is evaluated in terms of various numerical examples, which demonstrate their superiority in terms of accuracy and robustness against the observation noise. This paper presents four new methods for estimating the parameters of an autoregressive (AR) process based on observations corrupted by white noise. The first three methods are iterative, while the last one is non-iterative. One method is designed to achieve an unbiased estimation of the AR parameters by undermining the destructive impact of observation noise in terms of utilizing the null space of the AR parameter vector. Another one uses both low- and high-order Yule-Walker equations to construct a constrained least squares optimization problem, in which the variance of observation noise is estimated by alternating between two equations. One more method exploits an approximation which leads to reducing the problem of estimating the AR parameters with arbitrary order p to estimating just two parameters, while the last one estimates the variance of the observation noise using the minimum eigenvalue of the enlarged autocorrelation matrix. The performance of the proposed methods is evaluated in terms of various numerical examples, which demonstrate their superiority in terms of accuracy and robustness against the observation noise compared to state-of-the-art existing methods in most simulation examples. It makes the proposed methods a good fit for practical analysis of data contaminated by observation noise, when AR modeling is applicable, and gives a range of choices of methods for different data analysis situations. [ABSTRACT FROM AUTHOR]

This paper studies the impact of additional pilot overhead for covariance matrix estimation in a time-division duplexed (TDD) massive multiple-input multiple-output (MIMO) system. We choose average uplink (UL) and downlink (DL) spectral efficiencies (SEs) as performance metrics for the massive MIMO system, and derive closed form expressions for them in terms of the additional pilot overhead. The expressions are derived by considering linear minimum mean squared error (LMMSE)-type and element-wise LMMSE-type channel estimates that represent LMMSE and element-wise LMMSE with estimated covariance matrices, respectively. Using these expressions, a detailed theoretical analysis of SE behavior as a function of pilot overhead for both LMMSE-type and element-wise LMMSE-type channel estimation are presented, followed by simulations, which also demonstrate and validate theoretical results. [ABSTRACT FROM AUTHOR]

• A general learning-based decomposition model suitable for fusing images from various imaging modalities is proposed. • The multimodal images are decomposed into correlated and uncorrelated components. • A CDL method based on simultaneous sparse approximation is proposed for estimating the correlated features. • The uncorrelated components are estimated using a Pearson correlation-based constraint. This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for a variety of imaging modalities. In particular, the images to be fused are decomposed into correlated and uncorrelated components using sparse representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization problem is solved by an alternating minimization algorithm. Contrary to other learning-based fusion methods, the proposed approach does not require any training data, and the correlated features are extracted online from the data itself. By preserving the uncorrelated components in the fused images, the proposed fusion method significantly improves on current fusion approaches in terms of maintaining the texture details and modality-specific information. The maximum-absolute-value rule is used for the fusion of correlated components only. This leads to an enhanced contrast-resolution without causing intensity attenuation or loss of important information. Experimental results show that the proposed method achieves superior performance in terms of both visual and objective evaluations compared to state-of-the-art image fusion methods. [ABSTRACT FROM AUTHOR]

Describes an algorithm for noise cancellation using multi-sensory signal recordings of the same noisy source. Problem formulation; Scheme for noise cancellation for multi-sensory signals; Derivation of the algorithms; Proof of optimality of the algorithm for the weight parameter adjustment.

• This paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level limited discussion in the open literature. We formulate an l_p-norm metric for pulse compression model which suits for either the ISL or PSL minimization only with different initializations of p. The waveform and filter can be updated simultaneously (not in alternating manner). A new algorithm with guaranteed convergence is proposed to solve the introduced problem. A number of new Lemmas are proved to find proper majorizers. Low sidelobe level is required for pulse compression (PC) radar systems since it can reduce false alarm triggered by the high sidelobe and improve the ability to detect weak targets nearby strong scattering points. In most of the existing literature, sidelobe suppression is achieved by either transmit waveform design at the transmitter or mismatched filter optimization at the receiver. Joint design of waveform and filter is an approach with limited discussion. Thus, this paper aims to jointly design transmit waveform and mismatched filter to achieve low sidelobe level. We first formulate an L p -norm metric for PC model which suits for either the integrated sidelobe level or peak sidelobe level (PSL) minimization (only with different initializations of p). Then we present a new iterative method to deal with the problem by using Dinkelbach's scheme and majorization minimization method. The proposed method is shown to be convergent and the computational complexity is also analyzed. Numerical examples demonstrate that waveforms and filters designed by using the proposed method produce lower PSL than the existing techniques. [ABSTRACT FROM AUTHOR]

Cooperative wireless multicast is investigated, in which a source sends multicast messages to a number of users. For a multicast message from the source, some users do not successfully receive the message (called unsuccessful users). For a successful user (who successfully receives the message from the source), we define its worst relaying channel gain as the smallest channel gain among its channel gains to all unsuccessful users. It is proposed that the successful user whose worst relaying channel gain is the highest among the worst relaying channel gains of all successful users is selected to serve as a relay. Considering that the channels in the system are independent but nonidentically distributed Rayleigh fading, we derive a closed-form outage probability expression for the proposed scheme. It is shown that the proposed scheme can achieve full diversity, and thus, having a larger number of users can improve the outage performance. Furthermore, we study the time allocation strategy that determines the durations used by the source and the selected relay for their transmissions, respectively. An approximate optimal time allocation is derived. In addition, we also investigate the case with relay selection error and the case with mixed Rayleigh/Rician fading. Simulation results verify the performance of the proposed cooperative multicast scheme and time allocation strategy. [ABSTRACT FROM AUTHOR]

A wireless relay network with one source, one relay and one destination is considered, where nodes communicate via N orthogonal channels. We develop optimal power allocation strategies at both the source and relay for maximizing the overall source-destination capacity under individual power constraints at the source and relay. Some properties of the optimal solution are studied. [ABSTRACT FROM PUBLISHER]

Wireless multimedia sensor networks (WMSNs) are expected to support multimedia services such as delivery of video and audio streams. However, due to the relatively stringent quality-of-service (QoS) requirements of multimedia services (e.g., high transmission rates and timely delivery) and the limited wireless resources, it is possible that not all the potential sensor nodes can be admitted into the network. Thus, node admission is essential for WMSNs, which is the target of this paper. Specifically, we aim at the node admission and its interaction with power allocation and link scheduling. A cross-layer design is presented as a two-stage optimization problem, where at the first stage the number of admitted sensor nodes is maximized, and at the second stage the network lifetime is maximized. Interestingly, it is proved that the two-stage optimization problem can be converted to a one-stage optimization problem with a more compact and concise mathematical form. Numerical results demonstrate the effectiveness of the two-stage and one-stage optimization frameworks. [ABSTRACT FROM AUTHOR]

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well. [ABSTRACT FROM AUTHOR]