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Dirty paper coding (DPC) scheme is the capacity achieving transmission technique in multiuser MIMO downlink channels. As a suboptimal solution to DPC, successive zero-forcing DPC (SZF-DPC) has been proposed recently. The zero-interference constraint in designing the precoding matrices limits the number of supportable users. In this correspondence, we propose three low-complexity suboptimal user scheduling algorithms to exploit the multiuser diversity gain in SZF-DPC as the number of users grows. The first algorithm greedily maximizes the true sum rate. The second algorithm is based on eigenvalues. The third algorithm relies on the diagonal elements of the effective channel matrix since eigenvalues and diagonal entries of a Hermitian matrix have a strong relationship. Simulation results show that the proposed scheduling algorithms can obtain a significant fraction of sum rate of the optimal solution. Furthermore, the performance analysis is provided to prove that the proposed user selection algorithms can achieve the same asymptotic sum rate as that of DPC. [ABSTRACT FROM AUTHOR]

The use of a large-scale antenna array (LSAA) has become an important characteristic of multi-antenna communication systems to achieve beamforming gains such as in designing millimeter-wave (mmWave) systems to combat severe propagation losses. In such applications, each antenna element has to be driven by a radio frequency (RF) chain for the implementation of fully-digital beamformers, significantly increasing the hardware cost, complexity, and power consumption. Therefore, constant-modulus analog beamforming (CMAB) becomes a viable solution. In this paper, we consider the scaled analog beamforming (SAB) or constant-modulus analog beamforming (CMAB) architecture and design the system parameters by solving two variants of beampattern matching problem. In the first case, both the magnitude and phase of the beampattern are matched to the given desired beampattern whereas in the second case, only the magnitude of the beampattern is matched. Both the beampattern matching problems are cast as a variant of the constant-modulus least-squares (CLS) problem. We provide efficient algorithms based on the alternating majorization-minimization (AMM) framework that combines the alternating minimization and the MM frameworks and the conventional-cyclic coordinate descent (C-CCD) algorithms to solve the problem in each case. We also propose algorithms based on a new modified-CCD (M-CCD) based approach. For all the developed algorithms we prove convergence to a Karush-Kuhn-Tucker (KKT) point (or a stationary point). Numerical results demonstrate that the proposed algorithms converge faster than the state-of-the-art solutions. Among all the algorithms, the M-CCD-based algorithms have faster convergence when evaluated in terms of the number of iterations and the AMM-based algorithms offer lower complexity. [ABSTRACT FROM AUTHOR]

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

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]

The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for downlink channel state information within reach—since such channel can be fully characterized by several key parameters. However, most existing channel estimation work under the DD model has not yet considered DP arrays, perhaps because of the complex array manifold and the resulting difficulty in algorithm design. In this paper, we first reveal that the DD channel with DP arrays at the transmitter and receiver can be naturally modeled as a low-rank tensor, and thus the key parameters of the channel can be effectively estimated via tensor decomposition algorithms. On the theory side, we show that the DD–DP parameters are identifiable under mild conditions, by leveraging identifiability of low-rank tensors. Furthermore, a compressed tensor decomposition algorithm is developed for alleviating the downlink training overhead. We show that, by using judiciously designed pilot structure, the channel parameters are still guaranteed to be identified via the compressed tensor decomposition formulation even when the size of the pilot sequence is much smaller than what is needed for conventional channel identification methods, such as linear least squares and matched filtering. Extensive simulations are employed to showcase the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for the design of transmit and receive beamformers for interference alignment (IA) without symbol extensions in multi-antenna cellular networks. We consider IA in a G cell network with K users/cell, N antennas at each base station (BS) and M antennas at each user. The proposed framework is developed by recasting the conditions for IA as two sets of rank constraints, one on the rank of interference matrices, and the other on the transmit beamformers in the uplink. The interference matrix consists of all the interfering vectors received at a BS from the out-of-cell users in the uplink. Using these conditions and the crucial observation that the rank of interference matrices under alignment can be determined beforehand, this paper develops two sets of algorithms for IA. The first part of this paper develops rank minimization algorithms for IA by iteratively minimizing a weighted matrix norm of the interference matrix. Different choices of matrix norms lead to reweighted nuclear norm minimization (RNNM) or reweighted Frobenius norm minimization (RFNM) algorithms with significantly different per-iteration complexities. Alternately, the second part of this paper devises an alternating minimization (AM) algorithm where the rank-deficient interference matrices are expressed as a product of two lower-dimensional matrices that are then alternately optimized. Simulation results indicate that RNNM, which has a per-iteration complexity of a semidefinite program, is effective in designing aligned beamformers for proper-feasible systems with or without redundant antennas, while RFNM and AM, which have a per-iteration complexity of a quadratic program, are better suited for systems with redundant antennas. [ABSTRACT FROM PUBLISHER]

One-bit quantization can significantly reduce the massive multiple-input and multiple-output (MIMO) system hardware complexity, but at the same time it also brings great challenges to the system algorithm design. Specifically, it is difficult to recover information from the highly distorted samples as well as to obtain accurate channel estimation without increasing the number of pilots. In this paper, a novel inference algorithm called variational approximate message passing (VAMP) for one-bit quantized massive MIMO receiver is developed, which attempts to exploit the advantages of both the variational Bayesian inference algorithm and the bilinear generalized approximated message passing algorithm to accomplish joint channel estimation and data detection in a closed form with first-order complexity. Asymptotic state evolution analysis indicates the fast convergence rate of VAMP and also provides a lower bound for the data detection error. Moreover, through extensive simulations, we show that VAMP can achieve excellent detection performance with low pilot overhead in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

This paper studies the achievable rates of Gaussian interference channels with additive white Gaussian noise (AWGN), when improper or circularly asymmetric complex Gaussian signaling is applied. For the Gaussian multiple-input multiple-output interference channel (MIMO-IC) with the interference treated as Gaussian noise, we show that the user's achievable rate can be expressed as a summation of the rate achievable by the conventional proper or circularly symmetric complex Gaussian signaling in terms of the users' transmit covariance matrices, and an additional term, which is a function of both the users' transmit covariance and pseudo-covariance matrices. The additional degrees of freedom in the pseudo-covariance matrix, which is conventionally set to be zero for the case of proper Gaussian signaling, provide an opportunity to further improve the achievable rates of Gaussian MIMO-ICs by employing improper Gaussian signaling. To this end, this paper proposes widely linear precoding, which efficiently maps proper information-bearing signals to improper transmitted signals at each transmitter for any given pair of transmit covariance and pseudo-covariance matrices. In particular, for the case of two-user Gaussian single-input single-output interference channel (SISO-IC), we propose a joint covariance and pseudo-covariance optimization algorithm with improper Gaussian signaling to achieve the Pareto-optimal rates. By utilizing the separable structure of the achievable rate expression, an alternative algorithm with separate covariance and pseudo-covariance optimization is also proposed, which guarantees the rate improvement over conventional proper Gaussian signaling. [ABSTRACT FROM PUBLISHER]

The multiple-input multiple-output (MIMO) system makes efficient use of spectrum and increases the transmission throughput in wireless communications. The sphere decoding (SD) is an efficient algorithm that enables the maximum-likelihood (ML) detection for the MIMO system. However, the SD algorithm has variable complexity, and its complexity increases rapidly with decreasing signal-to-noise ratio (SNR). In this paper, we propose a novel ML detection algorithm for the MIMO system based on differential metrics. We define the differential metrics and derive the associated recursive calculation. We then give the indicative functions, which can be used to possibly find some ML-detected bits of the initial sequence. The indicative functions are further applied to implement an efficient tree search for ML detection. The proposed algorithm does not need QR decomposition and matrix inversion. The tree search process needs only the additive operation, while the number of multiplications before the tree search is constant. Our algorithm can achieve the exact ML detection as the SD algorithm. Unlike the SD algorithm, the complexity of our algorithm reduces with decreasing SNR, whereas at high SNR, the complexity is nearly constant. We also give the convergence analysis for the SD and proposed algorithms, and the simulation verifies our analysis. For the proposed algorithm, the number of necessary memory is constant during the tree search, and the implementation by parallel processing is possible. The soft output of ML-detected bits can also be generated in our algorithm. [ABSTRACT FROM PUBLISHER]

The problem of transmit code design in multiple-input multiple-output (MIMO) radar systems is addressed in this paper. The problem is considered in both colocated and widely separated antenna radars in the presence of signal-dependent interference and target mobility. Due to complexity of exact expressions of the optimal detector in the MIMO radars, information theoretic criteria are employed as design metrics which results in a general form of nonconvex optimization problems. In order to tackle the problem, a novel technique based on the minorization-maximization algorithm is proposed and solutions are presented under practical constraints on the transmit code, namely energy constraint, peak-to-average-power ratio constraint, and similarity constraint. Furthermore, the devised method is extended to be robust against uncertainties of the clutter and interference statistics. Finally, numerical examples are used to show the performance of the proposed technique in different situations. [ABSTRACT FROM PUBLISHER]

Unit-modulus least squares (ULS) problems arise in many applications, including phase-only beamforming, sensor network localization, synchronization, phase retrieval, and radar code design. ULS formulations can always be recast as unit-modulus quadratic programs, to which semidefinite relaxation (SDR) can be applied, and is often the state-of-the-art approach. However, SDR lifts the problem dimension (i.e., the number of variables) from $N$ to $N^2$, which drastically increases the memory burden and computational cost when the problem size is already large—e.g., when designing phase-only beamformer weights for massive multiple-input-multiple-output systems. This paper focuses on scalable first-order algorithms for the ULS problem and some of its variants. It advocates using simple gradient projection (GP) as a starting point for solving the ULS problem, establishes global convergence of GP to a Karush–Kuhn–Tucker point for this NP-hard problem, and bounds its iteration complexity. Then it proposes ULS extensions tailored to reflect practical beamformer design objectives, bringing in and exploiting new degrees of freedom to improve the beampattern designs. Simple variants of GP are proposed to deal with these extended ULS problems. Simulations are used to showcase the effectiveness of the proposed algorithms in both the plain ULS problem and in the context of phase-only beamforming. [ABSTRACT FROM PUBLISHER]

This paper considers the jointly optimal pilot and data power allocation in single-cell uplink massive multiple-input-multiple-output systems. Using the spectral efficiency (SE) as performance metric and setting a total energy budget per coherence interval, the power control is formulated as optimization problems for two different objective functions: the weighted minimum SE among the users and the weighted sum SE. A closed form solution for the optimal length of the pilot sequence is derived. The optimal power control policy for the former problem is found by solving a simple equation with a single variable. Utilizing the special structure arising from imperfect channel estimation, a convex reformulation is found to solve the latter problem to global optimality in polynomial time. The gain of the optimal joint power control is theoretically justified, and is proved to be large in the low-SNR regime. Simulation results also show the advantage of optimizing the power control over both pilot and data power, as compared to the cases of using full power and of only optimizing the data powers as done in previous work. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an algorithm to design interference alignment (IA) precoding and decoding matrices for arbitrary MIMO X networks. The proposed algorithm is rooted in the homotopy continuation techniques commonly used to solve systems of nonlinear equations. Homotopy methods find the solution of a target system by smoothly deforming the solution of a start system which can be trivially solved. Unlike previously proposed IA algorithms, the homotopy continuation technique allows us to solve the IA problem for both unstructured (i.e., generic) and structured channels such as those that arise when time or frequency symbol extensions are jointly employed with the spatial dimension. To this end, we consider an extended system of bilinear equations that include the standard alignment equations to cancel the interference, and a new set of bilinear equations that preserve the desired dimensionality of the signal spaces at the intended receivers. We propose a simple method to obtain the start system by randomly choosing a set of precoders and decoders, and then finding a set of channels satisfying the system equations, which is a linear problem. Once the start system is available, standard prediction and correction techniques are applied to track the solution all the way to the target system. We analyze the convergence of the proposed algorithm and prove that, for many feasible systems and a sufficiently small continuation parameter, the algorithm converges with probability one to a perfect IA solution. The simulation results show that the proposed algorithm is able to consistently find solutions achieving the maximum number of degrees of freedom in a variety of MIMO X networks with or without symbol extensions. Further, the algorithm provides insights into the feasibility of IA in MIMO X networks for which theoretical results are scarce. [ABSTRACT FROM PUBLISHER]

This paper studies a multiple-input multiple-output (MIMO) broadcast relay channel (BRC), in which a multiple-antenna base station (BS) communicates with multiple-antenna users through an infrastructure-based multiple-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and linear processing at the RS, our aim is to find the input covariance matrices and the RS beamforming matrix that maximize the system sum-rate. To solve this non-convex problem, a more tractable dual multiple access relay channel (MARC) is investigated and an alternating-minimization algorithm is proposed. Furthermore, the mapping from the resulting covariance matrices for the MARC to the covariance matrices for the BRC is derived. Unlike other existing single-antenna-user schemes, our solution is applicable to a more general network with any number of antennas at the users. Compared with two such single-antenna-user schemes, simulations show that the proposed scheme outperforms the all-pass relay design and performs similarly to the SVD-relay design. Moreover, the proposed design performs close to the sum-rate upper bound with the performance gap decreasing with increasing number of antennas per user. It is also observed that having more antennas at the RS than at the BS is desirable for better system performance. [ABSTRACT FROM PUBLISHER]

The block diagonalization (BD) scheme is a low-complexity suboptimal precoding technique for multiuser multiple input-multiple output (MIMO) downlink channels, which completely precancels the multiuser interference. Accordingly, the precoder of each user lies in the null space of other users' channel matrices. In this paper, we propose an iterative algorithm using QR decompositions (QRDs) to compute the precoders. Specifically, to avoid dealing with a large concatenated matrix, we apply the QRD to a sequence of matrices of lower dimensions. One problem of BD schemes is that the number of users that can be simultaneously supported is limited due to zero interference constraints. When the number of users is large, a set of users must be selected, and selection algorithms should be designed to exploit the multiuser diversity gain. Finding the optimal set of users requires an exhaustive search, which has too high computational complexity to be practically useful. Based on the iterative precoder design, this paper proposes a low-complexity user selection algorithm using a greedy method, in which the precoders of selected users are recursively updated after each selection step. The selection metric of the proposed scheduling algorithm relies on the product of the squared row norms of the effective channel matrices, which is related to the eigenvalues by the Hadamard and Schur inequalities. An asymptotic analysis is provided to show that the proposed algorithm can achieve the optimal sum rate scaling of the MIMO broadcast channel. The numerical results show that the proposed algorithm achieves a good trade-off between sum rate performance and computational complexity. When users suffer different channel conditions, providing fairness among users is of critical importance. To address this problem, we also propose two fair scheduling (FS) algorithms, one imposing fairness in the approximation of the data rate, and another directly imposing fairness in the product of the squared row norms of the effective channel matrices. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a Partial Interference Alignment and Interference Detection (PIAID) design for K-user quasi-static MIMO interference channels with discrete constellation inputs. Each transmitter has M antennas and transmits L independent data streams to the desired receiver with N receive antennas. We focus on the case where not all K-1 interfering transmitters can be aligned at every receiver. As a result, there will be residual interference at each receiver that cannot be aligned. Each receiver detects and cancels the residual interference based on the constellation map. However, there is a window of unfavorable interference profile at the receiver for Interference Detection (ID). In this paper, we propose a low complexity Partial Interference Alignment scheme in which we dynamically select the user set for IA so as to create a favorable interference profile for ID at each receiver. We first derive the average symbol error rate (SER) by taking into account of the non-Guassian residual interference due to discrete constellation. Using graph theory, we then devise a low complexity user set selection algorithm for the PIAID scheme, which minimizes the asymptotically tight bound for the average end-to-end SER performance. Moreover, we substantially simplify interference detection at the receiver using Semi-Definite Relaxation (SDR) techniques. It is shown that the SER performance of the proposed PIAID scheme has significant gain compared with various conventional baseline solutions. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a robust lattice alignment design for K-user quasi-static multiple-input multiple-output (MIMO) interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite signal-to-noise ratio (SNR) or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method compared with the time-division multiple-access (TDMA), two-stage maximum-likelihood (ML) decoding, generalized Han–Kobayashi (HK), distributive IA and conventional IA methods in the literature. [ABSTRACT FROM PUBLISHER]

Achieving the boundary of the capacity region in the multiple-input multiple-output (MIMO) broadcast channel requires the use of dirty paper coding (DPC). As practical nearly optimum implementations of DPC are computationally complex, purely linear approaches are often used instead. However, in this case, the problem of maximizing a weighted sum rate constitutes a nonconvex and, in most cases, also a combinatorial optimization problem. In this paper, we present two heuristic nearly optimum algorithms with reduced computational complexity. For this purpose, a lower bound for the weighted sum rate under linear zero-forcing constraints is used. Based on this bound, both greedy algorithms successively allocate data streams to users. In each step, the user is determined that is given an additional data stream such that the increase in weighted sum rate becomes maximum. Thereby, the data stream allocations and filters obtained in the previous steps are kept fixed and only the filter corresponding to the additional data stream is optimized. The first algorithm determines the receive and transmit filters directly in the downlink. The other algorithm operates in the dual uplink, from which the downlink transmit and receive filters can be obtained via the general rate duality leading to nonzero-forcing in the downlink. Simulation results reveal marginal performance losses compared to more complex algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study the invertibility of M-variate Laurent polynomial N x P matrices. Such matrices represent multidimensional systems in various settings such as filter banks, multiple-input multiple-output systems, and multirate systems. Given an N x P Laurent polynomial matrix H( z1,.. . , ZM) of degree at most k, we want to find a P x N Laurent polynomial left inverse matrix G(z) of H(z) such that G(z)H(z) = I. We provide computable conditions to test the invertibility and propose algorithms to find a particular inverse. The main result of this paper is to prove that H(z) is generically invertible when N P ⩾ M; whereas when N P < M, then H(z) is generically noninvertible. As a result, we propose an algorithm to find a particular inverse of a Laurent polynomial matrix that is faster than current algorithms known to us. [ABSTRACT FROM AUTHOR]

Recently, the duality between multiple-input multiple-output (MIMO) multiple-access channels (MAC) and MIMO broadcast channels (BCs) has been established under a total power constraint. The same set of rates for MAC can be achieved in BC exploiting the MAC-BC duality formulas while preserving the total power constraint. In this paper, we describe the BC optimal power allocation applying this duality in a downstream x-digital subscriber lines (xDSLs) context under a total power constraint for all modems over all tones. Then, a new algorithm called BC-optimal spectrum balancing (BC-OSB) is devised for a more realistic power allocation under per-modem total power constraints. The capacity region of the primal BC problem under per-modem total power constraints is found by the dual optimization problem for the BC under per-modem total power constraints which can be rewritten as a dual optimization problem in the MAC by means of a precoder matrix based on the Lagrange multipliers. We show that the duality gap between the two problems is zero. The multiuser power allocation problem has been solved for interference channels and MAC using the OSB algorithm. In this paper we solve the problem of multiuser power allocation for the BC case using the OSB algorithm as well and we derive a computational efficient algorithm that will be referred to as BC-OSB. Simulation results are provided for two VDSL2 scenarios: the first one with differential-mode (DM) transmission only and the second one with both DM and phantom-mode (PM) transmissions. [ABSTRACT FROM AUTHOR]

Abstract-This paper compares the complexity of the sphere decoder (SD) and a previously proposed detection scheme, denoted here as block SD (BSD), when they are applied to the detection of multiple-input multiple-output (MIMO) systems `in frequency-selective channels. The complexity of both algorithms depends on their preprocessing and tree search stages. Although the BSD was proposed as a means of greatly reducing the complexity of the preprocessing stage of the SD, no study was done on how the complexity of the tree search stage could be affected by that reduced preprocessing stage. This paper shows, both analytically and through simulation, that the reduction iü preprocessing complexity provided by the BSD has the side effect of increasing the complexity of its tree search stage compared to that of the SD, independent of the signal-to-noise ratio (SNR). In addition, this paper shows how sorting the columns of the frequency-selective channel matrix in the SD does not reduce the complexity of the tree search stage, contrary to what occurs in frequency-flat channels. [ABSTRACT FROM AUTHOR]

The paper presents two formulations of causal cubic splines with equidistant knots. Both are based on a causal direct B-spline filter with parallel or cascade implementation. In either implementation, the causal part of the impulse response is realized with an efficient infinite-impulse-response (HR) structure, while only the anticausal part is approximated with a finite-order finite-impulse-response (FIR) filter. Resulting cubic coefficients are computed from the causal B-spline coefficients by using a third-order output FIR filter with either single-input multiple-output (SIMO) or multiple-input multiple-output (MIMO) structure, depending on the chosen formulation of the cubic spline. The paper demonstrates and proves that the properties of the resulting causal splines are quite different, whether they are based on a more popular B-spline formulation, or a bit neglected tridiagonal matrix formulation. It is shown that the proposed low-complexity but accurate causal interpolators can be realized for many practical applications with the delay of only a few samples. [ABSTRACT FROM AUTHOR]

In this paper, a new deterministic technique for blind equalization of multiple-input multiple-output (MIMO) channels is presented. The proposed method relies on the channel order diversity of the finite impulse response-MIMO channel, and it does not make any assumption about the spectra or the finite alphabet property of the sources. Assuming that the lengths of the single-input multiple-output channels are known or estimated a priori, it is able to extract the sources up to a scale and rotation matrix ambiguity, which only affects to those sources associated to single-input multiple-output (SIMO) channels with equal lengths. Unlike previously proposed techniques, the method described in this paper obtains the equalizers and the best linear combination of their outputs in a single step and, thus, optimally. The reformulation of the proposed method as a set of nested canonical correlation analysis problems is exploited to obtain efficient batch and adaptive equalization algorithms. Finally, the performance of the proposed algorithms is evaluated by means of some simulation examples. [ABSTRACT FROM AUTHOR]

The kurtosis maximization criterion has been effectively used for blind spatial extraction of one source from an instantaneous mixture of multiple non-Gaussian sources, such as the kurtosis maximization algorithm by Ding and Nguyen, and the fast kurtosis maximization algorithm (FKMA) by Chi and Chen. By empirical studies, we found that the smaller the normalized kurtosis magnitude of the extracted source signal, the worse the performance of these algorithms. In this paper, with the assumption that each source is a non-Gaussian linear process, a novel blind source extraction algorithm, called turbo source extraction algorithm (TSEA), is proposed. The ideas of the TSEA are to exploit signal temporal properties for increasing the normalized kurtosis magnitude, and to apply spatial and temporal processing in a cyclic fashion to improve the signal extraction performance. The proposed TSEA not only outperforms the FKMA, but also shares the convergence and computation advantages enjoyed by the latter. This paper also considers the extraction of multiple sources, also known as source separation, by incorporating the proposed TSEA into the widely used multistage successive cancellation (MSC) procedure. A problem with the MSC procedure is its susceptibility to error propagation accumulated at each stage. We propose two non-cancellation multistage (NCMS) algorithms, referred to as NCMS-FKMA and NCMS-TSEA, that are free from the error propagation effects. Simulation results are presented to show that the NCMS-TSEA yields substantial performance gain compared with some existing blind separation algorithms, together with a computational complexity comparison. Finally, we draw some conclusions. [ABSTRACT FROM AUTHOR]

In this paper, a method for approximating a multi- input multi-output (MIMO) transfer function by a causal finite-im- pulse response (FIR) paraunitary (PU) system in a weighted least- squares sense is presented. Using a complete parameterization of FIR PU systems in terms of Householder-like building blocks, an iterative algorithm is proposed that is greedy in the sense that the observed mean-squared error at each iteration is guaranteed to not increase. For certain design problems in which there is a phase- type ambiguity in the desired response, which is formally defined in the paper, a phase feedback modification is proposed in which the phase of the FIR approximant is fed back to the desired response. With this modification in effect, it is shown that the resulting iterative algorithm not only still remains greedy, but also offers a better magnitude-type fit to the desired response. Simulation results show the usefulness and versatility of the proposed algorithm with respect to the design of principal component filter bank (PCFB)-like filter banks and the FIR PU interpolation problem. Concerning the PCFB design problem, it is shown that as the McMillan degree of the FIR PU approximant increases, the resulting filter bank behaves more and more like the infinite-order PCFB, consistent with intuition. In particular, this PCFB-like behavior is shown in terms of filter response shape, multiresolution, coding gain, noise reduction with zeroth-order Wiener filtering in the subbands, and power minimization for discrete multitone (DMT)-type transmul- tiplexers. [ABSTRACT FROM AUTHOR]

Lattice Reduction aided soft output MIMO detectors (LR-SOMD) have been demonstrated to offer a promising gain. This work explores the potential of implementing a LR-SOMD on a parallel programmable baseband processor. In this paper, first a LR algorithm called the Data Regularized Parallel Lattice Reduction algorithm (DRP-LR) is proposed. Afterwards, a low-complexity LR-SOMD, Radius Constrained Multi-Tree Selective Spanning (RC-MTSS) is presented. RC-MTSS uses a novel multiple-tree search approach for LR-SOMD, while combining the benefits of Sphere Detection (SD) and Selective Spanning with Fast Enumeration (SSFE). A fixed complexity LR-SOMD, Multi-Tree Selective Spanning (MTSS) is also proposed for implementation. Both the algorithms, DRP-LR and MTSS, are enabled to exploit data level parallelism (DLP) and instruction level parallelism (ILP). In order to evaluate performance, the proposed DRP-LR and MTSS are implemented on the ADRES baseband processor for a 4 \times 4 LTE system using QAM-64. DRP-LR achieves an average throughput of 33.33 M LR per second, which is comparable to recently reported ASIC implementations, while MTSS shows an average throughput of 730 Mbps on the same processor. To the best of authors' knowledge, this is the first reported implementation of a LR-SOMD algorithm on a parallel programmable baseband processor. [ABSTRACT FROM AUTHOR]

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels. [ABSTRACT FROM PUBLISHER]

Parallel multiple-input multiple-output (MIMO) broadcast channels have been shown to be separable from an information theoretic point of view, i.e., capacity can be achieved with a strategy that performs separate encoding and decoding on each subchannel subject to a power allocation across subchannels. In this paper, we show that separability does not necessarily hold if the transmit strategy is restricted to linear transceivers. The proof will be done by identifying a rate tuple that is achievable in a certain set of channels with joint treatment of the subchannels, but lies outside the achievable rate region of separate linear precoding. The implications of this result are that any algorithm to optimize transmit strategies in parallel MIMO broadcast channels that is based on both linear transceivers and separate encoding on each subchannel does in fact introduce two suboptimalities and not only one. [ABSTRACT FROM AUTHOR]

This paper considers the degrees of freedom (DOF) for a K user multiple-input multiple-output (MIMO) M \times N interference channel using interference alignment (IA). A new performance metric for evaluating the efficacy of IA algorithms is proposed, which measures the extent to which the desired signal dimensionality is preserved after zero-forcing the interference at the receiver. Inspired by the metric, two algorithms are proposed for designing the linear precoders and receive filters for IA in the constant MIMO interference channel with a finite number of symbol extensions. The first algorithm uses an eigenbeamforming method to align sub-streams of the interference to reduce the dimensionality of the interference at all the receivers. The second algorithm is iterative, and is based on minimizing the interference leakage power while preserving the dimensionality of the desired signal space at the intended receivers. The improved performance of the algorithms is illustrated by comparing them with existing algorithms for IA using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, we consider a channel estimation scheme for a two-hop nonregenerative MIMO relay system without the direct link between source and destination. This scheme has two phases. In the first phase, the source does not transmit while the relay transmits and the destination receives. In the second phase, the source transmits, the relay amplifies and forwards, and the destination receives. At the destination, the data received in the first phase are used to estimate the relay-to-destination channel, and the data received in the second phase are used to estimate the source-to-relay channel. The linear minimum mean-square error estimation (LMMSE) is used for channel estimation, which allows the use of prior knowledge of channel correlations. For phase 1, an algorithm is developed to compute the optimal source pilot matrix for use at the relay. For phase 2, an algorithm is developed to compute the optimal source pilot matrix for use at the source and the optimal relay pilot matrix for use at the relay. [ABSTRACT FROM AUTHOR]

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean-square error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, the algorithm and its convergence can be extended to a general class of sum-utility maximization problem. The effectiveness of the proposed algorithm is validated by numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced linear minimum mean square error (LMMSE) turbo equalization scheme is proposed for multiple-input multiple-output (MIMO) communication systems with bit-interleaved coded modulation (BICM) in the time domain and multiplexing in the space domain. The proposed turbo equalization scheme outperforms the conventional LMMSE turbo equalization by adopting two new signal processing techniques. First, it performs hybrid soft interference cancellation (HSOIC) by subtracting the soft decisions of the interfering symbols, and the soft decisions are calculated by using a hybrid of the a priori information at the equalizer input and the a posteriori information at the equalizer output. Second, it employs a novel block-wise reliability-based ordering scheme such that more “reliable” symbols are detected before the less “reliable” ones to reduce error propagation in HSOIC. The symbol reliability information is based on the symbol a priori probability, as a unique byproduct of turbo detection, thus can be obtained with very small overhead. A low-complexity approximation of the enhanced MIMO LMMSE turbo equalization is also proposed to balance the tradeoff between complexity and performance. The performance of the enhanced MIMO LMMSE turbo equalization has been verified through both numerical simulations and the undersea experimental data collected in the SPACE08 experiment launched near Martha's Vineyard, Edgartown, MA, in 2008. [ABSTRACT FROM PUBLISHER]

This paper studies opportunistic spectrum sharing strategy (i.e., quantized precoding strategy) selection for cognitive multiple-input multiple-output multiple access channels with limited feedback under interference power constraint and maximum transmission stream number constraint. We put forward a game-theoretic framework to model the precoding strategy selection behaviors of the secondary users under the specified constraints. First, we prove the formulated discrete game is a potential game which possesses at least one feasible pure strategy Nash equilibrium. The feasibility and optimality of the Nash equilibrium are also analyzed. Then we prove that the solution to the sum rate maximization problem constitutes a feasible pure strategy Nash equilibrium of our formulated game. Furthermore, we design two algorithms. The iterative precoding strategy selection algorithm based on the best response rule is designed to attain a feasible Nash equilibrium. The modified algorithm is designed to improve the sum rate performance. Simulation results show that our designed algorithms can achieve optimal or near optimal sum rate performance with low complexity. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of non-symmetrical joint zero-diagonalization (NSJZD) of a given set of matrices and its use in user interference cancellation for a new multiple-access multiple-input multiple-output (MIMO) wireless transmission scheme. First, sufficient conditions for uniqueness of the non-symmetrical zero-diagonalizer are described, and we propose a non-iterative efficient algorithm, termed non-symmetrical joint algebraic zero-diagonalization (NSJAZ), based on the reformulation of the initial problem into a joint generalized eigendecomposition problem. Second, we define the new concept of MIMO zero-division multiple access (ZDMA) allowing training-sequence free and simultaneous multiple access to a wireless receiver base station over a single frequency channel. Each ZDMA emitter combines two types of modulation (FSK/AM). The multiple-antenna ZDMA access point performs an NSJAZ-based blind separation of the different accesses assuming a flat fading channel. Computer simulations of such a system are presented both in analog and digital cases. Finally, to demonstrate that our multiple access scheme is technically feasible and works in a real environment, we present some preliminary results obtained with a full-analog ZDMA-based hardware prototype system that we have developed. [ABSTRACT FROM AUTHOR]

Real-time matrix inversion is a key enabling technology in multiple-input multiple-output (MIMO) communications systems, such as 802.11n. To date, however, no matrix inversion implementation has been devised which supports real-time operation for these standards. In this paper, we overcome this barrier by presenting a novel matrix inversion algorithm which is ideally suited to high performance floating-point implementation. We show how the resulting architecture offers fundamentally higher performance than currently published matrix inversion approaches and we use it to create the first reported architecture capable of supporting real-time 802.11n operation. Specifically, we present a matrix inversion approach based on modified squared Givens rotations (MSGR). This is a new QR decomposition algorithm which overcomes critical limitations in other QR algorithms that prohibits their application to MIMO systems. In addition, we present a novel modification that further reduces the complexity of MSGR by almost 20%. This enables real-time implementation with negligible reduction in the accuracy of the inversion operation, or the BER of a MIMO receiver based on this. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the base station controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in each cluster are adaptive to both intracluster channel state information (CCSI) and intracluster queue state information (CQSI), and computed at each cluster manager (CM) over a shorter time scale. We show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost constrained partially observed Markov decision process (CPOMDP). By exploiting the special problem structure, we derive an equivalent Bellman equation in terms of pattern selection Q-factor to solve the CPOMDP. To address the distributed requirement and computational complexity, we approximate the pattern selection Q-factor by the sum of per-cluster potential functions and propose a novel distributed online learning algorithm to estimate them distributedly. We show that the proposed distributed online learning algorithm converges almost surely. By exploiting the birth-death structure of the queue dynamics, we further decompose the per-cluster potential function into the sum of per-cluster per-user potential functions and formulate the instantaneous power allocation as a per-stage QSI-aware interference game played among all the CMs. The proposed QSI-aware simultaneous iterative water-filling algorithm (QSIWFA) is shown to achieve the Nash equilibrium (NE). [ABSTRACT FROM AUTHOR]

The concept of cognitive radio (CR) has recently received great attention from the research community as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. In this paper we propose and analyze a totally decentralized approach, based on game theory, to design cognitive MIMO transceivers, who compete with each other to maximize their information rate. The formulation incorporates constraints on the transmit power as well as null and/or soft shaping constraints on the transmit covariance matrix, so that the interference generated by secondary users be confined within the temperature-interference limit required by the primary users. We provide a unified set of conditions that guarantee the uniqueness and global asymptotic stability of the Nash equilibrium of all the proposed games through totally distributed and asynchronous algorithms. Interestingly, the proposed algorithms overcome the main drawback of classical waterfilling based algorithms-the violation of the temperature-interference limit-and they have the desired features required for CR applications, such as low-complexity, distributed implementation, robustness against missing or outdated updates of the users, and fast convergence behavior. [ABSTRACT FROM AUTHOR]

A multiple-input multiple-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. This increased virtual aperture enables many of the MIMO radar advantages, including enhanced parameter identifiability and improved resolution. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good autoand cross-correlation properties. We present in this paper new computationally efficient cyclic algorithms for MIMO radar waveform synthesis. These algorithms can be used for the design of unimodular MIMO sequences that have very low autoand cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature. A number of examples are provided to demonstrate the performances of the new waveform synthesis algorithms. [ABSTRACT FROM AUTHOR]

This paper studies the performance of multiple-input multiple-output (MIMO) systems employing multichannel beam-forming in the presence of unequal power cochannel interference and noise. Our results apply to a wide class of multichannel systems which transmit on the eigenmodes of the MIMO channel, allowing for transmission on any arbitrary number of eigensubchannels, with possibly different modulation formats, and with possibly unequal powers. New exact expressions are derived for the symbol error rate (SER) of each eigensubchannel, the global SER, and the outage probability. We also present simplified closed-form expressions for the high SNR regime, and show that cochannel interference has no effect on the diversity order, but leads to a loss in array gain. Our results are based on new closed-form exact and asymptotic expressions which we derive for the marginal ordered eigenvalue distributions of a certain class of finite-dimensional complex random matrices. [ABSTRACT FROM AUTHOR]

Recently, lattice-reduction-aided detectors have been proposed for multiinput multioutput (MIMO) systems to achieve performance with full diversity like the maximum likelihood receiver. However, these lattice-reduction-aided detectors are based on the traditional Lenstra-Lenstra-Lovász (LLL) reduction algorithm that was originally introduced for reducing real lattice bases, in spite of the fact that the channel matrices are inherently complex-valued. In this paper, we introduce the complex LLL algorithm for direct application to reducing the basis of a complex lattice which is naturally defined by a complex-valued channel matrix. We derive an upper bound on proximity factors, which not only show the full diversity of complex LLL reduction-aided detectors, but also characterize the performance gap relative to the lattice decoder. Our analysis reveals that the complex LLL algorithm can reduce the complexity by nearly 50% compared to the traditional LLL algorithm, and this is confirmed by simulation. Interestingly, our simulation results suggest that the complex LLL algorithm has practically the same bit-error-rate performance as the traditional LLL algorithm, in spite of its lower complexity. [ABSTRACT FROM AUTHOR]

The PARAFAC decomposition of a higher-order tensor is a powerful multilinear algebra tool that becomes more and more popular in a number of disciplines. Existing PARAFAC algorithms are computationally demanding and operate in batch mode-both serious drawbacks for on-line applications. When the data are serially acquired, or the underlying model changes with time, adaptive PARAFAC algorithms that can track the sought decomposition at low complexity would be highly desirable. This is a challenging task that has not been addressed in the literature, and the topic of this paper. Given an estimate of the PARAFAC decomposition of a tensor at instant t, we propose two adaptive algorithms to update the decomposition at instant t + 1, the new tensor being obtained from the old one after appending' a new slice in the 'time' dimension. The proposed algorithms can yield estimation performance that is very close to that obtained via repeated application of state-of-art batch algorithms, at orders of magnitude lower complexity. The effectiveness of the proposed algorithms is illustrated using a MIMO radar application (tracking of directions of arrival and directions of departure) as an example. [ABSTRACT FROM AUTHOR]

Abstract-This paper discusses the channel capacity of multiantenna ultra-wideband (UWB) indoor wireless systems. It is shown first that for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) cases, when the transmit SNR is lower than some value, say -20 dB, using optimal power spectrum allocation (OPSA) at the transmitter side can increase the reliable transmission rate considerably compared to the uniform power spectrum allocation scheme, while when the transmit signal-to-noise ratio (SNR) is higher than some value, say 10 dB, the benefit of OPSA is very limited. Therefore, measures such as the water-filling algorithm should be taken to make full use of the frequency-selectivity of UWB systems if the transmit SNR is low. Second, in the multiple-input single-output (MISO) case, the outage probability decreases with the number of transmit antennas when the communication rate is lower than some value, but increases instead when the rate is higher than another value. A critical transmission rate is identified. Third, in the cases of SIMO and MIMO, the communication rate supportable by the channel with a given outage probability increases approximately logarithmically and linearly, respectively, with the number of receive antennas. [ABSTRACT FROM AUTHOR]

Abstract-Linear transmit beamforming and receive combining is a low complexity approach for communications in the multiuser multiple-input multiple-output (MIMO) channel. This paper proposes a full search and two low complexity coordinated algorithms where each receiver uses a quantized combining vector. Since the optimization is performed at the transmitter with channel state information from all the receivers, the receive combining vectors are (either jointly or independently) quantized at the transmitter and sent to the receivers via a low-rate feedforward control channel. Rate bounds are provided to estimate the impact of quantization loss on the achievable rate in Rayleigh channels. Simulations show that the sum rate of the proposed methods using Grassmannian codebooks is close to the sum capacity of the MIMO broadcast channel, especially in the high signal-to-noise ratio (SNR) regime. [ABSTRACT FROM AUTHOR]

Abstract-The concept of multiple-input multiple-output (MIMO) radars has drawn considerable attention recently. Unlike the traditional single-input multiple-output (SIMO) radar which emits coherent waveforms to form a focused beam, the MIMO radar can transmit orthogonal (or incoherent) waveforms. These waveforms can be used to increase the system spatial resolution. The waveforms also affect the range and Doppler resolution. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. Recently, the idea of ambiguity function has been extended to the case of MIMO radar. In this paper, some mathematical properties of the MIMO radar ambiguity function are first derived. These properties provide some insights into the MIMO radar waveform design. Then a new algorithm for designing the orthogonal frequency-hopping waveforms is proposed. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions. [ABSTRACT FROM AUTHOR]

This paper presents an analytical performance investigation of transmit beamforming (BF) systems in Rayleigh product multiple-input multiple-output (MIMO) channels. We first derive new closed-form expressions for the cumulative distribution function, probability density function, and moments of the maximum eigenvalue of a product of independent complex Gaussian matrices, which are used to provide a complete statistical characterization of the received signal-to-noise ratio (SNR). We then derive a number of key performance metrics, including outage probability, symbol error rate, and ergodic capacity. We examine, in detail, three important special cases of the Rayleigh product MIMO channel: the degenerate keyhole scenario and the multiple-input single-output and single-input multiple-output scenarios, for which we derive insightful closed-form expressions for various exact and asymptotic measures (e.g., diversity order, array gain, and high SNR power offset, among others). We also compare the performance of transmit BF with orthogonal space-time block codes and quantify the benefit of exploiting transmitter channel knowledge in Rayleigh product MIMO channels. This is shown to be significant even for low-dimensional systems. [ABSTRACT FROM AUTHOR]

This paper studies capacity-approaching transmission schemes for the multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) channel, under the assumption that the channel state information (CSI) is completely known at the receiver but only partially available at the transmitter via a limited-rate feedback channel. A vertical Bell Labs layered space-time (V-BLAST)-based transmission structure is considered, where multiple data streams are independently encoded at the transmitter (i.e., horizontal encoding) and successively decoded at the receiver by the zero-forcing-based generalized decision feedback equalizer. A closed-loop V-BLAST extension is presented whereby transmit powers, rates, and antenna mappings for multiple data streams at different OFDM tones are jointly optimized at the receiver and then returned to the transmitter via the feedback channel. Two low-complexity algorithms for optimization of feedback parameters are proposed: one is based on the Lagrange dual-decomposition method and the other is a greedy algorithm. Antenna and tone grouping techniques by exploiting the MIMO-OFDM channel space-frequency correlations are also proposed to reduce the feedback complexity. Simulation results show that by only a moderate amount of feedback, the proposed closed-loop V-BLAST scheme improves substantially the throughput of the conventional open-loop V-BLAST scheme without feedback and, furthermore, approaches closely the MIMO-OFDM channel capacity achievable by the eigenmode transmission that requires the complete CSI at the transmitter. [ABSTRACT FROM AUTHOR]

In this paper, we present a near ML-achieving sphere decoding algorithm that reduces the number of search operations in the sphere-constrained search. Specifically, by adding a probabilistic noise constraint on top of the sphere constraint, a more stringent necessary condition is provided, particularly at an early stage, and, hence, branches unlikely to be survived are removed in the early stage of sphere search. The tradeoff between the performance and complexity is easily controlled by a single parameter, so-called pruning probability. Through the analysis and simulations, we show that the complexity reduction is significant while maintaining the negligible performance degradation. [ABSTRACT FROM AUTHOR]

Blind identification is a crucial subtask in signal processing problems such as blind signal separation (BSS) and direction-of-arrival (DOA) estimation. This paper presents a procedure for multiple-input multiple-output instantaneous blind identification based on second-order temporal properties of the signals, such as coloredness and nonstationarity. The procedure consists of two stages. First, based on assumptions on the second-order temporal structure (SOTS) of the source and noise signals, and using subspace techniques, the problem is reformulated in a particular way such that each column of the unknown mixing matrix satisfies a system of multivariate homogeneous polynomial equations. Then, this nonlinear system of equations is solved by means of a so-called homotopy method, which provides a general tool for solving (possibly nonexact) systems of nonlinear equations by smoothly deforming the known solutions of a simple start system into the desired solutions of the target system. Our blind identification procedure allows to estimate the mixing matrix for scenarios with more sources than sensors without resorting to sparsity assumptions, something that is often believed to be impossible when using only second-order statistics. In addition, since our algorithm does not require any assumption on the mixing matrix, also mixing matrices that are rank-deficient or even have identical columns can be identified. Finally, we give examples and performance results for speech source signals. [ABSTRACT FROM AUTHOR]

In distributed detection systems with wireless sensor networks, the communication between sensors and a fusion center is not perfect due to interference and limited transmitter power at the sensors to combat noise at the fusion center's receiver. The problem of optimizing detection performance with such imperfect communication brings a new challenge to distributed detection. In this paper, sensors are assumed to have independent but nonidentically distributed observations, and a multiple-input/multiple-output (MIMO) channel model is included to account for imperfect communication between the sensors and the fusion center. The J-divergence between the distributions of the detection statistic under different hypotheses is used as a performance criterion in order to provide a tractable analysis. Optimization of performance with individual and total transmitter power constraints on the sensors is studied, and the corresponding power allocation scheme strikes a tradeoff between two factors, the communication channel quality and the local decision quality. For the case with orthogonal channels, under certain conditions, the power allocation can be solved by a weighted water-filling algorithm. Simulations show that the proposed power allocation in certain cases only consumes as little as 25% of the total power used by an equal power allocation scheme while achieving the same performance. [ABSTRACT FROM AUTHOR]