This paper investigates the use of the weighted centroid localization (WCL) method for user localization in cell-free massive MIMO (CF-mMIMO) networks. This low-complexity algorithm operates solely on received power measurements from pilot transmissions, requiring no prior channel information or estimation. It enables coarse localization, which is valuable for various network management tasks while incurring minimal cost and overhead. Using a stochastic geometry-based analytical framework, we derive approximations for the localization mean-square error, providing insights into the performance and limitations of WCL. We also present an exact expression for the localization error cumulative distribution function, along with alternative approximations based on moment matching. The predictive capability of the proposed analytical framework is validated through extensive simulations that incorporate key practical impairments, such as multipath propagation, spatially correlated shadowing, and pilot contamination, that are analytically intractable. These results confirm the practical utility of our analysis in supporting the design of CF-mMIMO networks to meet specific localization performance targets.

This paper explores data compression’s key role in cooperative sensing within integrated sensing and communication (ISAC) networks, where range-angle maps generated at each base station (BS) are shared with a fusion center (FC). Efficient compression schemes minimize network overhead while ensuring accurate target detection and localization. Motivated by this challenge, we propose three novel compression approaches tailored for a network of multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM)-based mono-static sensors: i) excision filtering (EF) for map sifting, ii) principal component analysis (PCA) for dimensionality reduction, and iii) quantization for efficient encoding. To localize both point-like and extended targets from fused range-angle maps, we exploit the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. Given that DBSCAN requires careful tuning of its clustering parameters, we introduce an artificial intelligence (AI)-driven method to optimize these settings dynamically. A comprehensive rate-distortion analysis evaluates the network’s localization performance under varying compression levels. Key metrics—including bit rate, generalized optimal sub-pattern assignment (GOSPA) error, missed detection rate, and false alarm rate—provide a holistic assessment that balances localization accuracy with network overhead.

Orthogonal time frequency space (OTFS) modulation is gaining recognition for its potential to facilitate integrated sensing and communication (ISAC) within future mobile networks. However, computing the sensing channel matrix in orthogonal time frequency space (OTFS), a crucial step for accurate target parameter estimation, presents significant challenges due to its high dimensionality. Therefore, this study introduces an innovative method to reduce such computational complexity by combining two ingredients. First, through algebraic operations, we decompose the sensing channel matrix into four lower-dimensional matrices whose elements can be associated with a Dirichlet kernel. Second, we formulate an analytical criterion, independent of system parameters, that leverages the properties of the Dirichlet kernel and identifies the most informative elements of these matrices that deserve computation. To demonstrate the effectiveness of our approach, we assess the computational complexity of this distilled channel matrix in terms of the number of elementary operations required. Numerical results indicate that our technique markedly decreases receiver complexity by up to three orders of magnitude without compromising sensing performance.

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems, namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we introduce a novel linear minimum mean-squared-error (LMMSE) estimator. We also provide a rigorous theoretical characterization of side-lobe levels achieved by the proposed LMMSE estimator and two benchmark schemes, proving its superiority for any modulation scheme and SNR level. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate beams for sensing and communications not overlapping in time. Simulations demonstrate improved performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. Additionally, we present experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements

The adoption of dynamic spectrum sharing requires addressing various technical challenges, such as intelligent sensing and radio frequency (RF) spectrum awareness. In this scenario, we present a new framework that utilises variational Bayes factor analysis (VBFA) for cooperative wideband spectrum sensing (WSS) without any prior assumption. The approach is data-driven and capable of detecting unused spectrum bands using a test statistic on an evidence lower bound (ELBO). The framework is then applied to a case study that considers shadowing effects and frequency-selective multipath channels between primary users (PUs) and sensors. The solution performs better than the state-of-the-art methods, demonstrating excellent performance in low signal-to-noise ratio (SNR) environments, e.g., reaching a detection probability of 90% for a nominal SNR of $-10 \mathrm{d B}$.

This work proposes a maximum likelihood-based parameter estimation framework for a multistatic millimeter wave integrated sensing and communication system using energy-efficient hybrid digital-analog arrays. Due to the typically large arrays used in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field (NF) regime. To address this, we propose a two-step estimation process. Initially, we consider far-field (FF) propagation assumptions, followed by refined estimation based on NF assumptions, enhancing accuracy when the target is within the NF of the arrays. In particular, when operating in the NF of the transmitter (Tx), we select beamfocusing array weights designed to achieve constant gain over an extended spatial region. Subsequently, we re-estimate target parameters at the receivers (Rxs). The effectiveness of the proposed framework is evaluated over various scenarios through numerical simulations. In particular, the impact of customdesigned flat-gain beamfocusing codewords in improving both communication and sensing performance when the target is in the NF of the Tx is demonstrated. Additionally, the benefit of considering a correct NF channel model when the target is located near an Rx is shown.

This work proposes a maximum likelihood (ML)- based parameter estimation framework for a millimeter wave (mmWave) integrated sensing and communication (ISAC) system in a multistatic configuration using energy-efficient hybrid digital-analog (HDA) arrays. Due to the typically large arrays deployed in the higher frequency bands to mitigate isotropic path loss, such arrays may operate in the near-field (NF) regime. The proposed parameter estimation in this work consists of a two-stage estimation process, where the first stage is based on far-field (FF) assumptions, and is used to obtain a first estimate of the target parameters. In cases where the target is determined to be in the NF of the arrays, a second estimation based on NF assumptions is carried out to obtain more accurate estimates. In particular, when operating in the near-filed of the transmitter (Tx), we select beamfocusing array weights designed to achieve a constant gain over an extended spatial region and re-estimate the target parameters at the receivers (Rxs). We evaluate the effectiveness of the proposed framework in numerous scenarios through numerical simulations and demonstrate the impact of the custom-designed flat-gain beamfocusing codewords in increasing the communication performance of the system.

In this work, we propose a novel framework for reactive jammer detection based on spectrum patrolling, blind source separation (BSS), and causal inference. The methodology is based on a spectrum patrol that performs jamming detection without demodulating the received signals; therefore, it can manage scenarios with legitimate users belonging to different networks and adopting completely different network protocols. Furthermore, because of the wireless medium, over-the-air signals captured by the patrols are mixed; consequently, BSS is used to separate traffic patterns. The proposed causal inference-based approach (namely all-versus-one transfer entropy (AvOTE)) reaches a remarkably high probability of detection (i.e., 0.95) with low false alarm probability (i.e., 0.05) even in high shadowing regimes. Moreover, to fully understand the impact of different parameters on the performance, we study the impact of the signal-to-jammer ratio (SJR) and the jammer attack strategy on the detection performance in different shadowing regimes.

In this paper, we consider a joint sensing and communication (JSC) network in which multiple base stations (BSs) cooperate through a fusion center (FC) to detect and track the objects present in a supervised area. Every BS acts as a monostatic sensor capable of scanning the environment and sensing the targets while simultaneously communicating with user equipments (UEs). In particular, each BS generates range-angle maps, which are shared with a FC for data fusion and tracking via particle filter (PF) and multi-hypothesis tracker (MHT) algorithms. The performance of the proposed solutions is evaluated by varying the fraction of power and time devoted to sensing to manage the network overhead and offer a sensing/communication trade-off. Numerical results show that the proposed algorithms can successfully track multiple targets with different sizes and behavior in a vehicular scenario, ensuring, e.g., a root mean squared error (RMSE) of the estimated position of a pedestrian less than 50 cm when considering three BSs.

In this work, we consider a multistatic joint sensing and communication network composed of a transmitter and multiple receivers capable of estimating the position of a target in the monitored area. In particular, the system consists of a transmitter equipped with multiple antennas (typically a base station) and several receivers that can act as sensors with a single antenna. The transmitter adopts multiple beams to accommodate the communication towards the user equipment and the sensing functionality, with the ability to split the power between the two beams to adjust the sensing/communication trade-off. Processing of target echoes by the sensors produces a bistatic distance estimate or soft maps, which are combined by the fusion center. We then propose two sensor data fusion strategies, least square and soft maps fusion; the former has a negligible impact on the network overhead, while the latter always provides better performance in terms of root mean squared error of target position estimation when considering a small fraction of power devoted to sensing. Finally, we highlight the benefits of cooperation offered by the multistatic configuration, compared to the bistatic one, in terms of power saving for sensing.

This work proposes a low-complexity estimation approach for an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) system. In particular, we first define four low-dimensional matrices used to compute the channel matrix through simple algebraic manipulations. Secondly, we establish an analytical criterion, independent of system parameters, to identify the most informative elements within these derived matrices, leveraging the properties of the Dirichlet kernel. This allows the distilling of such matrices, keeping only those entries that are essential for detection, resulting in an efficient, low-complexity implementation of the sensing receiver. Numerical results, which refer to a vehicular scenario, demonstrate that the proposed approximation technique effectively preserves the sensing performance, evaluated in terms of root mean square error (RMSE) of the range and velocity estimation, while concurrently reducing the computational effort enormously.

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing.

Systems that combine sensing and communication functionalities are gaining interest for several possible applications related to the internet of things (IoT) and the upcoming 6G mobile radio networks. Studies have recently been proposed to find the optimal tradeoff between sensing and communication performance. However, these studies assume continuous transmission and thus consider that the time available for estimation can always be large enough to achieve some desired accuracy. Moreover, in line with this assumption, communication performance is measured via the well-known Shannon capacity, which implicitly assumes indefinitely long error correction codes. However, in the case of short packet transmissions, such as the case in several IoT applications, the above assumption is unrealistic, as the length of the transmitted packet limits that of the error correction code and the observation time for parameter estimation. Therefore, this paper aims at investigating the optimal tradeoff between the data transmission and the target localization capabilities in a finite block-length regime, by making use of some typical metrics of the finite length information theory. In particular, the optimal beamforming, which minimizes the Cramér Rao bound of target localization, is derived under a constraint over the block error probability for given packet length values.

This work investigates a multibeam system for joint sensing and communication (JSC) based on multiple-input multiple-output (MIMO) 5G new radio (NR) waveforms. In particular, we consider a base station (BS) acting as a monostatic sensor that estimates the range, speed, and direction of arrival (DoA) of multiple targets via beam scanning using a fraction of the transmitted power. The target position is then obtained via range and DoA estimation. We derive the sensing performance in terms of probability of detection and root mean squared error (RMSE) of position and velocity estimation of a target under line-of-sight (LOS) conditions. Furthermore, we evaluate the system performance when multiple targets are present, using the optimal sub-pattern assignment (OSPA) metric. Finally, we provide an in-depth investigation of the dominant factors that affect performance, including the fraction of power reserved for sensing.

Networks of randomly distributed nodes appear in various fields, including forestry and wireless communications, and can often be modeled, using stochastic geometry theory, as Poisson point processs (PPPs). In these contexts, estimation of nodes density is important for monitoring and optimizing the network. Originally, this problem has been addressed in forestry where the trees are the nodes and, assuming these are distributed according to an infinite two-dimensional homogeneous PPP, the spatial density can be estimated by measuring the distances from one reference tree to its neighbors. However, in many other scenarios, nodes could result invisible with some probability, for example depending on distance. In this paper, we derive the Cramér-Rao bounds and new estimators for the node spatial density, taking into account a limited capability in sensing neighbors. As an example, we provide estimators of the spatial density of transmitting devices in wireless networks with links affected by thermal noise, path loss, and shadowing.

This work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility.

The energy detector and the sphericity test are two widely used spectrum sensing techniques that utilize different properties of the signal received at the secondary user terminal. In this paper we use meta analysis to combine these two techniques and derive two novel mixture detectors that outperform both techniques. Since the spectrum sensing capability of the energy detector is limited by the uncertain knowledge of the noise power, first, we analyze the performance of the energy detector with estimated noise power. We derive analytical expressions for the false alarm and the detection probabilities when the secondary user terminal is equipped with multiple antennas. Next, we apply meta analysis to combine the outputs of the energy detector and the sphericity test to derive two mixture detectors, namely, Fisher's method and the weighted z-transform method. Furthermore, we extend our analysis to consider cooperative spectrum sensing where multiple secondary user terminals cooperatively detect the presence of primary users. Based on the mixture detectors, we propose two new cooperative spectrum sensing techniques and derive simple analytical expressions for false alarm probabilities. Extensive numerical examples are used to illustrate the accuracy of our analysis and to highlight the performance gains obtained by the mixture detectors.

Data originating from devices and sensors in Internet of Things scenarios can often be modeled as sparse signals. In this paper, we provide new source compression schemes for noisy sparse and non-strictly sparse sources, based on channel coding theory. Specifically, nonlinear excision filtering by means of model order selection or thresholding is first used to detect the support of the non-zero elements of sparse vectors in noise. Then, the sparse sources are quantized and compressed using syndrome-based encoders. The theoretical performance of the schemes is provided, accounting for the uncertainty in the support estimation. In particular, we derive the operational distortion-rate and operational distortion-energy of the encoders for noisy Bernoulli-uniform and Bernoulli-Gaussian sparse sources. It is found that the performance of the proposed encoders approaches the information-theoretic bounds for sources with low sparsity order. As a case study, the proposed encoders are used to compress signals gathered from a real wireless sensor network for environmental monitoring.

One of the key issues in the acquisition of sparse data by means of compressed sensing is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper, we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution.

The increasing availability of inexpensive software defined radios (SDRs) allows nowadays to implement cognitive radio (CR) functionalities in large scale networks such as the Internet-of-Things and future 5G systems. In this work, we focus on the spectrum sensing functionality that must take into account the front-end impairments of low-cost devices. Based on the noise model of a real SDR dongle, we address the problem of robust signal detection in the presence of noise power uncertainty and non-flat noise power spectral density (PSD). In particular, we analyze the receiver operating characteristic (ROC) of different known detectors in the presence of such front-end impairments, to understand the performance attainable in a real-world scenario. Based on the analysis, we propose two frequency-domain detectors that are proven to outperform previously proposed spectrum sensing techniques such as, e.g., eigenvalues-based tests.

Precise localization and tracking of moving objects is of great interest for a variety of emerging applications including the Internet-of-Things (IoT). The localization and tracking tasks are challenging in harsh wireless environments, such as indoor ones, especially when objects are not equipped with dedicated tags (noncollaborative). The problem of detecting, localizing, and tracking noncollaborative objects within a limited area has often been undertaken by exploiting a network of radio sensors, scanning the zone of interest through wideband radio signals to create a radio image of the objects. This paper presents a sensor network for radio imaging (sensor radar) along with all of the signal processing steps necessary to achieve highaccuracy objects tracking in harsh propagation environments. The described sensor radar is based on the impulse radio (IR) ultrawideband (UWB) technology, entailing the transmission of very short duration pulses. Experimental results with actual UWB signals in indoor environments confirm the sensor radar's potential in IoT applications.

Data originating from many devices and sensors can be modeled as sparse signals. Hence, efficient compression techniques of such data are essential to reduce bandwidth and transmission power, especially for energy constrained devices within machine to machine communication scenarios. This paper provides accurate analysis of the operational distortion-rate function (ODR) for syndrome-based source encoders of noisy sparse sources. We derive the probability density function of error due to both quantization and pre- quantization noise for a type of mixed distributed source comprising Bernoulli and an arbitrary continuous distribution, e.g., Bernoulli- uniform sources. Then, we derive the ODR for two encoding schemes based on the syndromes of Reed-Solomon (RS) and Bose, Chaudhuri, and Hocquenghem (BCH) codes. The presented analysis allows designing a quantizer such that a target average distortion is achieved. As confirmed by numerical results, the closed-form expression for ODR perfectly coincides with the simulation. Also, the performance loss compared to an entropy based encoder is tolerable.

Weighted centroid localization (WCL) based on received signal strength (RSS) measurements is an attractive low-complexity solution that enables cognitive radios (CRs) to have a geolocation awareness of the radio environment. In this paper, we propose a new analytical framework to accurately calculate the performance of WCL based on the statistical distribution of the ratio of two quadratic forms in normal variables. In particular, we derive an exact expression for the cumulative distribution function (CDF) of the two-dimensional location estimation in the presence of independent and identically distributed (i.i.d.) as well as correlated shadowing. Numerical results confirm that the analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as CR network spatial topology.

This letter provides tight upper bounds on the weak restricted isometry constant for compressed sensing with finite Gaussian measurement matrices. The bounds are used to develop a unified framework for the guaranteed recovery assessment of jointly sparse matrices from multiple measurement vectors. The analysis is based on the exact distribution of the extreme singular values of Gaussian matrices. Several joint sparse reconstruction algorithms are analytically compared in terms of the maximum support cardinality ensuring signal recovery, i.e., mixed norm minimization, MUSIC, and OSMP based algorithms.

Signal compression is essential for energy and bandwidth efficient communication and storage systems. In this paper, we provide two practical approaches for source compression of noisy sparse and non-strictly sparse (compressible) sources. The proposed schemes are based on channel coding theory to construct a source encoder that decreases the number of transmitted bits while preserving the fidelity of the reconstructed signal at the receiver by exploiting its sparsity. In addition, a model order selection scheme is proposed to detect the nonzero elements of sparse vectors embedded in noise, or to find a nonlinear sparse approximation of compressible signals. As illustrated by numerical results, our approach provides a lower distortion-rate function compared to previously known methods. For example, the proposed schemes achieve a lower distortion, about 2 orders of magnitude, compared to compressed sensing, for the same rate.

In the context of compressed sensing, we provide a new approach to the analysis of the symmetric and asymmetric restricted isometry property for Gaussian measurement matrices. The proposed method relies on the exact distribution of the extreme eigenvalues for Wishart matrices, or on its approximation based on the Tracy-Widom law, which in turn can be approximated by means of properly shifted and scaled Gamma distributions. The resulting probability that the measurement submatrix is ill conditioned is compared with the known concentration of measure inequality bound, which has been originally adopted to prove that Gaussian matrices satisfy the restricted isometry property with overwhelming probability. The new analytical approach gives an accurate prediction of such probability, tighter than the concentration of measure bound by many orders of magnitude. Thus, the proposed method leads to an improved estimation of the minimum number of measurements required for perfect signal recovery.

A common approach for estimating the number of wireless sources is to adopt model order selection based on information theoretic criteria (ITC). In this paper we study the generalized information criterion (GIC) and propose a design method for setting the penalty in practical situations, where the sample size is finite. The design is based on the maximum probability of correct model selection, that can be approximated using the statistic of the ratio between the largest eigenvalue and the trace of a white central Wishart matrix. For this metric we provide the exact distribution and a new approximation.

Blind localization of primary user (PU) is a geo-location spectrum awareness feature that can be very useful in enhancing the functionality of cognitive radios (CRs) in terms of minimizing the interference to the PU. However, the estimation of the PU position within the region is made difficult because cooperation between the PU and the secondary user (SU) does not exist and therefore the PU signal parameters remain unknown to the SU. The centroid-based localization techniques have significantly been adopted as suitable candidates that do not require knowledge of such parameters. In this paper we investigate the localization performance of such techniques by imposing constraints to the selection of the SU nodes, termed as SU cluster, to estimate the PU location. In particular, we impose a minimum distance constraint between any two SU nodes and group the qualifying nodes into a cluster. Only the SU nodes from the constrained cluster can take part in localizing the PU. We simulate the proposed method for a shadow fading wireless environment and compare the results with the centroid and the weighted centroid based blind localization methods. Our results show that the mean squared error in the estimation of the position of the PU is significantly improved for the proposed method compared to the two standard centroid localization techniques especially when the true PU location is away from the center of the region.

Spectrum sensing is an essential functionality in cognitive radio (CR) systems allowing us to discover spectrum opportunities and enabling primary user (PU) protection. Wideband spectrum sensing (WS) improves the awareness of the surrounding radio environment by jointly monitoring multiple frequency bands. In this paper we propose a WS approach based on the observation of a frequency domain representation of the received signal and the adoption of model order selection (MOS) to identify the occupied frequency components. We provide a general formulation of the problem valid for any kind of spectral representation and then focus on the case in which discrete Fourier transform (DFT) is used. This choice is motivated by the fact that DFT blocks are available in many wireless systems, such as OFDM receivers and recently proposed software radio architectures. We provide analytical expressions for the maximum probability of correct selection of the occupied sub-bands valid for MOS approaches encompassed within the generalized information criterion (GIC). We then propose a method for designing the selection algorithm to balance overestimation and underestimation. Numerical results show that the MOS scheme derived for DFT can be successfully applied also when more accurate frequency representations, such as multitaper (MT) spectrum estimates, are adopted.

In this paper we present a thorough study of spectrum sensing performance in cognitive radio (CR) scenarios where the primary user (PU) transmission is not continuous. In particular, we consider a sensing scheme in which the spectrum is monitored periodically for a fraction of time. In such a situation, sensing is affected by common detection impairments, including noise and fading, as well as by the PU temporal behavior. It is thus necessary to properly design periodic sensing parameters to balance between sensing overhead and detection performance. In this context, we derive a comprehensive analytical framework which accounts for detector performance, presence of noise and fading, PU temporal statistics, and periodic sensing. The analysis allows expression of the detection and false alarm probabilities in closed-forms to capture an explicit relationship between the PU temporal statistic and periodic sensing parameters. Our results show that the temporal behavior of the PU have a significant impact on the detection performance, and therefore a proper design of the sensing parameters is important. Based on our analysis we propose useful strategies for the design of effective periodic sensing.

Wideband spectrum sensing (SS) allows cognitive radios (CRs) to reach, by monitoring large portions of spectrum, a better awareness of the surrounding radio environment. In this paper, we formulate wideband SS as a model order selection problem. This approach consists in the adoption of information theoretic criteria (ITC) to identify the occupied frequency components in a frequency domain representation of the observed signal. We provide a general formulation of the problem and then focus on the case in which discrete Fourier transform (DFT) is used as spectral representation. Finally, we propose consistent ITC for which we provide analytical expressions for the maximum probability of detection.

In this paper we propose a wireless sensor network (WSN) designed for landslides monitoring and risk management. The WSN is self-organizing, has fault tolerance capabilities, and its behavior is driven by the events to be monitored, to guarantee fast deployment, robustness in harsh environments, and very long lifetime. Data collected by sensors are delivered through the network to a remote unit (RU) for on-line analysis and alerting. The WSN has been installed on a landslide located in Torgiovannetto (Italy) for an experimental campaign of several months where performance metrics, such as path statistics and battery levels, have been collected. These metrics demonstrate the effectiveness of the network protocols to manage self-organization, node failures, low link quality and unexpected battery depletion. With negligible human intervention during the pilot experiment the WSN revealed a very high level of robustness, which makes it suitable to monitor landslides in critical scenarios.

Novel non-coherent impulse communications receivers are proposed to alleviate excessive noise collection in clustered multipath channels. To this aim, a stop-and-go strategy based on energy detection in the autocorrelation receiver or in the energy detection receiver is employed. This strategy enables the selective collection of useful signal portions only, allowing the integration interval to be kept large without noise penalty. To implement this strategy, a blind method is employed, using model order selection based on information theoretic criteria, which optimizes the performance of the proposed receiver and does not require the estimation of channel parameters. The bit error probability of the proposed stop-and-go receivers is evaluated, and our results highlight the considerable performance gain at the expense of a small increase in complexity.

In multi-antenna systems the use of multiple antennas at one end or both ends of the link produces multiple channels. A useful, although ill-defined, metric for such a link is the number of independent channels provided. In this paper, we discuss several candidate metrics and compare their utility in the Rayleigh fading, single-input multiple-output case. We show that most of the metrics available in the literature have limitations and can exhibit non-physical behaviour. In order to improve on their performance, we develop two novel measures for the number of independent channels based on the statistical construction of the channel and channel capacity. These two measures are then extended to multiple-input multiple-output systems, Rician channels and arbitrary channel models.

In cooperative spectrum sensing networks the sensing nodes are often assumed to have the same noise power level. However, the different secondary users (SUs) could experience different temperatures, have receiver chains with different characteristics or even with a completely different architecture. Therefore, it is unlikely that they experience exactly the same noise power. In this paper we study the problem of cooperative spectrum sensing in cognitive radio (CR) networks, focusing on the case where the receivers experience different levels of noise power (uncalibrated receivers). We propose the independence test and compare it with the popular sphericity test. The independence test is in fact the generalized likelihood ratio (GLR) when the SUs are uncalibrated. We address in particular the threshold setting problem under a Neyman-Pearson framework. In order to reduce the complexity of the analysis, we approximate the test metrics as beta distributed random variables (r.v.s), by using a moment-matching approach. We provide simple and analytically tractable expressions for the computation of the probability of false alarm and for setting the decision threshold. Numerical simulations show that these approximated forms match very well the empirical distributions, allowing a very precise estimation of the probability of false alarm. In cooperative spectrum sensing with uncalibrated receivers the independence test is shown to be robust to strong imbalances of the noise power level.

Transmitted-reference (TR) modulation schemes have generated interest in the context of ultrawide bandwidth (UWB) communications in order to avoid complex channel estimation and synchronization. In these schemes, the length of the integration interval must be carefully chosen to achieve optimal performance. Difficulties arise since this parameter is related to the channel characteristics and to the signal-to-noise ratio (SNR). In this paper, we propose a blind method for the integration time determination, that adopts a model order selection strategy based on information theoretic criteria (ITC). Observing the received signal, without a-priori information about the channel and the SNR, the proposed technique finds an integration time closer to the channel ensemble optimum integration time, i.e., the integration time obtained a-posteriori for the considered channel model as the value that minimizes the average bit error probability (BEP) of the TR scheme for each SNR.

In this work we perform an asymptotic analysis of estimated noise power (ENP) energy detector (ED) to derive the condition for the existence of the SNR wall phenomenon. We prove that an ED with noise estimation does not exhibit the SNR wall if the variance of the estimate reduces when the observation time increases. In the absence of SNR wall, we show that the maximum slope of the design curves (SNR vs. observation time for an arbitrary target probability of false alarm (Pfa) and probability of detection (Pd)), equal to -5 dB/decade for the ideal ED, can be reached also by an ENP-ED. Finally, we derive analytical expressions for the design curves when maximum likelihood (ML) noise power estimation is adopted, and we prove that, asymptotically, the signal-to-noise ratio (SNR) penalty with respect to ideal ED is of 1.5 dB, when the number of noise-only samples is equal to the number of observed samples.

In this paper we present a distributed spectrum sensing technique based on a ring-formulation of the cognitive radio (CR) nodes in a network. The CR nodes in a network form a ring based on a particular criteria and distributes the local spectrum sensing decisions along the ring in a sequential manner to the successive CR nodes. Considering this method, we eliminate the requirement for all the CR nodes to send/broadcast its local decisions to all the other CR nodes as in the traditional distributed detection method. Moreover, in our method all the CR nodes in the ring will have the spectrum sensing information from all the other nodes in the ring unlike the traditional sequential distributed-sensing technique (without the ring formation). We also consider the temporal behavior of the primary user modeled as a Poisson-Pareto burst process, and present two distributed sensing techniques based on the 'ring-around' strategy for the energy based local detection method. We provide closed-form solutions for the detection and false alarm probabilities for the ring-around detection methods and present numerical results for Rayleigh fading signals with AWGN.

An uncertain knowledge of the noise power level can severely limit the energy detector (ED) spectrum sensing capability. In some situations this uncertainty can cause signal-to-noise ratio (SNR) penalties or even the rise of the SNR wall phenomenon. In this paper we analyze the performance of the ED with estimated noise power (ENP), addressing the threshold design and giving the conditions for the existence of the SNR wall. We derive analytical expressions for the design curves (SNR vs. observation time for a target performance) for the ENP-ED. Then we apply our analysis to cognitive radio (CR) systems where energy detection is used for fast sensing. For example it is shown that the SNR penalty with respect to ideal ED is of 5 log10(1+λ/λ) dB, when the time dedicated to noise power estimation is a multiple λ of the ED observation interval.

Radar sensor networks (RSNs) are gaining importance in the context of passive localization and tracking. The performance of RSNs is affected by disturbances, system's parameters, network topology, and the number of radar elements. In this paper, we derive a unified analytical framework that takes all this aspects into account and allows the derivation of probability of detection and localization uncertainty. The results enable the system designer to have a clear understanding on the effects of each system parameter and the trade-off between performance and complexity. Moreover, the potential for high-accuracy passive localization of ultrawide bandwidth (UWB) systems is shown.

In this paper we consider cooperative spectrum sensing to detect incumbent spectrum users (ISU) in cognitive radio (CR) networks. We propose a time-divisional cooperative periodic spectrum sensing (TD-CPSS) technique and analyze the detection performance based on the blind energy based detection scheme. The CR detects the presence of the ISU by means of TD-CPSS and opportunistically uses the spectrum for secondary communications. The proposed technique saves energy at the local CR nodes due to periodic sensing and at the same time maintains the minimum required detection probability by optimizing the sensing period. The detection probability together with the false alarm probability are derived for the TD-CPSS technique based on the additive noise at the sensing node and the temporal statistics of the ISU transmissions. In our model, we consider additive white Gaussian noise (AWGN) for local sensing and the Poisson-Pareto spectral occupancy model for the temporal behavior of the ISU transmissions. We also provide expression for the required time period for the proposed sensing technique which attains the minimum required detection probability whilst minimizing the energy consumption considering the noise and temporal statistics.

In a receive diversity system the use of multiple antennas at one end of the link produces multiple channels. A useful, although ill-defined, metric for such a link is the number of independent channels provided. In this letter we discuss several candidate metrics and compare their utility. We show that most of the metrics available in the literature have limitations and can exhibit non-physical behaviour. In order to improve on their performance, we develop two novel measures for the number of independent channels based on the statistical construction of the channel and channel capacity.

With the increasing proliferation of different communication devices sharing the same spectrum, it is critical to understand the impact of interference in heterogeneous wireless networks. In this paper, we put forth a mathematical model for coexistence in networks composed of both narrowband (NB) and ultrawideband (UWB) wireless nodes, based on fundamental tools from stochastic geometry. Our model considers that the interferers are spatially scattered according to a Poisson field, and are operating asynchronously in a wireless environment. We first determine the statistical distribution of the aggregate interference for both cases of NB and UWB emitters. We then provide error probability expressions for two dual configurations: 1) a NB victim link subject to the aggregate UWB interference, and 2) a UWB victim link subject to the aggregate NB interference. The results show that while the impact of a single interferer on a link is often negligible due to restrictions on the transmitted power, the aggregate effect of multiple interferers may cause significant degradation. Therefore, aggregate interference must be considered to ensure coexistence in heterogeneous networks. The proposed analytical framework shows good agreement with physical-level simulations of the system.

Ultra-wide-band (UWB) signals are suitable for underlay communications, over a frequency band where, possibly, other systems are active. Such coexistence of UWB and other systems is possible if the mutual interference has a small impact on their respective performance. This paper aims to present recent results on the interference and coexistence among UWB systems and other conventional narrow-band (NB) systems. Specifically, we consider a point-to-point UWB (NB) under the interference generated by a finite number of NB (UWB) radio transmitters. We consider channels including additive white Gaussian noise and multipath fading both for the victim and the interfering links, and different receiver architectures. While our main focus is on UWB systems based on impulse radio, wide-band systems employing carrier-based direct-sequence spread-spectrum and orthogonal frequency-division multiplexing are also considered.

Over the coming decades, high-definition situationally-aware networks have the potential to create revolutionary applications in the social, scientific, commercial, and military sectors. Ultrawide bandwidth (UWB) technology is a viable candidate for enabling accurate localization capabilities through time-of-arrival (TOA)-based ranging techniques. These techniques exploit the fine delay resolution property of UWB signals by estimating the TOA of the first signal path. Exploiting the full capabilities of UWB TOA estimation can be challenging, especially when operating in harsh propagation environments, since the direct path may not exist or it may not be the strongest. In this paper, we first give an overview of ranging techniques together with the primary sources of TOA error (including propagation effects, clock drift, and interference). We then describe fundamental TOA bounds (such as the CramÉr–Rao bound and the tighter Ziv–Zakai bound) in both ideal and multipath environments. These bounds serve as useful benchmarks in assessing the performance of TOA estimation techniques. We also explore practical low-complexity TOA estimation techniques and analyze their performance in the presence of multipath and interference using IEEE 802.15.4a channel models as well as experimental data measured in indoor residential environments.

Broadband connectivity to passengers of high speed trains by means of a satellite link requires the realization of gap fillers (GFs) to extend the service coverage inside railway tunnels. In this paper we investigate the performance of GFs based on the digital video broadcasting-satellite (DVB-S) technology in a reference railway tunnel whose propagation characteristics have been derived by means of the ray tracing technique. The channel impulse response has been investigated in order to derive DVB-S performance in terms of bit error rate at different train position along the tunnel. Then, the statistical properties of the received power inside the tunnel has been assessed, showing a log-normal distribution with a strong correlation in time. Finally, based on the statistical behavior of the fading, an analytical expression of the system outage is provided and the transmitted power required to guarantee a given coverage has been derived.

This paper evaluates the performance of wideband communication systems in the presence of narrowband interference (NBI). In particular, we derive closed-form bit-error probability expressions for spread-spectrum systems by approximating narrowband interferers as independent asynchronous tone interferers. The scenarios considered include additive white Gaussian noise channels, flat-fading channels, and frequency-selective multipath fading channels. For multipath fading channels, we develop a new analytical framework based on perturbation theory to analyze the performance of a Rake receiver in Nakagami-m channels. Simulation results for NBI such as GSM and Bluetooth are in good agreement with our analytical results, showing the approach developed is useful for investigating the coexistence of ultrawide bandwidth systems with existing wireless systems.

We investigate the performance of BPSK and QPSK with coherent detection and matched filtering in the presence of both time and phase asynchronous cochannel interfering signals. More precisely, we analyze the role played by different channel statistics on the distribution of the decision variable at the output of the matched filter. The results show that the Gaussian approximation is accurate not only in the (obvious) case of a large number of interferers, but also when the desired signal is subject to fading, whatever the number of interferers is. For example, when the desired signal is subject to Rayleigh fading, even in the presence of only one unfaded interferer the Kullback-Leibler distance between the exact distribution of the decision variable and that obtained with the Gaussian approximation on the interference is lower than 0.01 [nats] for all cases of practical interest.

This paper investigate the performance of time hopping (TH) pulse position modulation (PPM) systems in the presence of narrowband interference. In particular, we derive closed-form expressions for the bit error probability (BEP) of a TH-PPM system with independent asynchronous tone interferers with arbitrary amplitudes and frequencies. Different scenarios are taken into consideration with fading on the interferer and/or on the useful signal. Simulation results show that the assumption of tone interferers is a good approximation for narrowband interferers. It is shown that our analytical results are useful in assessing the possible coexistence of TH-PPM systems with existing wireless systems.

It is well known that MIMO systems offer the promise of achieving very high spectrum efficiencies (many tens of bits/Hz) in a mobile environment. The gains in MIMO capacity are sensitive to the presence of spatial and temporal correlation introduced by the radio environment. In this paper we examine how MIMO capacity is influenced by a number of factors, e.g.: a) temporal correlation, b) various combinations of low/high spatial correlations at either end, c) combined spatial and temporal correlations, In all cases we compare the channel capacity that would be achievable under independent fading. We investigate the behaviour of "capacity fades", examine how often the capacity experiences the fades, develop a method to determine level crossing rates and average data durations and relate these to antenna numbers.

The closed form expression of the probability density function of the disturbance due to a quaternary PSK (QPSK) cochannel interferer is obtained. Then the bit error rate performance of QPSK in the presence of multiple cochannel interferers and additive white Gaussian noise (AWGN) is analyzed. In particular, a closed form expression is given to evaluate the bit error probability of QPSK with multiple cochannel interference. It is shown that in order to determine the minimum signal-to-interference ratio required to obtain a fixed bit error probability, care must be given to the number of active interferers: in fact, strong differences are found when varying the number of interfering cochannel signals, using the same signal-to-total interference ratio. The comparison with respect to the Gaussian approximation is also discussed.