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In this paper, we propose and implement blind multiuser carrier frequency offsets (CFOs) estimation for multi-input multi-output (MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system using orthogonal propagator method (OPM) over a noisy frequency-selective channel. The performance of typical OPM is limited at low signal-to-noise ratio (SNR) due to its noise-free signal model. The additive white Gaussian noise mainly impacts the diagonal elements of the data covariance matrix (DCM) of the received signal. First, the proposed fractional CFOs estimator minimizes the noise effect by reconstructing the denoised diagonal elements of the DCM for the received signal through cubic Hermite interpolation. Therefore, we use modified OPM to estimate multiple CFOs for MIMO SC-FDMA systems. Second, we have used an iterative method based on the first-order Taylor series approximation of the CFO vector to avoid peak ambiguity and reduce the number of iterations required in the grid search. The mean square error of the proposed CFO estimator considerably improves compared with the existing methods at a low SNR regime. The bit error rate performance of the proposed method for MIMO SC-FDMA is also compared with the existing methods. Finally, the proposed estimator is validated by implementing it on radio frequency hardware testbed over an indoor propagation environment. [ABSTRACT FROM AUTHOR]

Carrier frequency offset (CFO) and channel estimation is a classic topic with a large body of prior work using the maximum likelihood (ML) approach together with the Cramér-Rao lower bound (CRLB) analysis. We give the maximum a posteriori probability (MAP) estimation solution, which is particularly useful for tracking. Unlike the ML cases, the corresponding Bayesian CRLB (BCRLB) shows a clear relation with parameters and low complexity algorithms are provided to achieve the BCRLB in almost all SNR range. Among them, the universal algorithm takes a new approach and avoids error floors of the traditional approach. We assume that the time invariant MIMO channel within a packet can have spatial correlation and nonzero mean. The estimation is based on pilot signals. An unexpected result is that the joint MAP estimation is equivalent to an individual MAP estimation of the frequency offset first, again different from the ML results. We provide insight on the pilot/training signal design based on the BCRLB. Unlike past algorithms that trade performance and/or complexity for the accommodation of time varying channels, the MAP solution provides a different route for dealing with time variation. Within a short enough (segment of) packet where the channel and CFO are approximately time invariant, the low complexity algorithm can be employed. Similar to the belief propagation, the estimation of the previous (segment of) packet can serve as the prior knowledge for the next (segment of) packet. [ABSTRACT FROM AUTHOR]

In contrast to Part I of this treatise (Xing, 2021) that focuses on the optimization problems associated with single matrix variables, in this paper, we investigate the application of the matrix-monotonic optimization framework in the optimization problems associated with multiple matrix variables. It is revealed that matrix-monotonic optimization still works even for multiple matrix-variate based optimization problems, provided that certain conditions are satisfied. Using this framework, the optimal structures of the matrix variables can be derived and the associated multiple matrix-variate optimization problems can be substantially simplified. In this paper several specific examples are given, which are essentially open problems. Firstly, we investigate multi-user multiple-input multiple-output (MU-MIMO) uplink communications under various power constraints. Using the proposed framework, the optimal structures of the precoding matrices at each user under various power constraints can be derived. Secondly, we considered the optimization of the signal compression matrices at each sensor under various power constraints in distributed sensor networks. Finally, we investigate the transceiver optimization for multi-hop amplify-and-forward (AF) MIMO relaying networks with imperfect channel state information (CSI) under various power constraints. At the end of this paper, several simulation results are given to demonstrate the accuracy of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

Beamspace channel estimation is indispensable for millimeter-wave MIMO systems relying on lens antenna arrays for achieving substantially increased data rates, despite using a small number of radio-frequency chains. However, most of the existing beamspace channel estimation schemes have been designed for narrowband systems, while the rather scarce wideband solutions tend to assume that the sparse beamspace channel exhibits a common support in the frequency domain, which has a limited validity owing to the effect of beam squint caused by the wide bandwidth in practice. In this paper, we investigate the wideband beamspace channel estimation problem without the common support assumption. Specifically, by exploiting the effect of beam squint, we first prove that each path component of the wideband beamspace channel exhibits a unique frequency-dependent sparse structure. Inspired by this structure, we then propose a successive support detection (SSD) based beamspace channel estimation scheme, which successively estimates all the sparse path components following the classical idea of successive interference cancellation. For each path component, its support at different frequencies is jointly estimated to improve the accuracy by utilizing the proved sparse structure, and its influence is removed to estimate the remaining path components. The performance analysis shows that the proposed SSD-based scheme can accurately estimate the wideband beamspace channel at a low complexity. Simulation results verify that the proposed SSD-based scheme enjoys a reduced pilot overhead, and yet achieves an improved channel estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper considers and analyzes the performance of semiblind, training, and data-aided channel estimation schemes for multiple-input multiple-output (MIMO) filter bank multicarrier (FBMC) systems with offset quadrature amplitude modulation. A semiblind MIMO-FBMC (SB-MF) channel estimator is developed that exploits both the training symbols and second-order statistical properties of the data symbols, which leads to a significant decrease in the mean squared error (MSE) with respect to its conventional training-based counterpart. Its performance is compared with that of the interference approximation method-based least squares MIMO-FBMC (LS-MF) channel estimator, wherein the channel is estimated using exclusively training symbols. The Cramér–Rao lower bounds are derived to characterize the MSE of the proposed and LS-MF estimators, which interestingly demonstrate that while the MSE per parameter of the proposed scheme decreases with the number of receive antennas, it remains constant for the training-based scheme. The resulting bit error rates are derived for the proposed SB-MF and LS-MF channel estimators. An expectation maximization-based data-aided MIMO-FBMC channel estimator is also investigated that performs iterative maximum a posteriori channel estimation in the E-step followed by data detection in the M-step. A comparative analysis is presented for the computational complexities of the various schemes. Simulation results with practical channel models demonstrate that the proposed semiblind scheme significantly outperforms the training-based and data-aided schemes. [ABSTRACT FROM AUTHOR]

This paper develops schemes for block-sparse channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems that exploit the spatial sparsity inherent in such channels. Initially, a novel sparse Bayesian learning (SBL) based block-sparse channel estimation technique is developed for a mmWave hybrid MIMO system with multiple measurement vectors, which overcomes the shortcomings of the existing orthogonal matching pursuit-based framework. This is subsequently extended to a temporally correlated block-sparse mmWave MIMO channel. Further, an online recursive hierarchical Bayesian Kalman Filter is developed for the estimation of a time-selective mmWave MIMO channel. Bayesian Cramér–Rao bounds are also derived for the proposed static and time-selective mmWave MIMO channel estimation schemes followed by precoder/combiner design employing the SBL-based imperfect channel estimates. Simulation results are presented to demonstrate the improved performance of the proposed SBL-based channel estimation techniques in comparison to the popular OMP-based scheme proposed recently. [ABSTRACT FROM AUTHOR]

This paper presents two novel approaches for joint carrier frequency offset (CFO) and doubly selective channel estimation in the uplink of multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) systems. Considering high-mobility situations, where channels change within one OFDMA symbol interval, and the time varying nature of CFOs, basis expansion modeling (BEM) is employed to represent the time variations of the channel. Two new approaches are then proposed based on Schmidt–Kalman filtering (SKF). The first approach utilizes Schmidt-extended Kalman filtering for each user to estimate CFO and BEM coefficients. The second approach uses Gaussian particle filtering along with SKF to estimate CFO and BEM coefficients of each user. The Bayesian Cramér–Rao bound is derived, and the performances of the new schemes are evaluated using the mean square errors. It is demonstrated that the new approaches can significantly improve the mean-square error performance in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

Motivated by recent developments in time-division duplex massive multiple-input multiple-output (MIMO) systems, this paper investigates semi-blind channel estimation for multiuser MIMO systems. An expectation-maximization (EM) algorithm is derived for semi-blind channel estimation and a tractable EM algorithm is obtained by assuming a Gaussian distribution for the unknown data symbols, which improves channel estimates even when the data symbols are drawn from a finite constellation, such as quadrature phase-shift keying. An alternate EM algorithm is also derived by employing suitable priors on the channel coefficients and it is shown to outperform the EM algorithm with no priors in the low signal-to-noise ratio regime. To analytically understand the performance of the semi-blind scheme, Cramer-Rao bounds (CRBs) for semi-blind channel estimation are derived for deterministic and stochastic (Gaussian) data symbol models. To get insight into the behavior of a massive MIMO system, the asymptotic behavior of the CRBs as the number of antennas at the base station grows is analyzed. The numerical results show the benefits of semi-blind estimation algorithm as measured by the mean squared error. In particular, the performance of the EM algorithm becomes closer to the genie-aided maximum likelihood estimator based on known data symbols as the number of antennas increases. This result is consistent with the asymptotic analysis of the two CRBs indicating that semi-blind channel estimation for massive MIMO systems is very promising. [ABSTRACT FROM AUTHOR]

For frequency division duplex channels, a simple pilot loop-back procedure has been proposed that allows the estimation of the uplink (UL) and downlink (DL) channel subspaces between an antenna array and a fully analog repeater. For this scheme, we derive the maximum likelihood (ML) estimators for the UL and DL subspaces, formulate the corresponding Cramér–Rao bounds, and show the asymptotic efficiency of both (singular value decomposition (SVD) based) estimators by means of Monte Carlo simulations. In addition, we illustrate how to compute the underlying (rank-1) SVD with quadratic time complexity by employing the power iteration method. To enable power control for the data transmission, knowledge of the channel gains is needed. Assuming that the UL and DL channels have on average the same gain, we formulate the ML estimator for the uplink channel vector norm, and illustrate its robustness against strong noise perturbations by means of simulations. [ABSTRACT FROM PUBLISHER]

Future multi-input multi-output (MIMO) wireless communications systems will use beamforming as a first-step towards realizing the capacity requirements necessitated by the exponential increase in data demands. The focus of this work is on beam alignment for time-division duplexing (TDD) systems, for which we propose a number of novel algorithms. These algorithms seek to obtain good estimates of the optimal beamformer/combiner pair (which are the dominant singular vectors of the channel matrix). They are motivated by the power method, an iterative algorithm to determine eigenvalues and eigenvectors through repeated matrix multiplication. In contrast to the basic power method which considers only the most recent iteration and assumes noiseless links, the proposed techniques consider information from all the previous iterations of the algorithm and combine them in different ways. The first technique (Sequential Least-Squares method) sequentially constructs a least-squares estimate of the channel matrix, which is then used to calculate the beamformer/combiner pair estimate. The second technique (Summed Power method) aims to mitigate the effect of noise by using a linear combination of the previously tried beams to calculate the next beam, providing improved performance in the low-SNR regime (typical for mmWave systems) with minimal complexity/feedback overhead. A third technique (Least-Squares Initialized Summed Power method ) combines the good performance of the first technique at the high-SNR regime with the low-complexity advantage of the second technique by priming the summed power method with initial estimates from the sequential method. [ABSTRACT FROM PUBLISHER]

We consider the problem of downlink training sequence design for frequency-division-duplex multiuser massive multiple-input multiple-output systems in the general case where users have distinct spatial correlations. The training sequences leverage spatial correlations and are designed to minimize the channel estimation weighted sum mean square error (MSE) under the assumption that users employ minimum MSE estimators. Noting that the weighted sum MSE function is invariant to unitary rotations of its argument, a solution is obtained using a steepest descent method on the Grassmannian manifold. We extend the proposed design to scenarios with temporal correlations combined with Kalman filters at the users, and design sequences exploiting the multiuser spatio-temporal channel structure. Finally, we consider scenarios where only a limited number of bits $B$ are available at the base station (BS) to inform the users of each chosen sequence, e.g., sequences are chosen from a set of $2^B$ vectors known to the BS and users, and develop a subspace version of matching pursuit techniques to choose the desired sequences. Simulation results using realistic channel models show that the proposed solutions improve user fairness with a proper choice of weights, lead to accurate channel estimates with training durations that can be much smaller than the number of BS antennas, and show substantial gains over randomly chosen sequences for even small values of $B$. [ABSTRACT FROM PUBLISHER]

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output system. Each receiver antenna of the base station is assumed to be equipped with a pair of one-bit analog-to-digital converters to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for both flat and frequency-selective fading, based on the Bussgang decomposition that reformulates the nonlinear quantizer as a linear function with identical first- and second-order statistics. The resulting channel estimator outperforms previously proposed approaches across all SNRs. We then derive closed-form expressions for the achievable rate in flat fading channels assuming low SNR and a large number of users for the maximal ratio and zero forcing receivers that takes channel estimation error due to both noise and one-bit quantization into account. The closed-form expressions, in turn, allow us to obtain insight into important system design issues such as optimal resource allocation, maximal sum spectral efficiency, overall energy efficiency, and number of antennas. Numerical results are presented to verify our analytical results and demonstrate the benefit of optimizing system performance accordingly. [ABSTRACT FROM PUBLISHER]

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subsequently, we develop a technique to design a constrained analog precoder that mimics the obtained unconstrained analog precoder under phase shifter constraints. The main idea is to adopt an additional baseband precoding matrix, which we call a compensation matrix. We analyze the SLNR loss due to the proposed hybrid precoding compared to fully digital precoding, and determine which factors have a significant impact on this loss. In the simulations, we show that if the channel is spatially correlated and the number of users is smaller than the number of RF chains, the SLNR loss becomes negligible compared to fully digital precoding. The main benefit of our method stems from the use of spatial channel matrices in such a way that not only is each user's desired signal considered, but also the inter-user interference is incorporated in the analog precoder design. [ABSTRACT FROM PUBLISHER]

This paper considers the optimal design of training sequences for channel estimation in large-scale multiple-input multiple-output orthogonal frequency-division multiplexing systems. The application scenario of interest is when the number of transmit antennas for the downlink (or the number of receive antennas for the uplink) is large, but not large enough to benefit the asymptotical optimality of using equipower training sequences (e.g., due to practical constraints on deployment costs, space, and antenna size). Under the criterion of minimizing the mean square error of the channel estimate, the optimal design of training sequences for such systems poses a truly large-scale optimization problem, to which existing optimization solvers are not applicable. We develop a fast convex programming (FCP) procedure to find its global optimal solution. In each iteration of the proposed FCP procedure, a solution is found in a scalable and closed form. The singularity and ill-conditionedness of the channel correlation matrices are also exploited to improve the computation efficiency. Furthermore, we also examine the design of reduced-length training sequences and develop a successive quadratic programming procedure to find the solutions. Intensive simulation results are provided to illustrate the performance of our methods. [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]

In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) are used to enhance the efficiency of simultaneous power transfer to multiple receivers (RXs) by constructively combining their induced magnetic fields, a technique termed “magnetic beamforming”. In this paper, we study the optimal magnetic beamforming design in a multiuser MIMO MRC-WPT system. We introduce and characterize the multiuser power region, which constitutes all the achievable power tuples for all RXs, subject to the given total power constraint over all TXs as well as their individual peak voltage and current constraints. For the special case without TX peak voltage and current constraints, we derive the optimal TX current allocation for the single-RX setup in closed-form and that for the multi-RX setup by applying the techniques of semidefinite relaxation (SDR) and time-sharing. In general, the problem is a nonconvex quadratically constrained quadratic programming (QCQP), which is difficult to solve. For the case of one single RX, we show that the SDR of the problem is tight and thus solve the problem efficiently. For the general case with multiple RXs, based on SDR we obtain two approximate solutions by applying the techniques of time-sharing and randomization, respectively. Moreover, we propose a new method to estimate the magnetic MIMO channel between TXs and RXs for practical implementation of magnetic beamforming. Numerical results show that our proposed magnetic channel estimation and adaptive beamforming schemes are practically effective and can significantly improve the power transfer efficiency and multiuser performance tradeoff in MIMO MRC-WPT systems compared with the benchmark scheme of uncoordinated WPT with fixed identical TXs' current. [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]

Hybrid analog/digital precoding is a promising technique to reduce the hardware cost of radio-frequency components compared with the conventional full-digital precoding approach in millimeter-wave multiple-input multiple output systems. However, the large antenna dimensions of the hybrid precoder design makes it difficult to acquire an optimal full-digital precoder. Moreover, it also requires matrix inversion, which leads to high complexity in the hybrid precoder design. In this paper, we propose a low-complexity optimal full-digital precoder acquisition algorithm, named beamspace singular value decomposition (SVD) that saves power for the base station and user equipment. We exploit reduced-dimension beamspace channel state information (CSI) given by compressive sensing (CS) based channel estimators. Then, we propose a CS-assisted beamspace hybrid precoding (CS-BHP) algorithm that leverages CS-based CSI. Simulation results show that the proposed beamspace-SVD reduces complexity by 99.4% compared with an optimal full-digital precoder acquisition using full-dimension SVD. Furthermore, the proposed CS-BHP reduces the complexity of the state-of-the-art approach by 99.6% and has less than 5% performance loss compared with an optimal full-digital precoder. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a channel estimation algorithm based on the received power measurements for the multi-antenna radio frequency (RF) energy transfer system. The energy beamforming is a key technology to resolve a low energy transfer efficiency problem of the RF energy transfer system, which is mainly caused by a free-space path loss. The channel gains between transmit and receive antennas should be estimated to use the energy beamforming technique. We propose a received power-based channel estimation scheme that can be implemented by means of a simple power meter at a receiver. In the proposed scheme, the receiver measures the received power of the RF signal and sends a feedback containing the received power measurements to the transmitter. We formulate a channel estimation problem, which is recast into a linear estimation problem by using complex vectorization. Then, this linear estimation problem is solved by the ordinary least squares method and the Kalman filter-based algorithm. We also propose a method to reduce the dimension of the estimation problem when there are a smaller number of receive antennas than transmit antennas. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement compared to the existing nonoverlapped designs and thus reduce the required channel estimation time. We develop a maximum likelihood estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon–Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using nonoverlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6 dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Massive MIMO is a variant of multiuser MIMO (Multi-Input Multi-Output) system, where the number of base-station antennas $M$ is very large and generally much larger than the number of spatially multiplexed data streams. Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal bandwidth of 10 to 100 MHz, requires very large sampling bit-rate and power consumption. To reduce complexity, Hybrid Digital-Analog architectures have been proposed. Our work in this paper is motivated by one of such schemes named Joint Spatial Division and Multiplexing (JSDM), where the downlink precoder (resp., uplink linear receiver) is split into product of a baseband linear projection (digital) and an RF reconfigurable beamforming network (analog), such that only m \ll M$ A/D converters and RF chains is needed. In JSDM, users are grouped according to similarity of their signal subspaces, and these groups are separated by the analog beamforming stage. Further multiplexing gain in each group is achieved using the digital precoder. Therefore, it is apparent that extracting the signal subspace of the M$ -dim channel vectors from snapshots of m$ -dim projections, with m \ll M antennas and, for a given $p \ll M$ , return a $p$-dim beamformer (subspace) that has a performance comparable with the best $p$ -dim beamformer designed from the full knowledge of the exact channel covariance matrix. We assess the performance of our proposed estimators both analytically and empirically via numerical simulations. [ABSTRACT FROM PUBLISHER]

The recent trends of densification and centralized signal processing in radio access networks suggest that future networks may comprise ubiquitous antennas coordinated to form a network-wide gigantic array, referred to as the ubiquitous array (UA). In this paper, the UA communication techniques are designed and analyzed based on a geometric model. Specifically, the UA is modeled as a continuous circular/spherical array enclosing target users and free-space propagation is assumed. First, consider the estimation of multiuser UA channels induced by user locations. Given single pilot symbols, a novel channel estimation scheme is proposed that decomposes training signals into Fourier/Laplace series and thereby translates multiuser channel estimation into peak detection of a derive function of location. The process is shown to suppress noise. Moreover, it is proved that estimation error due to interference diminishes with the increasing minimum user-separation distance following the power law, where the exponent is 1/3 and 1 for the circular and spherical UA, respectively. If orthogonal pilot sequences are used, channel estimation is found to be perfect. Next, consider channel-conjugate data transmission that maximizes received signal power. The power of interference between two users is shown to decay with the increasing user-separation distance sub-linearly and super-linearly for the circular and spherical UA, respectively. Furthermore, a novel multiuser precoding design is proposed by exciting different phase modes of the UA and controlling the mode weight factors to null interference. The number of available degrees of freedom for interference nulling using the UA is proved to be proportional to the minimum user-separation distance. [ABSTRACT FROM AUTHOR]

We address the problem of noise and interference corrupted channel estimation in massive MIMO systems. Interference, which originates from pilot reuse (or contamination), can in principle be discriminated on the basis of the distributions of path angles and amplitudes. In this paper, we propose novel robust channel estimation algorithms exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approaches are able to cope with a wide range of system and topology scenarios, including those where, unlike in previous works, interference channel may overlap with desired channels in terms of multipath angles of arrival or exceed them in terms of received power. In particular, we establish analytically the conditions under which the proposed channel estimator is fully decontaminated. Simulation results confirm the overall system gains when using the new methods. [ABSTRACT FROM AUTHOR]

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design. [ABSTRACT FROM AUTHOR]

In this paper, adaptive training beam sequence design for efficient channel estimation in large millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channels is considered. By exploiting the sparsity in large mmWave MIMO channels and imposing a Markovian random walk assumption on the movement of the receiver and reflection clusters, the adaptive training beam sequence design and channel estimation problem is formulated as a partially observable Markov decision process (POMDP) problem that finds non-zero bins in a two-dimensional grid. Under the proposed POMDP framework, optimal and suboptimal adaptive training beam sequence design policies are derived. Furthermore, a very fast suboptimal greedy algorithm is developed based on a newly proposed reduced sufficient statistic to make the computational complexity of the proposed algorithm low to a level for practical implementation. Numerical results are provided to evaluate the performance of the proposed training beam design method. Numerical results show that the proposed training beam sequence design algorithms yield good performance. [ABSTRACT FROM PUBLISHER]

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér–Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound. [ABSTRACT FROM AUTHOR]

In this paper, we derive the maximum a posteriori (MAP) symbol detector for a multiple-input multiple-output system in the presence of Wiener phase noise due to noisy local oscillators, and quasi-static fading channels. As in single-antenna systems, the computation of the optimum receiver is an analytically intractable problem and is unimplementable in practice. In this purview, we propose three suboptimal, low-complexity algorithms for approximately implementing the MAP symbol detector, which involve joint phase noise estimation and data detection. Our first algorithm is obtained by means of the sum-product algorithm, where we use the multivariate Tikhonov canonical distribution approach. In our next algorithm, we derive an approximate MAP symbol detector based on the smoother-detector framework, wherein the detector is properly designed by incorporating the phase noise statistics from the smoother. The third algorithm is derived based on the variational Bayesian framework. By simulations, we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions and observe that the proposed techniques significantly outperform the other important algorithms from prior works, which are considered herein. [ABSTRACT FROM PUBLISHER]

The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO systems, however, can be realized only when the quality of estimated channels is ensured at the transmitter and receiver side alike. In this paper, we introduce a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information from the decoders, the proposed channel estimator selects reliable data tones, subtracts interstream interferences, and performs re-estimation of the channels. We analyze the optimal data tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in system performance over the conventional channel estimators in realistic MIMO-OFDM scenarios. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Multi-antenna or multiple-input multiple-output (MIMO) techniques are appealing to enhance the transmission efficiency and range for radio frequency (RF) signal enabled wireless energy transfer (WET). In order to reap the energy beamforming gain in MIMO WET, acquiring the channel state information (CSI) at the energy transmitter (ET) is an essential task. This task is particularly challenging, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation. To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. Specifically, each feedback bit indicates the increase or decrease of the harvested energy by each ER in the present as compared to the previous intervals, which can be measured without changing the existing structure of the ER. Based on such feedback information, the ET adjusts transmit beamforming in subsequent training intervals and at the same time obtains improved estimates of the MIMO channels to different ERs by applying an optimization technique called analytic center cutting plane method (ACCPM). For the proposed ACCPM based channel learning algorithm, we analyze its worst-case convergence, from which it is revealed that the algorithm is able to estimate multiuser MIMO channels simultaneously without reducing the analytic convergence speed. Also, we provide extensive simulations to show its performances in terms of both convergence speed and energy transfer efficiency. [ABSTRACT FROM PUBLISHER]

We study the theoretical performance of two full-duplex multiple-input multiple-output (MIMO) radio systems: a full-duplex bi-directional communication system and a full-duplex relay system. We focus on the effect of a (digitally manageable) residual self-interference due to imperfect channel estimation (with independent and identically distributed (i.i.d.) Gaussian channel estimation error) and transmitter noise. We assume that the instantaneous channel state information (CSI) is not available the transmitters. To maximize the system ergodic mutual information, which is a nonconvex function of power allocation vectors at the nodes, a gradient projection algorithm is developed to optimize the power allocation vectors. This algorithm exploits both spatial and temporal freedoms of the source covariance matrices of the MIMO links between transmitters and receivers to achieve higher sum ergodic mutual information. It is observed through simulations that the full-duplex mode is optimal when the nominal self-interference is low, and the half-duplex mode is optimal when the nominal self-interference is high. In addition to an exact closed-form ergodic mutual information expression, we introduce a much simpler asymptotic closed-form ergodic mutual information expression, which in turn simplifies the computation of the power allocation vectors. [ABSTRACT FROM PUBLISHER]

To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance. [ABSTRACT FROM PUBLISHER]

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is reduced to minimize the overhead associated with phase noise estimation. New exact and closed-form expressions for the Bayesian Cramér–Rao lower bounds (BCRLBs) and soft-input maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing, estimation of phase noise over the length of a frame are derived. Simulations demonstrate that the proposed MAP estimators' mean-square error (MSE) performances are very close to the derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and complexity associated with tracking the phase noise processes over the length of a frame, a novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use soft statistics of the transmitted symbols given the current observations are proposed. Numerical results indicate that by employing the proposed phase tracking approach, the bit-error rate performance of a MIMO system affected by phase noise can be significantly improved. In addition, simulation results indicate that the proposed phase noise estimation scheme allows for application of higher order modulations and larger numbers of antennas in MIMO systems that employ imperfect oscillators. [ABSTRACT FROM PUBLISHER]

Through spatial multiplexing and diversity, multi-input multi-output (MIMO) cognitive radio (CR) networks can markedly increase transmission rates and reliability, while controlling the interference inflicted to peer nodes and primary users (PUs) via beamforming. The present paper optimizes the design of transmit- and receive-beamformers for ad hoc CR networks when CR-to-CR channels are known, but CR-to-PU channels cannot be estimated accurately. Capitalizing on a norm-bounded channel uncertainty model, the optimal beamforming design is formulated to minimize the overall mean-square error (MSE) from all data streams, while enforcing protection of the PU system when the CR-to-PU channels are uncertain. Even though the resultant optimization problem is non-convex, algorithms with provable convergence to stationary points are developed by resorting to block coordinate ascent iterations, along with suitable convex approximation techniques. Enticingly, the novel schemes also lend themselves naturally to distributed implementations. Numerical tests are reported to corroborate the analytical findings. [ABSTRACT FROM PUBLISHER]

This paper studies the MIMO applicability to filter bank based multicarrier (FBMC) modulations for low coherence bandwidth channels. Under these conditions the channel frequency response cannot be modeled flat at a subcarrier level. This implies that the techniques originally devised for OFDM do not restore the orthogonality between subcarriers when they are directly applied to FBMC. Aiming at circumventing this problem we propose the design of two MIMO FBMC schemes, which are based on a new subband processing. The figures of merit that govern the design of the first and second scheme are the signal to leakage plus noise ratio (SLNR) and the signal to interference plus noise ratio (SINR), respectively. However, we do not restrict the analysis to the design of a new subband processing but we also carry out an asymptotic analysis of the complexity as well as tackle the problem of estimating the channel. Simulation-based results have demonstrated that the addressed solutions clearly outperform previous MIMO FBMC schemes in terms of BER. In comparison to OFDM, the devised techniques are able to remain competitive even if the knowledge of the channel state information is not perfect. In those scenarios where the cyclic prefix (CP) length is not sufficiently large to avoid inter block interference, the proposed designs are able to reduce the BER. However, the price that should be paid is the increase of the complexity. [ABSTRACT FROM PUBLISHER]

Oscillator phase noise limits the performance of high speed communication systems since it results in time varying channels and rotation of the signal constellation from symbol to symbol. In this paper, joint estimation of channel gains and Wiener phase noise in multi-input multi-output (MIMO) systems is analyzed. The signal model for the estimation problem is outlined in detail and new expressions for the Cramér-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. A data-aided least-squares (LS) estimator for jointly obtaining the channel gains and phase noise parameters is derived. Next, a decision-directed weighted least-squares (WLS) estimator is proposed, where pilots and estimated data symbols are employed to track the time-varying phase noise parameters over a frame. In order to reduce the overhead and delay associated with the estimation process, a new decision-directed extended Kalman filter (EKF) is proposed for tracking the MIMO phase noise throughout a frame. Numerical results show that the proposed LS, WLS, and EKF estimators' performances are close to the CRLB. Finally, simulation results demonstrate that by employing the proposed channel and time-varying phase noise estimators the bit-error rate performance of a MIMO system can be significantly improved. [ABSTRACT FROM PUBLISHER]

This paper considers the context of orthogonal space-time block coded OFDM (OSTBC-OFDM) without channel state information at the receiver. Assuming noncoherent maximum-likelihood detection, the interest herein lies in detection within one OSTBC-OFDM block, motivated by its capability of accommodating relatively fast block fading channels. Our investigation focuses on analysis aspects, where we seek to establish practical noncoherent BPSK/QPSK OSTBC-OFDM schemes that have provably good channel identifiability and diversity properties. We consider perfect channel identifiability (PCI), a strong condition guaranteeing unique noncoherent channel identification for any (nonzero) channel. Through a judicious design involving special OSTBCs and pilot placement, we propose an OSTBC-OFDM scheme that is PCI-achieving and consumes fewer pilots compared to conventional pilot-aided channel estimation methods. We further our analysis by showing that a PCI-achieving scheme also achieves maximal noncoherent spatial diversity for the Kronecker Gaussian spatial-temporal channel fading model, which covers the popular i.i.d. Rayleigh fading channel and a variety of correlated and sparse multipath channels. All these results are developed in parallel for the centralized point-to-point MIMO scenario and a distributed relay communication scenario. For the latter scenario, our diversity analysis shows that the PCI-achieving scheme can also achieve maximal noncoherent cooperative diversity. The performance merits of the proposed PCI-achieving scheme are demonstrated by simulations. [ABSTRACT FROM PUBLISHER]

We consider coordinated linear precoding in downlink multicell multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) systems, where multiple base stations (BSs) coordinate to mitigate the co-channel interference. Two nonconvex rate optimization problems with per-cell power constraints are addressed: weighted sum rate maximization (WSRM) and maximizing the weighted sum of the minimal user rates (MWSMR) of the coordinated cells. We propose two iterative algorithms to achieve suboptimal solutions for the WSRM problem. One attempts to solve the Karush-Kuhn-Tucker (KKT) conditions of the problem and the other is an alternating maximization algorithm based on the idea of the classical Blahut-Arimoto algorithm. The distributed implementations of the two algorithms are also discussed. Moreover, an iterative algorithm based on alternating maximization is proposed to solve the MWSMR problem. Finally, simulation results are presented to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of full-duplex bidirectional communication between a pair of modems, each with multiple transmit and receive antennas. The principal difficulty in implementing such a system is that, due to the close proximity of each modem's transmit antennas to its receive antennas, each modem's outgoing signal can exceed the dynamic range of its input circuitry, making it difficult—if not impossible—to recover the desired incoming signal. To address these challenges, we consider systems that use pilot-aided channel estimates to perform transmit beamforming, receive beamforming, and interference cancellation. Modeling transmitter/receiver dynamic-range limitations explicitly, we derive tight upper and lower bounds on the achievable sum-rate, and propose a transmission scheme based on maximization of the lower bound, which requires us to (numerically) solve a nonconvex optimization problem. In addition, we derive an analytic approximation to the achievable sum-rate, and show, numerically, that it is quite accurate. We then study the behavior of the sum-rate as a function of signal-to-noise ratio, interference-to-noise ratio, transmitter/receiver dynamic range, number of antennas, and training length, using optimized half-duplex signaling as a baseline. [ABSTRACT FROM PUBLISHER]

Lattice reduction (LR) and successive interference cancellation (SIC) are two well-known techniques that can be used to improve detection performance over linear detectors for multiple-input multiple-output (MIMO) systems. However, the LR technique and the SIC technique usually need perfect knowledge on the channel at the receiver, and the use of these techniques with an erroneous channel matrix even worsens the detection performance compared to linear detectors. In this correspondence, we shall show how to modify these techniques to make them robust under imperfect channel estimation. Information needed for the proposed algorithm is moderate; the variance of channel estimation error is the only requirement. Furthermore, our algorithm is not sensitive to the error in the variance of channel estimation error. [ABSTRACT FROM AUTHOR]

Although equalizers promise to improve the signal- to-noise energy ratio, zero forcing equalizers are derived classically in a deterministic setting minimizing intersymbol interference, while minimum mean square error (MMSE) equalizer solutions are derived in a stochastic context based on quadratic Wiener cost functions. In this paper, we show that it is possible—and in our opinion even simpler—to derive the classical results in a purely deterministic setup, interpreting both equalizer types as least squares solutions. This, in turn, allows the introduction of a simple linear reference model for equalizers, which supports the exact derivation of a family of iterative and recursive algorithms with robust behavior. The framework applies equally to multiuser transmissions and multiple-input multiple-output (MIMO) channels. A major contribution is that due to the reference approach the adaptive equalizer problem can equivalently be treated as an adaptive system identification problem for which very precise statements are possible with respect to convergence, robustness and l2-stability. Robust adaptive equalizers are much more desirable as they guarantee a much stronger form of stability than conventional in the mean square sense convergence. Even some blind channel estimation schemes can now be included in the form of recursive algorithms and treated under this general framework. [ABSTRACT FROM PUBLISHER]

We derive and analyze two modified Kalman channel estimators (KCE) for time-varying, flat, spatially correlated MIMO channels in systems employing orthogonal space-time block codes: the steady-state KCE, which is less complex than the KCE, and the fading memory KCE, which is more robust to model mismatch. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we develop a sparse variational Bayesian (VB) extension of the space-alternating generalized expectation-maximization (SAGE) algorithm for the high resolution estimation of the parameters of relevant multipath components in the response of frequency and spatially selective wireless channels. The application context of the algorithm considered in this contribution is parameter estimation from channel sounding measurements for radio channel modeling purpose. The new sparse VB-SAGE algorithm extends the classical SAGE algorithm in two respects: i) by monotonically minimizing the variational free energy, distributions of the multipath component parameters can be obtained instead of parameter point estimates and ii) the estimation of the number of relevant multipath components and the estimation of the component parameters are implemented jointly. The sparsity is achieved by defining parametric sparsity priors for the weights of the multipath components. We revisit the Gaussian sparsity priors within the sparse VB-SAGE framework and extend the results by considering Laplace priors. The structure of the VB-SAGE algorithm allows for an analytical stability analysis of the update expression for the sparsity parameters. This analysis leads to fast, computationally simple, yet powerful, adaptive selection criteria applied to the single multipath component considered at each iteration. The selection criteria are adjusted on a per-component-SNR basis to better account for model mismatches, e.g., diffuse scattering, calibration and discretization errors, allowing for a robust extraction of the relevant multipath components. The performance of the sparse VB-SAGE algorithm and its advantages over conventional channel estimation methods are demonstrated in synthetic single-input–multiple-output (SIMO) time-invariant channels. The algorithm is also applied to real measurement data in a multiple-input–multiple-output (MIMO) time-invariant context. [ABSTRACT FROM PUBLISHER]

We consider a multicell frequency-selective fading uplink channel (network MIMO) from K single-antenna user terminals (UTs) to B cooperative base stations (BSs) with M antennas each. The BSs, assumed to be oblivious of the applied codebooks, forward compressed versions of their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since the BSs and the CS are assumed to have no prior channel state information (CSI), the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training, taking different path losses between the UTs and the BSs into account. We then study how the optimal training length is impacted by the backhaul capacity. Although our analytical results are based on a large system limit, we show by simulations that they provide very accurate approximations for even small system dimensions. [ABSTRACT FROM PUBLISHER]

Transmit beamforming (or precoding) is a powerful technique for enhancing performance of wireless multiantenna communication systems. Standard transmit beamformers require perfect channel state information at the transmitter (CSIT) and are sensitive to errors in channel estimation. In practice, such errors are inevitable due to finite feedback resources, quantization errors and other physical constraints. Hence, robustness has become a crucial issue recently. Among two popular robust designs, the stochastic approach exploits channel statistics and optimizes the average system performance while the maximin approach considers errors as deterministic and optimizes the worst case performance. The latter usually leads to a very conservative design against extreme (but rare) conditions which may occur at a very low probability. In this paper, we propose a more flexible approach that maximizes the average signal-to-noise ratio (SNR) and takes the extreme conditions into account using the probability with which they may occur. Simulation results show that the proposed beamformer offers higher robustness against channel estimation errors than several popular transmit beamformers. [ABSTRACT FROM PUBLISHER]

In this correspondence, we address statistically robust multiple-input multiple-output (MIMO) relay design problems under two imperfect channel state information (CSI) scenarios: 1) a ll nodes have imperfect CSI and 2) the destination node knows the exact CSI, while the other nodes have imperfect CSI. For each scenario, we develop robust source and relay matrices by considering a broad class of frequently used objective functions in MIMO system design and the averaged transmission power constraints. Simulation results demonstrate the improved robustness of the proposed algorithms against CSI errors. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) systems improve the spectral efficiency by allowing the coexistence in harmony of primary users (PUs), the legacy users, with secondary users (SUs). This coexistence is built on the premises that no SU can generate interference higher than some prescribed limits against PUs. The system design based on perfect channel state information (CSI) can easily end up violating the interference limits in a realistic situation where CSI may be imperfect. In this paper, we propose a robust design of CR systems, composed of multiple PUs and multiple noncooperative SUs, in either single-input single-output (SISO) frequency-selective channels or more general multiple-input multiple-output (MIMO) channels. We formulate the design of the SU network as a noncooperative game, where the SUs compete with each other over the resources made available by the PUs, by maximizing their own information rates subject to the transmit power and robust interference constraints. Following the philosophy of the worst-case robustness, we take explicitly into account the imperfectness of SU-to-PU CSI by adopting proper interference constraints that are robust with respect to the worst channel errors. Relying on the variational inequality theory, we study the existence and uniqueness properties of the Nash equilibria of the resulting robust games, and devise totally asynchronous and distributed algorithms along with their convergency properties. We also propose efficient numerical methods, based on decomposition techniques, to compute the robust transmit strategy for each SU. [ABSTRACT FROM AUTHOR]

This correspondence addresses the joint transceiver design for downlink multiuser multiple-input multiple-output (MIMO) systems, with imperfect channel state information (CSI) at the base station (BS) and mobile stations (MSs). By incorporating antenna correlation at both ends of the channel and taking channel estimation errors into account, we solve two robust design problems: minimization of the weighted sum mean-square-error (MSE) and minimization of the maximum weighted MSE. These problems are solved as follows: first, we establish three kinds of MSE uplink-downlink duality by transforming only the power allocation matrices from uplink channel to downlink channel and vice versa. Second, in the uplink channel, we formulate the power allocation part of each problem ensuring global optimality. Finally, based on the solution of the uplink power allocation and the MSE duality results, for each problem, we propose an iterative algorithm that performs optimization alternatively between the uplink and downlink channels. Computer simulations verify the robustness of the proposed design compared to the nonrobust/naive design. [ABSTRACT FROM PUBLISHER]