This paper introduces a novel hybrid analog/digital transceiver design for full-duplex (FD) integrated sensing and communication (ISAC) systems operating at mmWave band. The proposed scheme simultaneously supports downlink (DL) and uplink (UL) multiuser communication along with monostatic sensing, while suppressing the self-interference (SI). Considering that high SI levels may lead to saturation at low-noise amplifiers (LNAs), our design incorporates two SI mitigation constraints: one imposed at the receiver (RX) antennas before LNAs and another after the analog combining stage before analog-to-digital converters (ADCs). By formulating optimization problems that balance the trade-offs between spectral efficiency and beam-pattern error, we leverage a projected gradient ascent (PGA) algorithm with penalty-based methods to iteratively design hybrid precoders and combiners. Simulation results show that the proposed architecture strikes a balance between communication and sensing performance while effectively mitigating SI.

Practical Orthogonal Time-Frequency-Space (OTFS) systems face excessive out-of-band radiation (OBR), which degrades overall network performance. We investigate the application of spectral precoding to cyclic prefix-based OTFS to mitigate OBR in high-mobility wireless channels, and propose a modification of the low-complexity delay-time domain maximal ratio combining iterative decoder to account for the spectral precoding operation at the transmitter. Simulation results demonstrate the effectiveness of the proposed scheme to mitigate OBR and improve bit error rate, showcasing its potential for high-mobility OTFS applications.

Designing energy-efficient beamformers is a major challenge to the deployment of battery-powered devices transmitting short data packets at millimeter-wave frequencies. In general, directly maximizing energy efficiency will not meet the stringent reliability requirements of certain Internet-of-Things applications. We focus on designing analog beamformers in a device-to-device setting under short packets, a finite encoder set, and imperfect channel knowledge. To guarantee the required error decoding performance, a two-step procedure is proposed such that beamformers are designed to minimize the power consumption subject to reliability constraints under worst-case channel estimation errors, and then the channel encoder maximizing the energy efficiency is selected. The proposed design enjoys a moderate loss under practical low-resolution phase shifters, and exhibits robustness to inaccurate channel estimators, which is crucial to the tradeoff between energy efficiency and error decoding performance.

Next generation communication systems require accurate beam alignment to counteract the impairments that characterize propagation in high-frequency bands. The overhead of the pilot sequences required to select the best beam pair is prohibitive when codebooks contain a large number of beams, as is the case in practice. To remedy this issue, some schemes exploit information about the user location to predict the best beam pair. However, these schemes (i) involve no measurements whatsoever, which generally results in a highly suboptimal predicted beam, and (ii) are not robust to localization errors. To address these limitations, this paper builds upon the notion of radio map to develop two algorithms that attain a balance between the quality of the obtained beam pair and measurement overhead. The proposed algorithms predict the received power corresponding to each pair and measure just the$Q$pairs with highest prediction. While the first algorithm targets simplicity, the second one relies on a Bayesian approach to endow the prediction process with robustness to localization error. The performance of both algorithms is shown to widely outperform existing methods using ray-tracing data.

Reducing the large sidelobes of multicarrier signals is crucial to prevent adjacent channel interference. Spectral precoding is an effective approach toward this goal, at the expense of throughput loss due to precoder redundancy; thus, it is of interest to explore alternative precoder designs with improved performance at lower redundancies. We present a novel precoder which minimizes radiated power within a user-selectable frequency region. The structure of the precoding matrix is chosen to allow efficient mitigation of in-band distortion at the receiver by means of iterative and successive interference cancellation, while completely avoiding distortion to protected and pilot subcarriers. By exploiting the low-rank properties of constituent blocks, computational complexity can be significantly reduced with little impact on sidelobe reduction. Simulation results show the benefits of the proposed design, which is particularly effective in redundancy-limited settings targeting high spectral efficiency.

We study beamforming cancellation for a full-duplex (FD) wideband millimeter wave (mmWave) point-to-point bidirectional link in which both multicarrier-based nodes transmit and receive simultaneously and on the same frequency. The focus is on hybrid architectures in which the analog subblock of the beamformer, common to all subcarriers, is fully-connected and based on phase shifters with finite resolution, to avoid frontend saturation by mitigating FD-induced self-interference in the analog domain.

Enabling simultaneous transmission and reception on the same carrier frequency, i.e., full-duplex (FD) communication, critically requires that the self-interference (SI) be sufficiently suppressed. We investigate the design of precoders and combiners for an FD millimeter wave point-to-point bidirectional link, where dense antenna arrays provide an opportunity for SI suppression via beamforming cancellation. In practice the precoders and combiners are usually implemented in a hybrid digital/analog fashion with a reduced number of radio frequency (RF) chains, and it becomes necessary to overcome a number of limitations imposed by this structure. We focus on a hybrid architecture with a phase shifter-based fully-connected analog stage, assuming finite-resolution phase shifters. To avoid analog-to-digital converter saturation at the receiver, the proposed design aims at cancelling SI at the analog combiner output. Although accurate SI suppression is difficult with low-resolution phase shifters, our design is able to provide reasonable performance, significantly improving upon previous approaches, and allowing to efficiently exploit the availability of RF chains to partially compensate for coarse phase quantization effects.

Configuring precoders and combiners is a major challenge to deploy practical multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication systems with large antenna arrays. Most prior work addresses the problem focusing on a total transmit power constraint. In practical transmitters, however, power amplifiers must operate within their linear range, so that a power constraint applies to each one of the input signals to these devices. Therefore, precoder and combiner designs should incorporate per-antenna or per-radio frequency (RF) chain transmit power constraints. We focus on such problem for frequency-selective channels with multicarrier modulation, and assuming hybrid analog/digital architectures based on fully connected analog blocks implemented with finite-resolution phase shifters. We first derive an all-digital solution which aims to maximize spectral efficiency. Then, we develop hybrid precoders and combiners by approximately matching the corresponding all-digital matrices while still enforcing the power constraints. Numerical results show that the proposed all-digital design performs close to the upper bound given by the standard waterfilling-based solution with a total power constraint. Additionally, the hybrid designs exhibit a moderate loss even when low-resolution phase shifters are considered.

We consider Full-Duplex (FD) communication in millimeter wave (mmWave) bands. The dense antenna arrays available in mmWave offer a large number of degrees of freedom which could be exploited towards self-interference (SI) mitigation in FD. However, analog beamforming architectures based on practical phase shifters make this task quite challenging due to the lack of amplitude control and finite phase resolution. In this context, we consider a single-stream bidirectional point-to-point mmWave FD link, and develop a new analog beamforming design requiring a single radio frequency chain in each direction at each node, explicitly taking into account such hardware-related constraints. Simulation results show that the proposed scheme significantly outperforms previous methods for practical phase shifter resolution values of a few bits, while providing SI cancellation in the analog domain and thus avoiding RF frontend saturation.

Orthogonal precoding constitutes a powerful technique to reduce spectrum sidelobes of multicarrier signals. This reduction is bought at the cost of introducing precoder redundancy, which results in some throughput loss and additional precoding/decoding complexity. When the goal is to meet some spectral emission mask constraints, it is desirable to avoid unnecessary sidelobe suppression in order to keep precoder redundancy at a minimum. In this context, we introduce a general framework under which we develop a novel Lagrange multiplier-based mask-compliant orthogonal precoder design targeting minimal redundancy. We also adapt to this framework two previously proposed designs based on spectral notches and minimum out-of-band emission, respectively, to explicitly incorporate mask constraints. Simulation results are provided to show the effectiveness of the proposed designs under different practical masks for multicarrier wireless systems.

Windowing is an effective approach to reduce out-of-band radiation (OBR) in multicarrier systems in order to avoid adjacent channel interference. However, commonly used window functions are chosen in an ad hoc manner and fixed. We present an optimal window design for windowed-OFDM minimizing OBR within a given frequency region. The proposed technique is flexible in that it allows to specify the range of frequencies involved in the minimization and to apply spectral weighting. The optimal window can be computed offline.

Spectral precoding is a popular approach to reduce out-of-band radiation (OBR) in multicarrier systems in order to avoid adjacent channel interference. Since precoding will introduce signal distortion, appropriate decoding is required at the receiver side. We present a novel linear precoder design with flexibility to trade off OBR reduction, precoding/decoding complexity, and error rate at the receiver. The precoding matrices have low rank, which translates into significant computational savings. In this way, the requirements of different systems can be satisfied with varying complexity levels.

Configuring hybrid precoders and combiners is the main challenge to be solved to operate at millimeter wave (mmWave) frequencies. The use of hybrid architectures imposse hardware constraints on the analog precoder that need to be carefully dealt with. In this paper, we develop hybrid precoders and combiners aiming at minimizing the Euclidean distance with respect to the approximate all-digital precoders and combiners maximizing the spectral efficiency under per-antenna power constraints. Numerical results demonstrate the effectiveness of the proposed design method, whose performance is close to that of the all-digital solution.

We propose a novel mode of operation for amplify-and-forward relays in which the spectra of the relay input and output signals partially overlap. This partial-duplex relaying mode encompasses half- and full-duplex as particular cases. By viewing the partial-duplex relay as a bandwidth-preserving linear periodically time-varying system, a spectral efficiency analysis under self-interference is developed. In contrast with previous works, self-interference is regarded as a useful information-bearing component rather than simply assimilated to noise. This approach reveals that previous results regarding the impact of self-interference on (full-duplex) relay performance are overly pessimistic. Based on a frequency-domain interpretation of the effect of self-interference, a number of suboptimal decoding architectures at the destination node are also discussed. It is found that the partial-duplex relaying mode may provide an attractive tradeoff between spectral efficiency and receiver complexity.

This work addresses the problem of determining whether two multivariate random time series have the same power spectral density (PSD), which has applications, for instance, in physical-layer security and cognitive radio. Remarkably, existing detectors for this problem do not usually provide any kind of optimality. Thus, we study here the existence under the Gaussian assumption of optimal invariant detectors for this problem, proving that the uniformly most powerful invariant test (UMPIT) does not exist. Thus, focusing on close hypotheses, we show that the locally most powerful invariant test (LMPIT) only exists for univariate time series. In the multivariate case, we prove that the LMPIT does not exist. However, this proof suggests two LMPIT-inspired detectors, one of which outperforms previously proposed approaches, as computer simulations show.

This paper deals with the problem of beamformer design in a multibeam satellite, which is shared by several clusters of terminals, each served by an earth station, or gateway. Each gateway precodes the symbols addressed to its respective users; the design follows an MMSE criterion, and a judiciously chosen regularization factor accounts for the presence of mutually interfering clusters, extending more classical results applicable to the case of a single centralized station. More importantly, channel statistics can be used instead of instantaneous channel state information, avoiding the exchange of information among gateways through backhaul links. The on-board satellite beamforming weights are designed to exploit the degrees of freedom of the satellite antennas to minimize the noise impact and the interference to some specific users. On-ground beamforming results are provided as a reference to compare the joint performance of MMSE precoders and on-board beamforming network. A non-adaptive design complements the results and makes them more amenable to practical use by designing a coarse beamforming network.

Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all annotators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft assignments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.

We consider the problem of estimating the frame error rate (FER) of a given memoryless binary symmetric channel by observing the success or failure of transmitted packets. Whereas FER estimation is relatively straightforward if all observations correspond to packets with equal length, the problem becomes considerably more complex when this is not the case. We develop FER estimators when transmissions of different lengths are observed, together with the Cramer-Rao lower bound (CRLB). Although the main focus is on maximum likelihood (ML) estimation, we also obtain low-complexity schemes performing close to optimal in some scenarios. In a second stage, we consider the case in which FER estimation is performed at a node different from the receiver, and incorporate the impairment of unreliable observations by considering noisy ACK/NAK feedback links. The impact of unreliable feedback is analyzed by means of the corresponding CRLB. In this setting, the ML estimator is obtained by applying the expectation-maximization algorithm to jointly estimate the error probabilities of the data and feedback links. Simulation results illustrate the benefits of the proposed estimators.

This paper considers the design of hybrid precoders and combiners for mmWave MIMO systems with per-antenna power constraints and the additional limitations introduced by the phase-shifting network in the analog processing stage. Previous hybrid designs were obtained using a total power constraint, but in practical implementations per-antenna constraints are more realistic, specially at mmWave, given the large number of power amplifiers used in the transmit array. Assuming perfect channel knowledge, we obtain first an approximation to the all-digital solution for the precoder and the combiner given the per-antenna constraints. Then, we develop a new method for the design of the hybrid precoder and combiner which attempts to match such all- digital approximation. Simulation results show the effectiveness of the proposed approach, which performs close to the all-digital solution.

This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non line-of-sight (LOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.

This paper addresses the problem of distributed estimation of a parameter vector in the presence of noisy input and noisy output data, as well as data faults, performed by a wireless sensor network in which only local interactions among the nodes are allowed. In the presence of unreliable observations, standard estimators become biased and perform poorly in low signal-to-noise ratios. We propose therefore two different distributed approaches based on the Expectation-Maximization algorithm: in the first one the regressors are estimated at each iteration, whereas the second one does not require explicit regressor estimation. Numerical results show that the proposed methods approach the performance of a clairvoyant scheme with knowledge of the random data faults.

This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by low-complexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered. Setting out from a nonparametric regression framework, it is shown that a sensible approach leads to a support vector machine formulation. The simulated tests verify that accurate spectrum maps can be constructed using a simple sensing architecture with significant savings in the feedback.

The class of complex random vectors whose covariance matrix is linearly parameterized by a basis of Hermitian Toeplitz (HT) matrices is considered, and the maximum compression ratios that preserve all second-order information are derived-the statistics of the uncompressed vector must be recoverable from a set of linearly compressed observations. This kind of vectors arises naturally when sampling wide-sense stationary random processes and features a number of applications in signal and array processing. Explicit guidelines to design optimal and nearly optimal schemes operating both in a periodic and nonperiodic fashion are provided by considering two of the most common linear compression schemes, which we classify as dense or sparse. It is seen that the maximum compression ratios depend on the structure of the HT subspace containing the covariance matrix of the uncompressed observations. Compression patterns attaining these maximum ratios are found for the case without structure as well as for the cases with circulant or banded structure. Universal samplers are also proposed to compress unknown HT subspaces.

We consider Total Least Squares (TLS) estimation in a network in which each node has access to a subset of equations of an overdetermined linear system. Previous distributed approaches require that the number of equations at each node be larger than the dimension L of the unknown parameter. We present novel distributed TLS estimators which can handle as few as a single equation per node. In the first scheme, the network computes an extended correlation matrix via standard iterative average consensus techniques, and the TLS estimate is extracted afterwards by means of an eigenvalue decomposition (EVD). The second scheme is EVD-free, but requires that a linear system of size L be solved at each iteration by each node. Replacing this step by a single Gauss-Seidel subiteration is shown to be an effective means to reduce computational cost without sacrificing performance.

Active interference cancellation (AIC) is an effective technique to shape the OFDM spectrum, providing deep notches over protected bands without affecting receiver design. However, AIC typically introduces spectrum overshoot, of concern if compliance with a spectral emission mask is required. Most AIC designs neglect these spectral spurs, focusing on minimization of out-of-band radiation. We present a novel design that explicitly takes spectral spurs into account. The resulting optimization problem is convex and can be efficiently solved, although for systems with a large number of subcarriers the computational effort can be significant. A suboptimal solution based on a previous AIC design with a maximum power constraint is also proposed. Its performance is close to optimal while obtained at a much lower computational cost, comparing favorably to previous schemes.

With full-duplex relays, simultaneous reception and transmission in the same frequency produce self-interference distortion and relay oscillation, hampering reception at the receiver end unless properly mitigated. Previous adaptive methods for self-interference cancellation using multiple receive antennas require knowledge of the direction of arrival of the source signal. We present a novel architecture and adaptive scheme for Amplify-and-Forward relays allowing cancellation without such knowledge. The temporal filter is updated under the property restoral paradigm in order to match the power spectrum of the retransmitted signal to that of the source signal.

Simultaneous reception and transmission in the same frequency, so-called full-duplex operation, causes an infinite feedback loop in an amplify-and-forward relay. The unwanted echoes may result in oscillation at the relay, making it unstable, and distorting the spectrum. This paper presents an adaptive MIMO filtering method for full-duplex amplify-and-forward relays that aims at solving the joint problem of self-interference mitigation and equalization of the source-relay channel. The scheme exploits the knowledge of the autocorrelation of the transmitted signal as the only side information while allowing the relay, in the best case, to implement precoding as if there was not any self-interference or frequency selectivity in the source-relay channel. Finally, the proposed adaptation algorithm is investigated by determining its stationary points and by performing simulations in a MIMO-OFDM framework.

Distributed implementations of the Expectation-Maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivates our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

Wideband spectrum sensing provides a means to determine the occupancy of channels spanning a broad range of frequencies. Practical limitations impose that the acquisition should be accomplished at a low rate, much below the Nyquist lower bound. Dramatic rate reductions can be obtained by the observation that only a few parameters need to be estimated in typical spectrum sensing applications. This paper discusses the joint estimation of the power of a number of channels, whose power spectral density (PSD) is known up to a scale factor, using compressive measurements. First, relying on a Gaussian assumption, an efficient approximate maximum likelihood (ML) technique is presented. Next, a least-squares estimator is applied for the general non-Gaussian case.

We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an error floor in moderate and high signal-to-noise ratio (SNR), whereas previously proposed methods based on hard decisions on data failure events degrade as the SNR decreases. Aiming at optimal performance within the whole range of SNRs, we adopt a Maximum Likelihood framework based on the Expectation-Maximization (EM) algorithm. The statistical model and the iterative nature of the EM method allow for a diffusion-based distributed implementation, whereby the information propagation is embedded in the iterative update of the parameters. Numerical examples show that the proposed algorithm practically attains the Cramer-Rao Lower Bound at all SNR values and compares favorably with other approaches.

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.

We consider the problem of detecting a known signal with constant magnitude immersed in noise of unknown variance, when the propagation channel is frequency-flat and randomly time-varying within the observation window. A Basis Expansion Model with random coefficients is used for the channel, and a Generalized Likelihood Ratio approach is adopted in order to cope with deterministic nuisance parameters. The resulting scheme can be seen as a generalization of the wellknown Matched Filter detector, to which it reduces for time-invariant channels. Closed-form analytical expressions are provided for the distribution of the test statistic under both hypotheses, which allow to assess the detection performance.

Although full-duplex relaying schemes are appealing in order to improve spectral efficiency, simultaneous reception and transmission in the same frequency results in self-interference, distorting the retransmitted signal and making the relay prone to oscillation. Current feedback cancellation techniques by means of adaptive filters are hampered by the fact that the useful and interference signals are highly correlated. We present a new adaptive algorithm which effectively and blindly restores the spectral shape of the desired signal. In contrast with previous schemes, the novel adaptive feedback canceller has low complexity, does not introduce additional delay in the relay station, and partly compensates for multipath propagation.

Spectrum sensing is a challenging key component of the Cognitive Radio paradigm, since primary signals must be detected in the face of noise uncertainty and at signal-to-noise ratios (SNRs) well below decodability levels. Multiantenna detectors exploit spatial independence of receiver thermal noise to boost detection performance and robustness. Here, we study the problem of detecting Gaussian signals with unknown rank-P spatial covariance matrix when the noise at the receiver is independent across the antennas and with unknown power. A generic diagonal noise covariance matrix is allowed to model calibration uncertainties in the different antenna frontends. We derive the generalized likelihood ratio test (GLRT) for this detection problem. Although, in general, the corresponding statistic must be obtained by numerical means, in the low SNR regime the GLRT does admit a closed form. Numerical simulations show that the proposed asymptotic detector offers good performance even for moderate SNR values.

In the context of spectrum sensing, we investigate the performance of detectors equipped with M antennas (co-located or distributed) under Rayleigh fading, in terms of detection diversity. Rather than the high-SNR concept of diversity order common in the communications literature, we adopt the notion recently advocated by Daher and Adve in the radar community: the slope of the average probability of detection (P̅D) vs. SNR curve at P̅D= 0.5. This definition is well suited to spectrum sensing, which invariably deals with low SNR levels. It is shown that the diversity order grows as M for an optimal centralized detector having access to all observations, whereas for the two distributed schemes considered (the multiantenna energy detector and the OR detector) it grows no faster than √M.

In many applications based on Wireless Sensor Networks (WSNs) with static sensor nodes, the availability of accurate location information of the network nodes may become essential. The node localization problem is to estimate all the unknown node positions, based on noisy pairwise distance measurements of nodes within range of each other. Maximum Likelihood (ML) estimation results in a non-convex problem, which is further complicated by the fact that sufficient conditions for the solution to be unique are not easily identified, especially when dealing with sparse networks. Thereby, different node configurations can provide equally good fitness results, with only one of them corresponding to the real network geometry. This paper presents a novel soft-computing localization technique based on hybridizing a Harmony Search (HS) algorithm with a local search procedure whose aim is to identify the localizability issues and mitigate its effects during the iterative process. Moreover, certain connectivity-based geometrical constraints are exploited to further reduce the areas where each sensor node can be located. Simulation results show that our approach outperforms a previously proposed meta-heuristic localization scheme based on the Simulated Annealing (SA) algorithm, in terms of both localization error and computational cost.

A number of applications of wireless sensor networks require to know the location of the sensor nodes. Typically, however, mainly due to costs and limited capacity of the batteries powering the sensor nodes, only a few nodes of the network, denoted anchor nodes in the literature, are endowed with their exact positions. Thus, given a number of anchor nodes, the problem of estimating the locations of all the nodes of a wireless sensor network has attracted a large interest in the last years. The localization task is based on the estimated distances between pairs of nodes in range of each other and is particularly hard in the most appealing scenario, that is, when the network connectivity is quite low. In a recent paper, we have proposed to tackle the localization problem as a two-objective optimization task with the localization accuracy and the number of connectivity constraints that are not satisfied by the candidate geometry as the two objectives. In this paper, we aim to evaluate the behavior of five state-of-the-art multi-objective evolutionary algorithms (MOEAs) in solving the localization problem on different network topologies. We show that one of these MOEAs, namely PAES, statistically outperforms the others in terms of localization error.

Due to the need of limiting costs and energy consumption, in real deployments of wireless sensor networks the exact positions of only a restricted number of nodes are generally available. Since a lot of applications require to know where all the nodes have been placed, the issue of estimating the locations of the remaining nodes has attracted a lot of interest in the literature. In this paper, we discuss how the localization problem can be solved by using a two-objective evolutionary algorithm which concurrently aims to maximize the localization accuracy and minimize the number of connectivity constraints non-satisfied by the candidate geometry codified in the chromosomes. The proposed approach has been applied to different network configurations and compared in terms of normalized localization error with a state-of-the-art method based on semi-definite programming. The results show that our approach outperforms the compared method in all the configurations.

Dynamic Spectrum Access (DSA) is receiving considerable interest as a means to improve spectral usage in licensed bands. In order to avoid interference to licensed users, spectrum sensing has emerged as an enabling technology for DSA. The requirements for spectrum sensors are stringent, as licensed user detection must be performed reliably at low signal-to-noise ratios (SNR). Sensing performance can be improved by exploiting signal features not present in the background noise. These approaches result in tradeoffs among performance and robustness to departures from the signal model. We consider second-order signal features and develop detectors exploiting spatial (by using multiple antennas) as well as temporal signal correlation, taking advantage of the fact that the power spectrum of the primary signal at each antenna can be known up to a complex scalar representing the unknown propagation channel. A low-SNR Generalized Likelihood Ratio approach is adopted in order to overcome this uncertainty, resulting in different tests intimately related to familiar diversity combining techniques. The performance of the proposed detectors is analyzed and tested in different scenarios.

One of the key problems in cognitive radio (CR) is the detection of primary activity in order to determine which parts of the spectrum are available for opportunistic access. This detection task is challenging, since the wireless environment often results in very low SNR conditions. Moreover, calibration errors and imperfect analog components at the CR spectral monitor result in uncertainties in the noise spectrum, making the problem more difficult. In this work, we present a new multiantenna detector which is based on the fact that the observation noise processes are spatially uncorrelated, whereas any primary signal present should result in spatial correlation. In particular, we derive the generalized likelihood ratio test (GLRT) for this problem, which is given by the quotient between the determinant of the sample covariance matrix and the determinant of its block-diagonal version. For stationary processes the GLRT tends asymptotically to the integral of the logarithm of the Hadamard ratio of the estimated power spectral density matrix. Additionally, we present an approximation of the frequency domain detector in the low SNR regime, which results in computational savings. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.

In Cognitive Radio scenarios channelization information from primary network may be available to the spectral monitor. Under this assumption we propose a spectral estimation algorithm from compressed measurements of a multichannel wideband signal. The analysis of the Cramer-Rao Lower Bound (CRLB) for this estimation problem shows the importance of detecting the underlaying sparsity pattern of the signal. To this end we describe a Bayesian based iterative algorithm that discovers the set of active signals conforming the band and simultaneously reconstructs the spectrum. This iterative spectral estimator is shown to perform close to a Genie-Aided CRLB that includes full knowledge about the sparsity pattern of the channels.

We consider the problem of joint non-data-aided (NDA) estimation of carrier frequency and phase offsets, together with that of signal and noise powers, obtained from the baud-rate samples of a linearly modulated signal distorted by Gaussian noise. The Crame¿r-Rao lower bound (CRLB) is derived, for quadrature-symmetric constellations showing that the carrier parameters are decoupled from signal and noise powers. Furthermore, a joint NDA maximum-likelihood estimator is developed, based on the application of the expectation-maximization algorithm, which exhibits a jitter performance close to the CRLB for a wide SNR range.

Starting from local observations, iterative consensus algorithms attempt to drive a sensor network to a common estimate in a decentralized, incremental manner. When additive noise perturbs the sensor exchanges, a decreasing stepsize guarantees convergence under certain conditions, although the design of such stepsize sequence for fastest convergence is an unsettled issue. We present a greedy approach to stepsize sequence design, which minimizes the mean squared error at each iteration. This design requires knowledge of the network topology; in order to overcome this drawback, a modified design based only on average descriptors of the network is also developed.

Signal-to-noise ratio (SNR) estimation for linearly modulated signals is addressed in this letter, focusing on envelope-based estimators, which are robust to carrier offsets and phase jitter, and on the challenging case of nonconstant modulus constellations. For comparison purposes, the true Cramer-Rao lower bound is numerically evaluated, obtaining an analytical expression in closed form for the asymptotic case of high SNR values, which quantifies the performance loss with respect to coherent estimation. As the maximum-likelihood algorithm is too complex for practical implementation, an expectation-maximization (EM) approach is proposed, achieving a good tradeoff between complexity and performance for medium-to-high SNRs. Finally, a hybrid scheme based on EM and moments-based estimates is suggested, which performs close to the theoretical limit over a wide SNR range.

Recently, a time-domain equalizer (TEQ) design for multicarrier-based systems has been proposed which claims to minimize the delay spread of the overall channel impulse response. We show that this is true only in an approximate sense; depending on the channel considered, the loss in delay spread with respect to the true minimum can be significant. An iterative algorithm to find this minimum is presented, whose computational complexity is similar to that of standard TEQ designs like the MSSNR approach of Melsa et al. It is observed that the method iteratively and automatically seeks the time reference yielding best performance, an advantage with respect to the MSSNR design which must compute several TEQs over a range of time references in order to select the optimum.

We provide an analysis of the constant modulus (CM) cost function under the assumption of a white equalizer input. This can be achieved by means of an adaptive prewhitening all-pole filter and has been suggested in previous works as a means for both MSE improvement and DFE cold start-up. For white inputs, it is seen that CM -optimizing the spherical component of the equalizer parameter vector is equivalent to minimizing the fourth moment of its output, regardless of the value of the radial component. This leads to an eigenvector interpretation of prewhitened CM receivers, and to a blind initialization procedure from an eigenvector of the quadri-covariance matrix of the whitened data. Connections with iterative eigenvector-based schemes are also explored.

A novel car speed estimation technique from a pair of omnidirectional microphones is presented. This approach is based on the maximum likelihood principle and it directly estimates the speed without any assumptions on the acoustic signal emitted by the vehicle. This has the advantages of bypassing troublesome intermediate delay estimation steps as well as eliminating the need for an accurate yet general enough acoustic model. An analysis of the estimate for the particular case of a narrowband source is provided. The estimation algorithm is well suited to DSP implementation and performs well with preliminary field data.

This paper considers the blind equalization problem for nonlinear Volterra type channels excited by real IID (independent and identically distributed) symbols. Previous work has shown that under the right conditions the equalizers can be found from the second order statistics of the channel output as long as the number of subchannels exceeds the number of kernels. In order to alleviate this requirement, we consider the use of a simple precoding device previous to transmission which provides a tradeoff between effective data rate and number of subchannels required. Necessary and sufficient conditions for blind equalizability under this scheme are given, and an algorithm for the computation of the equalizers is presented.

Potential applications of blind channel identification and equalizationinwireless communication systems have been recently explored. For multi-user systems that areirreducible and column-reduced,second order statistical methods normally can identify channel dynamics up to a unitary mixing matrix. In this paper, we investigate the equalizability of desired users and the cancellation of unwanted interfering signals basedonsecond order output statistics. We show that a desired user channel can beequalizedifithas the longest memory. Furthermore, interfering user signals can becancelled under a morerelaxed multi-user channel condition.

We explore the utility of second-order statistics for blind identification/equalization of nonlinear channels. Under standard assumptions it is shown that the channel cannot be identified to within a scaling factor from the output second order statistics, but that the ambiguity is at a level that permits equalization. We show that these results cover cases that the prior literature does not address.

We take a new look to adaptive IIR lattice filters in order to devise algorithms that preserve the stability of the corresponding direct form schemes. By analyzing the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride and SHARF algorithms is presented. By contrast, it is shown that previous lattice versions of these algorithms may fail to preserve stability of stationary points.

This work provides conditions on the input sequence that ensure the exponential asymptotic stability of the inverse of the forward prediction error filter obtained by means of the recursive weighted least squares algorithm. Note that this filter is in general time-varying. Thus this result is a natural extension to the well-known minimum phase property of forward prediction error filters obtained by the autocorrelation method.

This work considers the implementation of recursive identification algorithms based on hyperstability concepts with polyphase structures. It is shown that the strictly positive real (SPR) condition required for convergence of these schemes can always be met by using a sufficiently high polyphase expansion factor M. For a given M, the degree of persistent excitation required for parameter convergence is obtained. When a priori knowledge about the unknown system is available, a compensating filter can be designed to avoid the need for a high M.

Some previous works have shown that under an autoregressive constraint on the input signal, least-squares equation-error methods provide stable models of the estimated transfer function. We present an alternative proof of this fact which allows one to increase the order of the autoregressive input by one, for both the monic and unit-norm approaches.

It is shown that the offline Steiglitz-McBride (1965) identification method may present stable limit cycles and even chaotic behavior when implemented in lattice form. Moreover, we also show that even under ideal conditions (i.e., with an adaptive filter of sufficient order and with noiseless measurements), the stationary point corresponding to identification of the unknown system need not be a convergent point of the lattice version of the online SM algorithm.

It is well known that the standard equation-error (EE) method for the identification of linear systems yields biased estimates if the data are noise corrupted. Due to this bias, the resulting estimate can be unstable in some cases, depending on the spectral characteristics of the input and the noise, the signal-to-noise ratio (SNR), and the unknown system. The set of all-pole linear systems whose equation-error estimate is stable for all wide sense stationary inputs and white measurement noise is investigated. Some results concerning the structure of this set are given.