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Multiple antenna techniques that allow energy beamforming have been looked upon as a possible candidate for increasing the transfer efficiency between the energy transmitter (ET) and the energy receiver in wireless power transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing received signal strength indicator (RSSI) values to estimate the channel. First, in the training stage, the ET will transmit using each beamforming vector in a codebook, which is predefined using a Cramer-Rao lower bound analysis. RSSI value corresponding to each beamforming vector is fed back to the ET, and these values are used to estimate the channel through a maximum likelihood analysis. The results that are obtained are remarkably simple, requires minimal processing, and can be easily implemented. The paper also validates the analytical results numerically, as well as experimentally, and it is shown that the proposed method achieves impressive results. [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]

In this paper, we consider a distributed cophasing (DCP) technique in which multiple sensor nodes (SNs) communicating their observations to a fusion center (FC) need to transmit different classes of data requiring different levels of error protection. To achieve this, we propose a variant of the DCP technique with autonomous constellation selection at the different SNs. In the first stage of the two-stage time division duplexed cophasing scheme, the SNs obtain estimates of the channels to the FC using pilot symbols transmitted by the latter. Following this, the SNs simultaneously transmit their data symbols prerotated according to the estimated channel phases to combine coherently at the FC. The symbols transmitted by different SNs are drawn from possibly different constellations selected based on the estimated instantaneous channel gains. We show that this scheme is equivalent to transmitting symbols from hierarchical constellations. Based on the properties of hierarchical constellations, we develop recursive expressions for the BER of the proposed system. Following this, we use the properties of the effective channel coefficients to show that it is possible to recover the transmitted data bits from the signal received at the FC blindly, without requiring explicit pilot symbols to be sent by the power-starved SNs. We develop three blind channel estimation and data detection schemes for the presented system model. Using Monte Carlo simulations, we show that the proposed blind channel estimation algorithm achieves a probability of error performance close to that with genie aided perfect channel state information (CSI) at the FC, while using only a moderate number of unknown data symbols for channel estimation. [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]

Constrained combinatorial clustering (CCC) is a new approach for grouping multiple features where the clustering metric depends on an unknown communication channel assignment. Features assigned to the same channel cannot be from the same source and, conversely, channels assigned to the same source must be distinct. While the number of sources and their states are unknown, the channels are assumed to be known except for additive noise. Potential clustering assignments are checked for compatibility with the constraints in a structured way that results in significant computational savings with respect to exhaustive enumeration, especially when combined with a $K$-best channel assignment algorithm that has polynomial complexity. By combining two channel assignment methods (exhaustive and $K$ -best) with two clustering techniques (CCC and greedy), four new algorithms are presented to solve this novel problem, along with detailed computational complexity analyses. The main algorithm (CCC) is a top-down clustering strategy based on assignment aggregation in a channel constrained environment. The approaches are compared on a one-dimensional simulation. Significant performance differences in source estimation accuracy and estimated number of sources are observed at low signal-to-noise ratios (SNRs) and for low values of $K$. Some novel permutation symmetry properties arising from the study, which lead to a new type of self-affine set or discrete fractal, are also presented. [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]

Phase noise correction is crucial to exploit full advantage of orthogonal frequency-division multiplexing (OFDM) in modern high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, either have not taken into account the fundamental properties of phase noise or are only able to provide estimates of limited applicability owing to considerable computational complexity. In this paper, we have reformulated the joint phase noise and channel estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorization–minimization technique. In addition, we propose two methods based on dimensionality reduction and regularization, respectively, that can adapt to various phase noise levels and signal-to-noise ratio and achieve much lower estimation errors than the benchmarks without incurring much additional computational cost. Several numerical examples with phase noise generated by free-running oscillators or phase-locked loops demonstrate that our proposed algorithms outperform existing methods with respect to both computational efficiency and mean squared error within a large range of SNRs. [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]

A low-complexity precoding scheme and semiblind channel estimation method are proposed for amplify-and-forward two-way relay networks with multiple relays and frequency-selective fading channels. In the proposed approach, precoding is performed using a rotation-based matrix and the composite channel impulse response of each source-relay-destination link is estimated based on the second-order statistics of the received signals. The ambiguity in the proposed channel estimates caused by the channel information of the direct link is eliminated using a small number of training blocks. The accuracy of the proposed semiblind channel estimates is examined by deriving the mean square error of the channel estimates for two asymptotic cases, namely an infinite number of data blocks and a high signal-to-noise ratio (SNR) regime. The two cases, thus, provide a useful insight into the effects on the estimation performance of imperfect direct link information and statistical errors of the ensemble correlations, respectively. The asymptotic analyses are confirmed by the simulation results, which show that the normalized mean square error of the channel estimates varies inversely with the SNR and the number of data blocks. [ABSTRACT FROM PUBLISHER]

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, the pulse shapes vary throughout the signal. In this paper, we show how to sample and perfectly reconstruct Lorentzian pulses with variable width. In the noiseless case, perfect recovery is guaranteed by a set of theorems. In addition, we verify that our algorithm is robust to model mismatch and noise. This allows us to apply the technique to two practical applications: electrocardiogram (ECG) compression and bidirectional reflectance distribution function (BRDF) sampling. ECG signals are one dimensional, but the BRDF is a higher dimensional signal, which is more naturally expressed in a spherical coordinate system; this motivated us to extend the theory to the 2D and spherical cases. Experiments on real data demonstrate the viability of the proposed model for ECG acquisition and compression, as well as the efficient representation and low-rate sampling of specular BRDFs. [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]

This paper concentrates on a robust transmit optimization problem for the multiuser multi-input single-output (MISO) downlink scenario and under inaccurate channel state information (CSI). This robust problem deals with a general-rank transmit covariance design and follows a safe rate-constrained formulation under spherically bounded CSI uncertainties. Curiously, simulation results in previous works suggested that the robust problem admits rank-one optimal transmit covariances in most cases. Such a numerical finding is appealing because transmission with rank-one covariances can be easily realized by single-stream transmit beamforming. This gives rise to a fundamentally important question, namely, whether we can theoretically identify conditions under which the robust problem admits a rank-one solution. In this paper, we identify one such condition. Simply speaking, we show that the robust problem is guaranteed to admit a rank-one solution if the CSI uncertainties are not too large and the multiuser channel is not too poorly conditioned. To establish the aforementioned condition, we develop a novel duality framework, through which an intimate relationship between the robust problem and a related maximin problem is revealed. Our condition involves only a simple expression with respect to the multiuser channel and other system parameters. In particular, unlike other sufficient rank-one conditions that have appeared in the literature, ours is verifiable. The application of our analysis framework to several other CSI uncertainty models is also discussed. [ABSTRACT FROM PUBLISHER]

Energy beamforming (EB) is a key technique for achieving efficient radio-frequency transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they can be constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of EB waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain practically, especially in a distributed system with ETs at separate locations. In this paper, we study practical and efficient channel training methods to achieve optimal EB in a distributed WET system. We propose two protocols with and without centralized coordination, respectively, where distributed ETs either sequentially or in parallel adapt their transmit phases based on a low-complexity energy feedback from the ER. The energy feedback only depends on the received power level at the ER, where each feedback indicates one particular transmit phase that results in the maximum harvested power over a set of previously used phases. Simulation results show that the two proposed training protocols converge very fast in practical WET systems even with a large number of distributed ETs, while the protocol with sequential ET phase adaptation is also analytically shown to converge to the optimal EB design with perfect CSI by increasing the training time. Numerical results are also provided to evaluate the performance of the proposed distributed EB and training designs as compared to other benchmark schemes. [ABSTRACT FROM PUBLISHER]

Channel estimation in multicarrier systems employing filter banks with offset quadrature amplitude modulation (FBMC/OQAM) is far less easy compared to orthogonal frequency division multiplexing (OFDM). Challenges come from the interference effect, inherent in this type of FBMC systems, and the (generally) nonnegligible frequency selectivity of their subchannels, due to the nature of this modulation and the cyclic prefix (CP)-free transmission. Most of the FBMC/OQAM channel estimation methods rely on the assumption of a low frequency selective channel (thus implying (almost) flat subchannels) in order to more easily (i.e., in a manner analogous to CP-OFDM) tackle this problem. However, they perform quite poorly when applied in more demanding propagation conditions. Others, more recent ones, relax the assumption of low frequency selectivity but they either depend on a parametric model for the channel or they concentrate on persubchannel estimation and often require long training sequences. This paper revisits this problem through a formulation that does not rely on any simplifying assumption about the channel. This gives rise to a new estimation method, applicable to channels of any frequency selectivity, which relies on a very short preamble for training. The latter is optimized with respect to the channel estimation mean squared error and is shown to result in a very significant simplification of the estimation procedure. Both full (block-type) and sparse (comb-type) preambles are considered. Applying these results in low-dispersion channels yields, as special cases, existing and new preamble-based estimation schemes for such scenarios. The superiority of the proposed method over classical ones is demonstrated via simulation results for both mildly and highly frequency selective channels. [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]

The performance of the recently proposed high-resolution direct position determination method is analyzed. The method is based on minimum-variance-distortionless-response considerations to create a high-resolution spectrum (heat map) that enables single-step localization of multiple radio frequency emitters. Under the assumption of independent, identically distributed, circular, complex Gaussian snapshots, we derive an approximation to the estimation mean square error matrix. The approximation is an analytical expression, composed of variance and bias due to finite sample effects and asymptotic bias. Some of the obtained closed-form results are also relevant for other estimation techniques that combine independent minimum-variance-distortionless-response spectrums. Simulations are used to verify the usefulness of the theoretical expressions. [ABSTRACT FROM AUTHOR]

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 consider mean squared estimation with lookahead of a continuous-time signal corrupted by additive white Gaussian noise. We show that the mutual information rate function, i.e., the mutual information rate as function of the signal-to-noise ratio (SNR), does not, in general, determine the minimum mean squared error (MMSE) with fixed finite lookahead, in contrast to the special cases with 0 and infinite lookahead (filtering and smoothing errors), respectively, which were previously established in the literature. Further, we investigate the simple class of continuous-time stationary Gauss-Markov processes (Ornstein-Uhlenbeck processes) as channel inputs, and explicitly characterize the behavior of the minimum mean squared error (MMSE) with finite lookahead and signal-to-noise ratio (SNR). We extend our results to mixtures of Ornstein–Uhlenbeck processes, and use the insight gained to present lower and upper bounds on the MMSE with lookahead for a class of stationary Gaussian input processes, whose spectrum can be expressed as a mixture of Ornstein–Uhlenbeck spectra. [ABSTRACT FROM AUTHOR]

In this paper, a training design and channel estimation scheme is considered for uplink cloud radio access networks (C-RANs) consisting of multiple user equipments (UEs), remote radio heads (RRHs), and a centralized baseband unit (BBU) pool. Since most signal processing functions in C-RANs are moved from RRHs to the BBU pool, the individual channels over the links between UEs and RRHs and the links between RRHs and the BBU pool cannot be estimated directly. To address this issue, segment training based individual channel estimation for C-RANs is proposed in this paper, in which channel state information acquisition is performed through two consecutive segments. By using the Kalman filter, the sequential minimum mean-square-error (SMMSE) estimator is developed to efficiently estimate the individual channel states through prior knowledge of long-term channel correlation statistics and the latest radio channel state. A training structure design subject to a power constraint is obtained by minimizing the mean-square-error (MSE) of the SMMSE estimator. Since the MSE is insufficient to fully evaluate the overall performance of C-RANs, the uplink ergodic capacity is derived to exploit the impact of channel estimation on the data transmission by taking the estimation errors into consideration, and the tradeoff between the lengths of two segment training sequences is optimized by maximizing the corresponding spectral efficiency. Furthermore, the Cramér-Rao bound is used to evaluate the proposed SMMSE estimator’s performance. Simulation results show that the SMMSE estimator and the corresponding training design can effectively decrease MSE and significantly increase the quality and efficiency of data transmission in C-RANs. [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]

We consider the problem of power allocation for the single-cell multi-user (MU) multiple-input single-output (MISO) downlink with quality-of-service (QoS) constraints. The base station acquires an estimate of the channels and, for a given beamforming structure, designs the power allocation so as to minimize the total transmission power required to ensure that target signal-to-interference-and-noise ratios at the receivers are met, subject to a specified outage probability. We consider scenarios in which the errors in the base station’s channel estimates can be modelled as being zero-mean and Gaussian. Such a model is particularly suitable for time division duplex (TDD) systems with quasi-static channels, in which the base station estimates the channel during the uplink phase. Under that model, we employ a precise deterministic characterization of the outage probability to transform the chance-constrained formulation to a deterministic one. Although that deterministic formulation is not convex, we develop a coordinate descent algorithm that can be shown to converge to a globally optimal solution when the starting point is feasible. Insight into the structure of the deterministic formulation yields approximations that result in coordinate update algorithms with good performance and significantly lower computational cost. The proposed algorithms provide better performance than existing robust power loading algorithms that are based on tractable conservative approximations and can even provide better performance than robust precoding algorithms based on such approximations. [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, we propose a new blind frequency synchronization scheme for multiuser orthogonal-frequency-division multiplexing (OFDM) uplink transmissions. The spatial multiplexing is supported in the considered model that allows the subcarriers to be simultaneously occupied by multiple users, while the orthogonal frequency division multiple access (OFDMA) can serve as a special case of our discussion. We propose to assign different null subcarriers to different users and design algorithm that can perform blind carrier frequency offset (CFO) estimation for each individual user with the aid of large number of receive antennas, which then removes the necessity of multidimensional searching. After estimating CFO for one user, the orthogonal complement space of the other users can be obtained which could be further utilized for multiuser interference cancelation. Hence, the multiuser OFDM uplink model reduces to an equivalent single user signal model, where the blind channel estimation with scalar ambiguity can be designed and the conventional single-user data detection can be peformed. To make the proposed study complete, the closed-form performance analysis for mean square error (MSE) of the CFO estimation is derived, based on which the discussion about optimal null subcarrier assignment scheme is further provided. Finally, the Cramer–Rao bound (CRB) is derived, and the numerical results are provided to corroborate the proposed studies. [ABSTRACT FROM PUBLISHER]

One of the main challenges facing wireless sensor networks (WSNs) is the limited power resources available at small sensor nodes. It is therefore desired to reduce the power consumption of sensors while keeping the distortion between the source and its estimate at the fusion centre (FC) below a specific threshold. In this paper, we analyze a subset selection strategy to reduce the average transmission power of the WSN. We consider a two-hop network and assume the channels between the source and the relay sensors to be time-varying fading channels, modeled as Gilbert-Elliott channels. We show that when these channels are known at the FC, a subset of sensors can be selected by the FC to minimize transmit power while satisfying the distortion criterion. Through analysis, we derive the probability distribution of the size of this subset. We also consider practical aspects of implementing the proposed scheme, including channel estimation at relays. Through simulations, we compare the performance of the proposed scheme with schemes appearing in the literature. Simulation results confirm that for a certain range of end-to-end bit-error rates (BERs), the proposed scheme succeeds to achieve a superior power reduction compared to other schemes. [ABSTRACT FROM PUBLISHER]

In this paper, we develop frequency-domain phase noise estimation schemes for an orthogonal frequency division multiplexing (OFDM) system that utilize the geometrical structure associated with the spectral components of the complex exponential of the phase noise process. This geometry can be expressed as a set of non-convex quadratic equality constraints that involve permutation matrices. Since the oscillator power spectral density is typically a low-pass process, we can reduce the dimensionality of the phase noise spectral vector. In particular, we propose a new dimensionality reduction model that preserves the phase noise spectral geometry when moving from smaller to larger dimensional spaces. Using this new model and building upon existing works, the phase noise estimation problem is posed as an optimization problem. We show, using the S-procedure, that the optimization problem has an optimal solution that is attained and can be solved using semidefinite programming. In order to reduce the computational complexity of the aforementioned optimization problem, we consider sub-optimal schemes that provide an estimate satisfying this geometry. Through numerical simulations, we demonstrate superior bit-error-rate performance when the proposed model used in conjunction with the phase noise spectral geometry is incorporated in the estimation process. [ABSTRACT FROM PUBLISHER]

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]

We consider the problem of comparing two complex multivariate random signal realizations to ascertain whether they have identical power spectral densities. Past work on this problem is limited to either scalar signals or real-valued multivariate signals. A binary hypothesis testing approach is formulated and a generalized likelihood ratio test (GLRT) is derived. An asymptotic analytical solution for calculating the test threshold is provided. We analyze the performance of this GLRT by deriving an approximate asymptotic distribution of the test statistic under the alternative hypothesis. We also provide robustification of this approach by adding artificial white noise to the received noisy signals. The results are illustrated via computer simulations which include application to user authentication in wireless networks with multiantenna receivers. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for the artificial noise assisted secure transmission in multiple-input, multiple-output, multiple antenna eavesdropper (MIMOME) wiretap channels in frequency-division duplexed (FDD) systems. We focus on a practical scenario that only the eavesdroppers’ channel distribution information (CDI) is available and the imperfect channel state information (CSI) of the legitimate receiver is acquired through training and analog feedback. By taking explicitly into account the signaling overhead and training power overhead incurred by channel estimation and feedback, we define the achievable effective ergodic secrecy rate (ESR), and investigate a joint power allocation and training overhead optimization problem for the maximization of effective ESR. We first derive a deterministic approximation for the achievable effective ESR which facilitates the joint optimization. Then, efficient iterative algorithms are proposed to solve the considered nonconvex optimization problem. In particular, in the high-SNR regime, a block coordinate descent method (BCDM) is proposed to handle the joint optimization. In the low-SNR regime, we transform the problem into a sequence of geometric programmings (GPs) and locate its Karush–Kuhn–Tucker (KKT) solution using the successive convex approximation (SCA) method. For the general case of SNR, we maximize the lower bound of the achievable effective ESR. Simulation results corroborate the theoretical analysis and illustrate the secrecy performance of the proposed secure transmission scheme. [ABSTRACT FROM AUTHOR]

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]

The problem of signal parameter estimation and tracking with measurement data of low resolution is considered. In comparison to an ideal receiver with infinite receive resolution, the performance loss of a simplistic receiver with 1-bit resolution is investigated. For the case where the measurement data is preprocessed by a symmetric hard-limiting device with 1-bit output, it is well-understood that the performance for low SNR channel parameter estimation degrades moderately by 2/\pi(-\1.96~ dB). Here, we show that the 1-bit quantization loss can be significantly smaller if information about the temporal evolution of the channel parameters is taken into account in the form of a state-space model. By the analysis of a Bayesian bound for the achievable tracking performance, we attain the result that the quantization loss in dB is in general smaller by a factor of two if the channel evolution is slow. For the low SNR regime, this is equivalent to a reduced loss of \sqrt2/\pi(-\0.98 dB). By simulating non-linear filtering algorithms for a satellite-based ranging application and a UWB channel estimation problem, both with low-complexity 1-bit analog-to-digital converter (ADC) at the receiver, we verify that the analytical characterization of the tracking error is accurate. This shows that the performance loss due to observations with low amplitude resolution can, in practice, be much less pronounced than indicated by classical results. Finally, we discuss the implication of the result for medium SNR applications like channel estimation in the context of mobile wireless communications. [ABSTRACT FROM PUBLISHER]

Joint optimization of nonlinear precoders and receive filters is studied for both the uplink and downlink in a cellular system. For the uplink, the base transceiver station (BTS) receiver implements successive interference cancellation, and for the downlink, the BTS station pre-compensates for the interference with Tomlinson-Harashima precoding (THP). Convergence of alternating optimization of receivers and transmitters in a single cell is established when filters are updated according to a minimum mean squared error (MMSE) criterion, subject to appropriate power constraints. Adaptive algorithms are then introduced for updating the precoders and receivers in a distributed manner without channel state information, assuming time-division duplex transmissions with channel reciprocity. Instead of estimating the channels, the filters are directly estimated according to a least squares criterion via bi-directional training: Uplink pilots are used to update the feedforward and feedback filters, which are then used as interference pre-compensation filters for downlink training of the mobile receivers. Numerical results show that nonlinear filters can provide substantial gains relative to linear filters with limited forward-backward iterations. [ABSTRACT FROM PUBLISHER]

The impulse response of wireless channels between the N_t transmit and N_r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., the N_tN_r channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance. [ABSTRACT FROM PUBLISHER]

Recently, a structure of an optimal linear precoder for multi cell downlink systems has been described, and many other references have used simplified versions of this precoder to obtain promising performance gains. These gains have been hypothesized to stem from the additional degrees of freedom that allow for interference mitigation through interference relegation to orthogonal subspaces. However, no conclusive or rigorous understanding has yet been developed. In this paper, we build on an intuitive interference induction trade-off and the aforementioned preceding structure to propose an interference aware RZF (iaRZF) preceding scheme for multi cell downlink systems, and we analyze its rate performance. Special emphasis is placed on the induced interference mitigation mechanism of iaRZF. For example, we will verify the intuitive expectation that the precoder structure can either completely remove induced inter-cell or intra-cell interference. We state new results from large-scale random matrix theory that make it possible to give more intuitive and insightful explanations of the precoder behavior, also for cases involving imperfect channel state information (CSI). We remark especially that the interference-aware precoder makes use of all available information about interfering channels to improve performance. Even very poor CSI allows for significant sum-rate gains. Our obtained insights are then used to propose heuristic precoder parameters for arbitrary systems, whose effectiveness are shown in more involved system scenarios. Furthermore, calculation and implementation of these parameters does not require explicit inter base station cooperation. [ABSTRACT FROM PUBLISHER]

Carrier frequency offset (CFO) introduces spectrum misalignment of the transmit and receive filters, causing energy loss and distortion of received signal. A recent study presents an accurate signal model that incorporates these characteristics. However, CFO estimators in the existing literature were developed based on the signal models without these characteristics. This paper generalizes the accurate signal model with CFO to include timing offset and sampling offset and then studies what effects the accurate signal model bring to the CFO estimation and how to address them. We investigate different existing CFO estimators and find some of them lose their optimality under the accurate signal model. We also develop two new integer normalized CFO (ICFO) estimators under the accurate signal model using only one OFDM symbol; one of them is a pilot-aided estimator using both pilot and data and the other one is a blind estimator using data only. Compared with the existing approaches, both of the new estimators make better use of channel and energy loss information, thus yielding more accurate estimation. Furthermore, the proposed methods overcome the existing approaches’ limitation to phase shift keying modulation. Simulation results corroborate the advantages of the new estimators. Analytical performance results are also provided for the proposed estimators. [ABSTRACT FROM PUBLISHER]

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]

We consider the detection and estimation of a zero-mean Gaussian signal in a wireless sensor network with a coherent multiple access channel, when the fusion center (FC) is configured with a large number of antennas and the wireless channels between the sensor nodes and FC experience Rayleigh fading. For the detection problem, we study the Neyman–Pearson (NP) detector and energy detector (ED) and find optimal values for the sensor transmission gains. For the NP detector, which requires channel state information (CSI), we show that detection performance remains asymptotically constant with the number of FC antennas if the sensor transmit power decreases proportionally with the increase in the number of antennas. Performance bounds show that the benefit of multiple antennas at the FC disappears as the transmit power grows. The results of the NP detector are also generalized to the linear minimum mean-squared error estimator. For the ED, which does not require CSI, we derive optimal gains that maximize the deflection coefficient of the detector, and we show that a constant deflection can be asymptotically achieved if the sensor transmit power scales as the inverse square root of the number of FC antennas. Unlike the NP detector, for high sensor power, the multi-antenna ED is observed to empirically have significantly better performance than the single-antenna implementation. A number of simulation results are included to validate the analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we examine the Bayesian Cramér–Rao lower bounds (BCRBs) for the channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under the time-selective flat-fading scenario, where the superimposed training is adopted at the relay node in order to achieve the individual channel estimation. We formulate the nonlinear dynamic state space for the individual channels and derive the online/offline BCRBs for the fully data-aided (FDA) channel estimator and the partially data-aided (PDA) estimator. The former estimator has full knowledge about the symbols from both the source and the relay, while the latter one has imperfect statistical information about the data of the source, and possesses full information of the symbols superimposed by the relay. For the FDA scenario, we calculate the closed-form online/offline BCRBs and analyze the effect of the nodes' mobility on the BCRB performance, while for the PDA case, with the assumption of square QAM constellation set at the source, we design one framework to numerically evaluate online/offline BCRBs and analyze the asymptotic BCRBs at high SNRs particularly for the 4-QAM constellation set. Finally, numerical results are provided to corroborate the proposed studies. [ABSTRACT FROM PUBLISHER]

This work addresses channel estimation in multiple antenna multicell interference-limited networks. Channel state information (CSI) acquisition is vital for interference mitigation. Wireless networks often suffer from multicell interference, which can be mitigated by deploying beamforming to spatially direct the transmissions. The accuracy of the estimated CSI plays an important role in designing accurate beamformers that can control the amount of interference created from simultaneous spatial transmissions to mobile users. Therefore, a new technique based on the structure of the spatial covariance matrix and the discrete cosine transform (DCT) is proposed to enhance channel estimation in the presence of interference. Bayesian estimation and least squares estimation frameworks are introduced by utilizing the DCT to separate the overlapping spatial paths that create the interference. The spatial domain is thus exploited to mitigate the contamination which is able to discriminate across interfering users. Gains over conventional channel estimation techniques are presented in our simulations which are also valid for a small number of antennas. [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]

Channel state feedback is a serious burden that limits deployment of transmit beamforming systems with many antennas in frequency-division duplex (FDD) mode. Transmit beamforming with limited feedback systems estimate the channel at the receiver and send quantized channel state or beamformer information to the transmitter. A different approach that exploits the spatio-temporal correlation of the channel is proposed here. The transmitter periodically sends a beamformed pilot signal in the downlink, while the receiver quantizes the corresponding received signal and feeds back the bits to the transmitter. Assuming an autoregressive (AR) channel model, Kalman filtering (KF) based on the sign of innovations (SOI) is proposed for channel tracking, and closed-form expressions for the channel estimation mean-squared error (MSE) are derived under certain conditions. For more general channel models, a novel tracking approach is proposed that exploits the quantization bits in a maximum a posteriori (MAP) formulation. Simulations show that close to optimum performance can be attained with only 2 bits per channel dwell time block, even for systems with many transmit antennas. This clears a hurdle for transmit beamforming with many antennas in FDD mode—which was almost impossible with the prior state-of-art. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a multi-cell MISO downlink, where a multi-antenna base station (BS) sends messages to its intended user, while a passive eavesdropper tries to intercept the information in each cell. Physical layer security is employed in order to guarantee information security. Due to incomplete channel state information (CSI) at BSs, residue inter-cell interference exists even with coordinated beamforming, which exerts a great impact on the secrecy performance. In this paper, we conduct a unified performance analysis for physical layer security with incomplete CSI, and derives closed-form expressions of ergodic secrecy rate, secrecy outage probability, and interception probability, considering CSI estimation accuracy, transmit signal to noise ratio (SNR), and channel condition. We reveal some insights on the secrecy performance through asymptotic analysis. Furthermore, based on the derived theoretical results, we propose a CSI exchange amount allocation scheme. Finally, the simulations validate our theoretical claims. [ABSTRACT FROM AUTHOR]

This paper investigates pilot-aided channel estimation for two-way relay networks (TWRNs) in the presence of synchronization errors between the two sources. The unpredictable synchronization error leads to time domain offset and signal arriving order (SAO) ambiguity when two signals sent from two sources are superimposed at the relay. A two-step channel estimation algorithm is first proposed, in which the linear minimum mean-square-error (LMMSE) estimator is used to obtain initial channel estimates based on pilot symbols and a linear minimum error probability (LMEP) estimator is then developed to update these estimates. Optimal training sequences and power allocation at the relay are designed to further improve the performance for LMMSE based initial channel estimation. To tackle the SAO ambiguity problem, the generalized likelihood ratio testing method is applied and an upper bound on the SAO detection error probability is derived. By using the SAO information, a scaled LMEP estimation algorithm is proposed to compensate for the performance degradation caused by SAO detection error. Simulation results show that the proposed estimation algorithms can effectively mitigate the negative effects caused by asynchronous transmissions in TWRNs, thus significantly outperforming the existing channel estimation algorithms. [ABSTRACT FROM AUTHOR]