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Despite that underwater acoustic channels are well known to contain various interferences, research on interference mitigation in underwater acoustic communications has been very limited. In this paper, we deal with a wideband orthogonal frequency division multiplexing (OFDM) transmission in the presence of an external interference which occupies partially the signal band and whose time duration is shorter than the OFDM block. We parameterize the unknown interference waveform by a number of parameters assuming prior knowledge of the frequency band and time duration of the interference, and develop an iterative receiver, which couples interference detection via a generalized likelihood-ratio-test (GLRT), interference reconstruction and cancellation, channel estimation, and data detection. In addition to simulation results, we verify the receiver performance using data sets collected from two experiments. In both time-invariant and time-varying channels, the proposed iterative receiver achieves robust performance in the presence of unknown interference. [ABSTRACT FROM PUBLISHER]

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]

To combat the detrimental effects of the variability in wireless channels, we consider cross-layer rate adaptation based on limited feedback. In particular, based on limited feedback in the form of link-layer acknowledgements (ACK) and negative acknowledgements (NAK), we maximize the physical-layer transmission rate subject to an upper bound on the expected packet error rate. We take a robust approach in that we do not assume any particular prior distribution on the channel state. We first analyze the fundamental limitations of such systems and derive an upper bound on the achievable rate for signaling schemes based on uncoded QAM and random Gaussian ensembles. We show that, for channel estimation based on binary ACK/NAK feedback, it may be preferable to use a separate training sequence at high error rates, rather than to exploit low-error-rate data packets themselves. We also develop an adaptive recursive estimator, which is provably asymptotically optimal and asymptotically efficient. [ABSTRACT FROM PUBLISHER]

The communication between a multiple-antenna transmitter and multiple receivers (users) with either a single or multiple-antenna each can be significantly enhanced by providing the channel state information at the transmitter (CSIT) of the users, as this allows for scheduling, beamforming and multiuser multiplexing gains. The traditional view on how to enable CSIT has been as follows: In time-division duplexed (TDD) systems, uplink (UL) and downlink (DL) channel reciprocity allows the use of a training sequence in the UL direction, which is exploited to obtain an UL channel estimate. This estimate is in turn recycled in the next downlink transmission. In frequency-division duplexed (FDD) systems, which lack the UL and DL reciprocity, the CSIT is provided via the use of a dedicated feedback link of limited capacity between the receivers and the transmitter. In this paper, we focus on TDD systems and put their classical approach in question. We show that the traditional TDD setup above fails to fully exploit the channel reciprocity in its true sense. In fact, we show that the system can benefit from a combined CSIT acquisition strategy mixing the use of limited feedback and that of a training sequence. This combining gives rise to a very interesting joint estimation and detection problem for which we propose two iterative algorithms. An outage rate based framework is also developed which gives the resource split between training and feedback. We demonstrate the potential of this hybrid combining in terms of the improved CSIT quality under a global training and feedback resource constraint. [ABSTRACT FROM PUBLISHER]

We analyze the mean squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing binary phase-shift-keying (BPSK) modulation when the covariance matrix is estimated using a finite number of samples. The conventional estimator suffers from a phase ambiguity that reduces to a sign ambiguity for the WL estimator. We derive accurate closed-form expressions for the MSE of the two estimators under four ambiguity resolution scenarios. In the first three scenarios, the receiver resolves the ambiguity using some clairvoyant knowledge about the channel. The first scenario, used as a reference, is the ideal case of optimal resolution. The second scenario assumes that one of the channel coefficients is known and the third assumes knowledge of the coefficient with the largest magnitude. The fourth scenario considers the more realistic case where pilot symbols are employed for ambiguity resolution. Our work demonstrates that there is a strong relationship between the accuracy of ambiguity resolution and the relative performance of WL and conventional subspace-based estimators, showing that the WL estimator performs better when partial or inaccurate channel information is employed for ambiguity resolution. [ABSTRACT FROM PUBLISHER]

Robust and accurate time-of-arrival (TOA) estimation in dense multipath channels such as those encountered in ultra-wideband (UWB) systems is a considerable challenge especially when the signal-to-noise ratio (SNR) is low. The exact maximum likelihood (EML) TOA estimator in dense multipath conditions has the potential to attain accurate TOA estimation, however, it is too complex for practical implementation. There is a substantial performance gap between the known practical algorithms for TOA estimation and the EML estimator. In this paper, a novel practical TOA estimation algorithm is developed that attains the EML performance when the multipath arrivals are dense. When the multipath arrivals density is low the estimator does not attain the maximum likelihood performance but still outperforms other known practical estimators. The estimator does not need to know the channel characteristics accurately, thus, it is robust to various multipath channels. The approach taken is to approximate the received multipath signal as a Gaussian process and derive the maximum likelihood estimator. In order to further decrease the computational load of the new algorithm, we develop a low complexity approximation with negligible performance degradation. The algorithm is useful for either single channel realization or multiple channel realizations using diversity either in time, frequency or space. When applying diversity technique a substantial performance gain is attained due to the optimal combining of the channel realizations and thus reliable TOA estimation is attainable even at low SNR. The estimator's performance can be closely predicted by a closed-form analytical error expression. [ABSTRACT FROM PUBLISHER]

Multiple distributed nodes in cooperative networks generally are subject to multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs), which result in time varying channels and erroneous decoding. This paper seeks to develop estimation and detection algorithms that enable cooperative communications for both decode-and-forward (DF) and amplify-and-forward (AF) relaying networks in the presence of MCFOs, MTOs, and unknown channel gains. A novel transceiver structure at the relays for achieving synchronization in AF-relaying networks is proposed. New exact closed-form expressions for the Cramér–Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. Next, two iterative algorithms based on the expectation conditional maximization (ECM) and space-alternating generalized expectation-maximization (SAGE) algorithms are proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination. Though the global convergence of the proposed ECM and SAGE estimators cannot be shown analytically, numerical simulations indicate that through appropriate initialization the proposed algorithms can estimate channel and synchronization impairments in a few iterations. Finally, a maximum likelihood (ML) decoder is devised for decoding the received signal at the destination in the presence of MCFOs and MTOs. Simulation results show that through the application of the proposed estimation and decoding methods, cooperative systems result in significant performance gains even in presence of impairments. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of joint carrier frequency offset (CFO) and channel estimation for OFDM systems over the fast time-varying frequency-selective channel is explored within the framework of the expectation-maximization (EM) algorithm and parametric channel model. Assuming that the path delays are known, a novel iterative pilot-aided algorithm for joint estimation of the multipath Rayleigh channel complex gains (CG) and the carrier frequency offset (CFO) is introduced. Each CG time-variation, within one OFDM symbol, is approximated by a basis expansion model (BEM) representation. An autoregressive (AR) model is built to statistically characterize the variations of the BEM coefficients across the OFDM blocks. In addition to the algorithm, the derivation of the hybrid Cramér-Rao bound (HCRB) for CFO and CGs estimation in our context of very high mobility is provided. We show that the proposed EM has a lower computational complexity than the optimum maximum a posteriori estimator and yet incurs only an insignificant loss in performance. [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]

The blind identification of single-input multiple-output (SIMO) systems is often performed by exploiting a cross-relation (CR) between channel pairs. It has been shown that this property allows an estimation of channel impulse responses up to a common gain factor if certain identifiability conditions are met. In this case, the estimated channels can be evaluated by a gain-compensated system distance known as normalized projection misalignment (NPM). Current algorithms for blind channel identification, however, suffer in the presence of insufficient channel diversity and observation noise. In this paper, we first demonstrate that in the absence of noise the CR identification error for channels with exact common zeros is given by a single-channel pole-zero transfer function. Next, we extend our analysis to the realistic case of near-common zeros and noise for which we show that the effective error can still be approximated by a common transfer function as long as the distance between the channel zeros remains below a signal-to-noise ratio-dependent threshold. A finite impulse response (FIR) modeling of the error then enables us to define a common-filter-error-compensated system distance, termed normalized filter-projection misalignment (NFPM), which establishes a natural extension to the NPM analysis. By finally considering realistic channels, which we blindly estimate with the adaptive multichannel least mean-square (MCLMS) algorithm, we demonstrate that the NFPM reliably reaches the noise floor, confirming that the effective error can be approximated by a common FIR filter. [ABSTRACT FROM PUBLISHER]

In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. [ABSTRACT FROM PUBLISHER]

We investigate robust turbo equalization over frequency selective channels in the presence of channel uncertainties. The turbo equalization framework investigated here contains a linear equalizer to combat ISI and a trellis based decoder. However, instead of completely tuning the linear equalizer parameters to the available inaccurate channel information, a minimax scheme and a competitive scheme are studied, which incorporate the uncertainty in channel information to equalizer design in order to improve robustness. Approximate implementations of these methods are also presented with reduced computational complexity. The performance improvement obtained by the proposed algorithms are demonstrated through simulations under different scenarios. [ABSTRACT FROM PUBLISHER]

We address the problem of primary user detection in Cognitive Radio from a wideband signal comprising multiple primary channels, exploiting a priori knowledge about the primary network: channelization and spectral shape of primary transmissions. Using this second-order statistical information, a multichannel Gaussian model is formulated. In order to obtain a generalized likelihood ratio test, we first address maximum likelihood (ML) estimation of the power levels at the different channels, as well as of the noise variance. The ML conditions suggest a suboptimal closed-form estimate, which takes the form of a constrained least squares estimator whose asymptotic efficiency is shown for flat bandpass spectra in white noise, a case of practical importance. The resulting detectors exploit those frequency bins corresponding to guard bands and to primary channels perceived as weak to improve noise variance estimation. Analytical expressions for the probabilities of detection and false alarm are presented. Performance is evaluated via simulations in the setting of a terrestrial TV primary network with realistic channelization parameters. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the identification of linear systems' impulse responses modeled as deterministic multipath channels. In the class of channels we study, we consider the effect of delays, Doppler shifts, and different angles of arrival and departure for each signal path. We explore the efficacy of techniques based on sparse signal recovery, which typically define a basis constructed using a finite quantization grid over the parameter space, and approximate the impulse response as a sparse linear combination over such basis. Our goal is to provide guidelines on the design of pilot sequences that are sufficiently informative (SI), i.e., those inputs that guarantee identifiability of system impulse responses that fit in the sparse model. Inputs that are SI provide minimal requirements for uniquely identifying the system response. However, a smaller class of inputs leads to good mean squared estimation error in the presence of noise and modeling errors, due to the finite precision of the parameter space quantization. To single out the class of robust designs, we provide a new metric, called localized coherence, in lieu of the so called mutual coherence, as a measure for ranking SI designs in terms of robustness to noise and to modeling errors. [ABSTRACT FROM AUTHOR]

In this correspondence, we study the effect of channel estimation error to the reception reliability for decode–forward based two-way relaying and physical layer network coding. In particular, we focus on the scenario where the channel estimation error is inversely proportional to signal-to-noise ratio (SNR). To such a scenario, analytic results have been developed to show that symbol error probability can approach zero by increasing SNR and extra diversity gains can still be achieved by applying relay selection. [ABSTRACT FROM AUTHOR]

In this correspondence, the use of superimposed training (ST) as a mean to estimate the finite impulse response (FIR) components of a widely linear (WL) system is proposed. The estimator here presented is based on the first-order statistics of the signal observed at the output of the system and its variance is independent of the channel components if suitable designed training sequences are employed. The construction of such sequences having constant magnitude both in time and frequency domains is also addressed. [ABSTRACT FROM AUTHOR]

To fully exploit sea experiments under controlled and reproducible laboratory conditions, a channel model driven by real data is derived. This model relies on the assumption that a channel recorded at sea is a single observation of an underlying random process. From this single observation, the channel statistical properties are estimated to then feed a stochastic simulator that generates multiple realizations of the underlying process. Based on the analysis of data collected in the Atlantic Ocean and the Mediterranean Sea, we fully relax the usual wide-sense stationary uncorrelated scattering (WSSUS) assumption. We show thanks to the empirical mode decomposition that a trend stationary model suits the analyzed underwater acoustic communication channels very well. Scatterers with different path delays are also assumed to be potentially correlated so that the true second-order statistics of the channel are taken into account by our model. Test cases illustrate the benefits of channel stochastic replay to communication system design and validation. The Matlab code corresponding to the proposed simulator is available at http://perso.telecom- bretagne.eu/fxsocheleau/software . [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]

It has been recently shown that the expectation-maximization (EM) algorithm may be viewed as message passing in factor graphs, and in particular, for a linear Gaussian system (with unknown coefficients), the EM algorithm may be purely implemented with Gaussian message passing. In this work, with a Gaussian assumption of the data symbols and a Forney-style factor graph representation for single-carrier transmission over frequency selective channels, a Gaussian message passing EM approach for joint channel estimation and detection is developed. The complexity of the proposed approach grows logarithmically with the length of the observation vector, enabling an efficient handling of (quasi-static and time-varying) frequency selective channels with a large number of channel taps. [ABSTRACT FROM PUBLISHER]

This paper compares and analyzes the performance of distributed cophasing techniques for uplink transmission over wireless sensor networks. We focus on a time-division duplexing approach, and exploit the channel reciprocity to reduce the channel feedback requirement. We consider periodic broadcast of known pilot symbols by the fusion center (FC), and maximum likelihood estimation of the channel by the sensor nodes for the subsequent uplink cophasing transmission. We assume carrier and phase synchronization across the participating nodes for analytical tractability. We study binary signaling over frequency-flat fading channels, and quantify the system performance such as the expected gains in the received signal-to-noise ratio (SNR) and the average probability of error at the FC, as a function of the number of sensor nodes and the pilot overhead. Our results show that a modest amount of accumulated pilot SNR is sufficient to realize a large fraction of the maximum possible beamforming gain. We also investigate the performance gains obtained by censoring transmission at the sensors based on the estimated channel state, and the benefits obtained by using maximum ratio transmission (MRT) and truncated channel inversion (TCI) at the sensors in addition to cophasing transmission. Simulation results corroborate the theoretical expressions and show the relative performance benefits offered by the various schemes. [ABSTRACT FROM PUBLISHER]

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]

A frequency domain state-space approach to the least squares (LS) estimation for finite impulse response (FIR) system identification is proposed by exploiting the Toeplitz structure of the data matrix, leading to two low-complexity FFT (fast Fourier transform)-based unbiased recursive estimators. The proposed recursive estimators are applied to the smoothing estimation of time-varying frequency selective channels in turbo equalization systems, and channel estimation approaches with complexity \cal O(logL) per symbol are developed, where L is the number of channel taps. [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]

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]

Recently, new progress has been made in using basis expansion models for system identification with compressed sensing. To aid the application of these methodologies, we introduce a metric, called localized coherence, for choosing input signals that result in better estimation performance. Its definition is motivated through the analysis of the normalized mean Euclidean error of the channel estimate and its efficacy is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

An analysis of the exact bit error rate (BER) performance of orthogonal space-time block codes (OSTBCs) with maximum-likelihood detection in the presence of channel estimation errors is presented. The possibly correlated coefficients of the multiple-input multiple-output (MIMO) propagation channel are assumed to be affected by flat block fading and the transmitted symbols belong to a pulse amplitude modulation or quadrature amplitude modulation signal constellation. For both square and nonsquare OSTBCs, we derive approximate and exact BER expressions, irrespective of the distribution of the fading. It is also shown how the exact expressions can be efficiently and accurately evaluated using numerical integration techniques. Their application to the arbitrarily correlated Nakagami-m fading channel is presented and an efficient importance sampling technique is derived. As the high diversity order resulting from the application of OSTBCs gives rise to small BER values, the numerical evaluation of the presented BER expressions is much faster than straightforward Monte Carlo simulations. BER results have shown the impact of both imperfect channel estimation and antenna correlation on the performance of MIMO OSTBC systems. It is also shown that under highly correlated conditions, antenna correlation is the major source of BER degradation. [ABSTRACT FROM AUTHOR]

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]

In this paper, pilot-aided in-phase and quadrature-phase (IQ) imbalance compensation for orthogonal frequency division multiplexing (OFDM) systems operating over doubly selective channels is addressed. Based on a reformulated system model and the least squares (LS) criterion, a joint IQ imbalance and channel estimation method is developed, and the corresponding compensation scheme is also proposed. Moreover, to further enhance the compensation performance, an iterative compensation algorithm is derived via an expectation-maximization (EM) algorithm. Simulation results show that the iterative compensation algorithm initialized by the proposed LS compensation scheme converges in a few iterations and its performance after convergence is close to the ideal case with perfect IQ imbalance and channel state information. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of optimal transmitter design to achieve physical layer security for a multiple-input single-output (MISO) cognitive radio network (CRN), in which a secondary user transmitter (SU-Tx) sends confidential information to a SU receiver (SU-Rx) on the same frequency band with a primary user (PU) in the presence of an eavesdropper receiver (ED-Rx). It is assumed that all the channel state information (CSI) of the secondary, primary and eavesdropper channels is not perfectly known at the SU-Tx. The optimal transmitter design, under the restriction of Gaussian signaling without preprocessing of information, involves a nonconvex semiinfinite optimization problem which maximizes the rate of the secondary link while avoiding harmful interference to the PU and keeping the eavesdropper totally ignorant of the messages sent regardless of the uncertainties in the CSI. We propose two approaches to solve this challenging optimization problem. The first one relates the original problem to a sequence of semiinfinite capacity-achieving transmitter design problems in an auxiliary CRN without any eavesdropper, which can then be solved through transformations and using convex semidefinite programs (SDPs). The second approach explores the hidden convexity of the problem and hence transforms it into a single SDP, which significantly reduces the computational complexity. Furthermore, a few heuristic beamforming solutions for the ease of implementation are also introduced. Finally, simulation results are presented to evaluate the performance of the proposed optimal and suboptimal solutions. [ABSTRACT FROM PUBLISHER]

Channel order estimation is a critical task for blind system identification. The performance of the blind system identification algorithms depends on the accuracy and robustness of the channel order estimation. In this paper, a new effective channel order estimation algorithm with high accuracy and robustness is proposed for single-input multi-output (SIMO) systems. The proposed algorithm is guaranteed to find the true channel order for the noise-free case and it performs significantly better than the alternative algorithms for noisy observations. This algorithm shows a consistent performance when the number of observations, channels and channel order are changed. The proposed algorithm is integrated with the least squares smoothing (LSS) algorithm for blind identification of the channel coefficients. Comparisons are done with a variety of different algorithms including linear prediction (LP) based methods. It is shown that significant gain can be obtained compared to the alternative approaches in effective channel order estimation. [ABSTRACT FROM PUBLISHER]

Digital subscriber line (DSL) data rates for short loops are typically limited by crosstalk between adjacent lines rather than by background noise. Precoding can reduce crosstalk in the downstream from the access node to the customer premises equipment significantly if an accurate estimate of the inverse crosstalk channel is provided. Recently, a backward-compatible method has been proposed for estimating downstream crosstalk channels using standardized signal-to-noise ratio (SNR) reports. This paper develops a probabilistic model of the estimation process and, within this model, provides conditions under which successive updates of the precoder are guaranteed to converge to the ideal inverse precoder. Bounds on estimator variance and convergence times are obtained and optimized with respect to system parameters. The analysis can be applied to the situation in which a new line is being activated and added to a group of precoded lines seamlessly, that is, with controlled impact on the SNR of the active lines. Two phases are proposed to achieve seamless activation; the protection phase is used to let the active lines learn the crosstalk from the activating line and the acquisition phase is used to let the activating line learn the crosstalk from the active lines. Results of the analysis are illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Improving channel information quality at the base station (BS) is crucial for the optimization of frequency division duplexed (FDD) multi-antenna multiuser downlink systems with limited feedback. To this end, this paper proposes to estimate a particular representation of channel state information (CSI) at the BS through channel norm feedback and a newly developed channel phase codebook, where the long-term channel correlation is efficiently exploited to improve performance. In particular, the channel representation is decomposed into a gain-related part and a phase-related part, with each of them estimated separately. More specifically, the gain-related part is estimated from the channel norm and channel correlation matrix, while the phase-related part is determined using a channel phase codebook, constructed with the generalized Lloyd algorithm. Using the estimated channel representation, joint optimization of multiuser precoding and opportunistic scheduling is performed to obtain an SDMA transmit scheme. Computer simulation results confirm the advantage of the proposed scheme over state of the art limited feedback SDMA schemes under correlated channel environment. [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]

We consider the downlink of a multiuser system with multiple antennas at the base station. Vector perturbation (VP) precoding is a promising variant of transmit-side channel inversion allowing the users to detect their data in a simple, noncooperative manner. VP precoding has so far been developed and analyzed under the assumptions that the transmitter has perfect channel state information (CSI) and that the receivers know perfectly a channel-dependent transmit power normalization factor and have infinite dynamic range. We demonstrate that the violation of any of these idealizing assumptions degrades the performance of VP significantly and almost always results in an error floor. Motivated by this observation, we propose a novel scheme which we term transmit outage precoding (TOP). With TOP, the transmitter uses a prearranged power scaling known by the receivers and refrains from transmitting when channel conditions are poor. We further show how to augment TOP and conventional VP to deal with a finite dynamic range at the receiver. The performance of the proposed schemes under various levels of transmit CSI is studied in terms of a theoretical diversity analysis and illustrated by numerical results. [ABSTRACT FROM PUBLISHER]

Coordinated information processing by the base stations of multi-cell wireless networks enhances the overall quality of communication in the network. Such coordinations for optimizing any desired network-wide quality of service (QoS) necessitate the base stations to acquire and share some channel state information (CSI). With perfect knowledge of channel states, the base stations can adjust their transmissions for achieving a network-wise QoS optimality. In practice, however, the CSI can be obtained only imperfectly. As a result, due to the uncertainties involved, the network is not guaranteed to benefit from a globally optimal QoS. Nevertheless, if the channel estimation perturbations are confined within bounded regions, the QoS measure will also lie within a bounded region. Therefore, by exploiting the notion of robustness in the worst-case sense some worst-case QoS guarantees for the network can be asserted. We adopt a popular model for noisy channel estimates that assumes that estimation noise terms lie within known hyper-spheres. We aim to design linear transceivers that optimize a worst-case QoS measure in downlink transmissions. In particular, we focus on maximizing the worst-case weighted sum-rate of the network and the minimum worst-case rate of the network. For obtaining such transceiver designs, we offer several centralized (fully cooperative) and distributed (limited cooperation) algorithms which entail different levels of complexity and information exchange among the base stations. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we propose algorithms to jointly optimize the multiple multiantenna relays which assist multipoint-to-multipoint communications in wireless networks. Assuming that the knowledge of the second order statistics of the channels such as covariance matrices are available, the multiple-input-multiple-output (MIMO) relays are designed using two different methods: 1) minimize the sum of the powers of the relays while fulfilling the signal-to-interference-plus-noise ratio (SINR) requirements for all destinations and 2) maximize the minimum of the SINRs of all destinations satisfying the transmit power constraint of each MIMO relay. Furthermore, considering the fact that the covariance matrices of the channels between the MIMO relays and destinations are subject to uncertainty due to feedback and quantization errors, the robust versions of the aforementioned methods based on worst-case concept are proposed. It is shown that the proposed nonrobust as well as robust designs are nonconvex optimization problems but they can be solved accurately and efficiently using the standard semidefinite relaxation and randomization techniques. Computer simulations verify the improved performance of the robust designs over nonrobust methods. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of data-aided (DA) and nondata-aided (NDA) per-antenna signal-to-noise ratio (SNR) estimation over wireless single-input multiple-output (SIMO) channels from linearly modulated signals. Under constant channels and additive white Gaussian noise (AWGN), we first derive the DA maximum-likelihood (ML) SNR estimator in closed-form expression. The performance of the DA ML estimator is analytically carried out by deriving the closed-form expression of its bias and variance. Besides, in order to compare its performance with the fundamental limit, we derive the DA Cramér-Rao lower bound (CRLB) in closed-form expression. In the NDA case, the expectation-maximization (EM) algorithm is derived to iteratively maximize the log-likelihood function. The performance of the NDA ML estimator is empirically assessed using Monte Carlo simulations. Moreover, we introduce an efficient algorithm, which applies to any one/two-dimensional M-ary constellation, to numerically compute the NDA CRLBs. In this paper, the noise components are assumed to be spatially uncorrelated over all the antenna elements and temporally white. In both cases, we show that our new inphase and quadrature I/Q-based estimators offer substantial performance improvements over the single-input single-output (SISO) ML SNR estimator due to the optimal usage of the statistical dependence between the antenna branches, and that it reaches the corresponding CRLB over a wide SNR range. We also show that the use of the I/Q-based ML estimators can lead to remarkable performance improvements over the moment-based estimators for the same antenna-array size. Moreover, it is shown that SIMO configurations can contribute to decreasing the required number of iterations of the EM algorithm. [ABSTRACT FROM AUTHOR]

In this paper, linear transceiver design for dual-hop nonregenerative [amplify-and-forward (AF)] MIMO-OFDM systems under channel estimation errors is investigated. Second order moments of channel estimation errors in the two hops are first deduced. Then based on the Bayesian framework, joint design of linear forwarding matrix at the relay and equalizer at the destination under channel estimation errors is proposed to minimize the total mean-square-error (MSE) of the output signal at the destination. The optimal designs for both correlated and uncorrelated channel estimation errors are considered. The relationship with existing algorithms is also disclosed. Moreover, this design is extended to the joint design involving source precoder design. Simulation results show that the proposed design outperforms the design based on estimated channel state information only. [ABSTRACT FROM AUTHOR]

This paper proposes a training-based channel estimation scheme for achieving quality-of-service discrimination between legitimate and unauthorized receivers in wireless multiple-input multiple-output (MIMO) channels. The proposed method has applications ranging from user discrimination in wireless TV broadcast systems to the prevention of eavesdropping in secret communications. By considering a wireless MIMO system that consists of a multiple-antenna transmitter, a legitimate receiver (LR) and an unauthorized receiver (UR), we propose a multi-stage training-based discriminatory channel estimation (DCE) scheme that aims to optimize the channel estimation performance of the LR while limiting the channel estimation performance of the UR. The key idea is to exploit the channel estimate fed back from the LR at the beginning of each stage to enable the judicious use of artificial noise (AN) in the training signal. Specifically, with knowledge of the LR's channel, AN can be properly superimposed with the training data to degrade the UR's channel without causing strong interference on the LR. The channel estimation performance of the LR in earlier stages may not be satisfactory due to the inaccuracy of the channel estimate and constraints on the UR's estimation performance, but can improve rapidly in later stages as the quality of channel estimate improves. The training data power and AN power are optimally allocated by minimizing the normalized mean-square error (NMSE) of the LR subject to a lower limit constraint on the NMSE of the UR. The proposed DCE scheme is then extended to the case with multiple LRs and multiple URs. Simulation results are presented to demonstrate the effectiveness of the proposed DCE scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose robust equalizers based on a minimax mean-squares-error (MSE) scheme for wireless multi-input-multi-output (MIMO) communications subject to time-varying channel uncertainties. We consider channel uncertainties within a neighborhood of the estimated channel matrix formed by placing a bound on the spectral matrix norm of channel estimation errors. First, we derive a linear-matrix-inequality (LMI) based solution to the targeted problem. Next, we modify the MSE cost function of the targeted problem and derive a guaranteed cost-based solution, which may save computational cost. Subsequently, channel uncertainty is partitioned into finite Markov-transitioned channel uncertainty states based on the least upper bound of the set of the tightest upper bounds on the matrix spectral norm of channel uncertainty. This leads to a multiple model-based minimax MSE approach. On this basis, a feasible equalizer can be obtained from a weighted combination of multiple over-guaranteed cost-based equalizers, each of which is designed with respect to a channel uncertain state. Simulation results show that a weighted combination of a moderate number of multiple over-guaranteed cost-based equalizers can achieve robust equalization effectively with superior MSE and bit-error-rate (BER) performance. [ABSTRACT FROM PUBLISHER]

In this correspondence, we introduce a new training protocol for channel state estimation in wireless relay networks. The distinctive feature of our protocol is that each relay forwards the imperfect channel state information (CSI) to the destination. We show that our method allows to obtain at the destination a higher effective SNR and a faster transfer of the CSI compared with the other existing training protocols. [ABSTRACT FROM PUBLISHER]

We propose a novel equalization method for doubly selective wireless channels, whose taps are represented by an arbitrary Basis Expansion Model (BEM). We view such a channel in the time domain as a sum of product-convolution operators created from the basis functions and the BEM coefficients. Equivalently, a frequency-domain channel can be represented as a sum of convolution-products. The product-convolution representation provides a low-complexity, memory efficient way to apply the channel matrix to a vector. We compute a regularized solution of a linear system involving the channel matrix by means of the GMRES and the LSQR algorithms, which utilize the product-convolution structure without ever explicitly creating the channel matrix. Our method applies to all cyclic-prefix transmissions. In an OFDM transmission with K subcarriers, each iteration of GMRES or LSQR requires only \cal O(K\log K) flops and \cal O(K) memory. Additionally, we further accelerate convergence of both GMRES and LSQR by using the single-tap equalizer as a preconditioner. We validate our method with numerical simulations of a WiMAX-like system (IEEE 802.16e) in channels with significant delay and Doppler spreads. The proposed equalizer achieves BERs comparable to those of MMSE equalization, and noticeably outperforms low-complexity equalizers using an approximation by a banded matrix in the frequency domain. With preconditioning, the lowest BERs are obtained within 3–16 iterations. Our approach does not use any statistical information about the wireless channel. [ABSTRACT FROM PUBLISHER]

This paper addresses the data-aided (DA) signal-to-noise ratio (SNR) estimation for constant modulus modulations over time-variant flat Rayleigh fading channels. The time-variant fading channel is modeled by considering the Jakes' model and the first order autoregressive (AR1) model. Closed-form expressions of the Cramér–Rao bound (CRB) for DA SNR estimation are derived for known and unknown fast fading Rayleigh channels parameters cases. As special cases, the CRBs over slow and uncorrelated fading Rayleigh channels are derived. Analytical approximate expressions for the CRBs are derived for low and high SNR. These expressions that enable the derivation of a number of properties that describe the bound's dependence on key parameters such as SNR, channel correlation and sample number. Since the exact maximum likelihood (ML) estimator is computationally intensive in the case of fast-fading channels, two approximate ML estimator solutions are proposed for high and low SNR cases in the case of known channel parameters. The performances of theses estimators are examined analytically in terms of means and variances. In the presence of unknown channel parameters, a high SNR ML estimator based on the AR1 correlation model is derived. It is shown that the ML estimates of the SNR parameter and unknown channel parameters may be obtained in a separable form. Finally, simulation results illustrate the performance of the estimator and confirm the validity of the theoretical analysis. [ABSTRACT FROM PUBLISHER]

Relaying is one of the key technologies to satisfy the demands of future mobile communication systems. In particular, two-way relaying is known to exploit the radio resources in a very efficient manner. In this contribution, we consider two-way relaying with amplify-and-forward (AF) MIMO relays. Since AF relays do not decode the signals, the separation of the data streams has to be performed by the terminals themselves. For this task both nodes require reliable channel knowledge of all relevant channel parameters. Therefore, we examine channel estimation schemes for two-way relaying with AF MIMO relays. We investigate a simple Least Squares (LS) based scheme for the estimation of the compound channels as well as a tensor-based channel estimation (TENCE) scheme which takes advantage of the special structure in the compound channel matrices to further improve the estimation accuracy. Note that TENCE is purely algebraic (i.e., it does not require any iterative procedures) and applicable to arbitrary antenna configurations. Then we demonstrate that the solution obtained by TENCE can be improved by an iterative refinement which is based on the structured least squares (SLS) technique. In this application, between one and four iterations are sufficient and consequently the increase in computational complexity is moderate. The iterative refinement is optional and targeted for cases where the channel estimation accuracy is critical. Moreover, we propose design rules for the training symbols as well as the relay amplification matrices during the training phase to facilitate the estimation procedures. Finally, we evaluate the achievable channel estimation accuracy of the LS-based compound channel estimation scheme as well as the tensor-based approach and its iterative refinement via numerical computer simulations. [ABSTRACT FROM PUBLISHER]

This paper presents an iterative scheme for joint timing acquisition, multi-channel parameter estimation, and multiuser soft-data decoding. As an example, an asynchronous convolutionally coded direct-sequence code-division multiple-access system is considered. The proposed receiver is derived within the space-alternating generalized expectation-maximization framework, implying that convergence in likelihood is guaranteed under appropriate conditions in contrast to many other iterative receiver architectures. The proposed receiver iterates between joint posterior data estimation, interference cancellation, and single-user channel estimation and timing acquisition. A Markov Chain Monte Carlo technique, namely Gibbs sampling, is employed to compute the a posteriori probabilities of data symbols in a computationally efficient way. Computer simulations in flat Rayleigh fading show that the proposed algorithm is able to handle high system loads unlike many other iterative receivers. [ABSTRACT FROM PUBLISHER]

A practical approach for detecting packet-based orthogonal-frequency-division multiplexing (OFDM) signals in the presence of phase noise is presented. An OFDM packet consists of several OFDM symbols with full-pilot symbols at the beginning followed by consecutive data symbols. Based on the full-pilot OFDM symbol, a frequency-domain joint phase noise and channel vector estimator is first derived. It is shown that the phase noise vector can be estimated by maximizing a constrained quadratic form without requiring knowledge of the channel vector. This estimated phase noise vector is then used to compute the least squares channel estimator. Assuming that the channel is constant during each packet, the estimated channel is used in subsequent data OFDM symbols for equalization and data detection. Since phase noise changes from one OFDM symbol to the next, the scattered pilots in each data OFDM symbol are used to non-iteratively estimate and mitigate the phase noise induced interference. A significant improvement in the signal-to-interference-plus-noise ratio is achieved using the proposed algorithm. [ABSTRACT FROM PUBLISHER]

We present a new approach to the blind detection of full rate space-time block coded transmissions. Our method is based on linear programming and is globally convergent. We exploit the implicit structure of space-time block codes to cast the problem as linear programming to be solved efficiently. Unlike several known methods, the proposed technique is applicable to full-rate space-time block codes such as the popular Alamouti orthogonal code and the quasi-orthogonal space time code. Moreover, we utilize the code structure of these full rate codes to achieve simultaneous detection of all coded data streams without relying on successive schemes that are prone to error propagation. For orthogonal space-time codes, our algorithm allows near maximum likelihood detection of orthogonal codes without channel knowledge. We also can generalize the application of the proposed receiver to be used with nonorthogonal codes. Our proposed algorithm can provide performance competitive with existing techniques without imposing any conditions on the number of receive antennas. [ABSTRACT FROM PUBLISHER]

We consider the robust precoder design for multiuser multiple- input single-output (MU-MISO) systems where the channel state information (CSI) is fed back from the single antenna receivers to the centralized transmitter equipped with multiple antennas. We propose to compress the feedback data by projecting the channel estimates onto a vector basis, known at the receivers and the transmitter, and quantizing the resulting coefficients. The channel estimator and the basis for the rank reduction are jointly optimized by minimizing the mean-square error (MSE) between the true and the rank-reduced CSI. Expressions for the conditional mean and the conditional covariance of the channel are derived which are necessary for the robust precoder design. These expressions take into account the following sources of error: channel estimation, truncation for rank reduction, quantization, and feedback channel delay. As an example for the robust problem formulation, vector precoding (VP) is designed based on the expectation of the MSE conditioned on the fed-back CSI. Our results show that robust precoding based on fed-back CSI clearly outperforms conventional precoding designs which do not take into account the errors in the CSI. [ABSTRACT FROM AUTHOR]

A distributed estimation scheme where the sensors transmit with constant modulus signals over a multiple access channel is considered. The proposed estimator is shown to be strongly consistent for any sensing noise distribution in the i.i.d. case both for a per-sensor power constraint, and a total power constraint. When the distributions of the sensing noise are not identical, a bound on their variances is shown to establish strong consistency. The estimator is shown to be asymptotically normal with a variance (AsV) that depends on the characteristic function of the sensing noise. Optimization of the AsV is considered with respect to a transmission phase parameter for a variety of noise distributions exhibiting differing levels of impulsive behavior. The robustness of the estimator in the sense of degrading gracefully when the sensing noise deviates from the Gaussian distribution to impulsive sensing noise distributions such as those with positive excess kurtosis, or those that do not have finite moments is shown. The proposed estimator is favorably compared with the amplify and forward scheme under an impulsive noise scenario. The effect of fading and phase error at the sensors are shown to not affect the consistency of the estimator, but to degrade the asymptotic variance by terms that depend on the fading and phase error distributions. Simulations corroborate our analytical results. [ABSTRACT FROM PUBLISHER]