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Radio stripes (RSs) is an emerging technology in beyond 5G and 6G wireless networks to support the deployment of cell-free architectures. In this paper, we investigate the potential use of RSs to enable joint positioning and synchronization in the uplink channel at sub-6 GHz bands. The considered scenario consists of a single-antenna user equipment (UE) that communicates with a network of multiple-antenna RSs distributed over a wide area. The UE is assumed to be unsynchronized to the RSs network, while individual RSs are time- and phase-synchronized. We formulate the problem of joint estimation of position, clock offset, and phase offset of the UE and derive the corresponding maximum-likelihood (ML) estimator, both with and without exploiting carrier phase information. To gain fundamental insights into the achievable performance, we also conduct a Fisher information analysis and inspect the theoretical lower bounds numerically. Simulation results demonstrate that promising positioning and synchronization performance can be obtained in cell-free architectures supported by RSs, revealing at the same time the benefits of carrier phase exploitation through phase-synchronized RSs.

Reconfigurable intelligent surfaces (RISs) have attracted enormous interest thanks to their ability to overcome line-of-sight blockages in mmWave systems, enabling in turn accurate localization with minimal infrastructure. Less investigated are however the benefits of exploiting RIS with suitably designed beamforming strategies for optimized localization and synchronization performance. In this paper, a novel low-complexity method for joint localization and synchronization based on an optimized design of the base station (BS) active precoding and RIS passive phase profiles is proposed, for the challenging case of a single-antenna receiver. The theoretical position error bound is first derived and used as metric to jointly optimize the BS-RIS beamforming, assuming a priori knowledge of the user position. By exploiting the low-dimensional structure of the solution, a novel codebook-based robust design strategy with optimized beam power allocation is then proposed, which provides low-complexity while taking into account the uncertainty on the user position. Finally, a reduced-complexity maximum-likelihood based estimation procedure is devised to jointly recover the user position and the synchronization offset. Extensive numerical analysis shows that the proposed joint BS-RIS beamforming scheme provides enhanced localization and synchronization performance compared to existing solutions, with the proposed estimator attaining the theoretical bounds even at low signal-to-noise-ratio and in the presence of additional uncontrollable multipath propagation.

The problem of mobile position estimation in multipath scenarios is addressed. A low-complexity, fully-adaptive algorithm is proposed, based on the pseudo maximum likelihood approach. The processing is done exclusively on-board at the mobile node by exploiting narrowband downlink radio signals. The proposed algorithm is able to estimate via adaptive beamforming (with spatial smoothing) the optimal projection matrices that maximize the likelihood; in addition, it can associate the line-of-sight over the trajectory, hence achieving an integration gain. The performance assessment shows that the proposed algorithm is very effective in (even severe) multipath conditions, outperforming natural competitors also when the number of antennas and snapshots is kept at the theoretical minimum.

Distance estimation, which arises in many applications and especially in range-based localization, is addressed for joint received signal strength (RSS) and time of arrival (TOA) data. A statistical characterization of the joint maximum likelihood estimator, which is unavailable in closed-form, is provided together with a full performance assessment in terms of the actual mean squared error (MSE), in order to establish when hybrid estimation is superior compared to RSS-only or TOA-only estimation. Furthermore, a novel closed-form estimator is proposed based on an ad-hoc relaxation of the likelihood function, which removes the need to adopt iterative methods for hybrid TOA/RSS ranging and strikes a better bias-variance tradeoff for improved performance. A thorough theoretical analysis, corroborated by numerical simulations, shows the effectiveness of the proposed approach, which outperforms state-of-the-art solutions.

Motivated by safety applications in urban vehicular scenarios, where GPS does not typically provide the required positioning accuracy, a GPS-free localization technique that exploits vehicle-to-infrastructure communications is proposed. In particular, it provides for a vehicle to opportunistically use the beacon packets received from a roadside unit (RSU) in order to obtain estimates of their angle of arrival. Such estimates, together with the RSU's position information within beacon packets, are fed to a weighted least squares algorithm that aims at localizing the vehicle. The algorithm tries to take advantage of reliable measurements typically collected closer to the RSU—where a very high signal-to-noise ratio yields an accurate angular resolution—while keeping robustness against multipath phenomena. Simulation results show the effectiveness of the proposed technique.

International audience; The paper addresses the problem of designing radar detectors more robust than Kelly's detector to possible mismatches of the assumed target signature, but with no performance degradation under matched conditions. The idea is to model the received signal under the signal-plus-noise hypothesis by adding a random component, parameterized via a design covariance matrix, that makes the hypothesis more plausible in presence of mismatches. Moreover, an unknown power of such component, to be estimated from the observables, can lead to no performance loss, under matched conditions. Derivation of the (one-step) GLRT is provided for two choices of the design matrix, obtaining detectors with different complexity and behavior. A third parametric detector is also obtained by an ad-hoc generalization of one of such GLRTs. The analysis shows that the proposed approach can cover a range of different robustness levels that is not achievable by state-of-the-art with the same performance of Kelly's detector under matched conditions.

The problem of maximum likelihood (ML) direct position estimation (DPE) of a multi-antenna receiver for the case of dynamic multipath environments is addressed, exploiting narrowband broadcast radio signals, without assuming special conditions such as mmWave massive MIMO, OFDM, or large bandwidth. To overcome the dramatic complexity of the plain ML formulation, which involves a large number of unknown parameters (proportional to the number of paths times the number of observations), a reduced-complexity algorithm based on a pseudo ML approach is proposed. Unlike classical two-step approaches, where angles of arrival (AOAs) are first estimated and then used in a second step to (geometrically) estimate the unknown position, the proposed algorithm also exploits the information brought by non line-of-sight (NLOS) paths: specifically, the whole multipath parameters are estimated via spatially-smoothed MUSIC and adaptive beamforming, to reconstruct the projection matrices appearing in the ML cost function, which is ultimately maximized with respect to the unknown position (sticking to the DPE approach). In addition, a novel AOA-based mechanism that conditionally associates the LOS over time for a given trial position is designed; in doing so, a performance gain is obtained by the coherent integration of multiple observations from different channel realizations. The performance assessment shows that the proposed algorithm is very effective in (even severe) multipath conditions, outperforming natural competitors also when the number of antennas and samples is kept at the theoretical minimum, and exhibiting robustness to several types of mismatch.

This paper deals with estimation of gain and phase errors possibly present in frequency modulated continuous wave radars. In particular, the Cramer–Rao lower bound of unbiased estimators of gain and phase errors in presence of nuisance parameters is computed. It is used as a reference for the performance of already proposed estimators (computed by Monte Carlo simulation).

An Empirical Bayes formalization of the regularized covariance estimation problem is proposed for (possibly high-dimensional, low-sample) normal variates. A simple iteration is provided to automatically adjust the shrinkage level, which provably converges to the maximum likelihood hyperparameter estimation for any choice of the starting point. The proposed approach is effective and can outperform both MSE-optimized diagonal loading and the Rao–Blackwell Leidot–Wolf estimator in terms of covariance-matrix-specific metrics.

Adaptive radar detection of point-like targets in presence of disturbance with unknown spectral properties is addressed. By relaxing the assumptions of the W-ABORT, a tunable detector with improved detection vs rejection trade-off is proposed. To this aim, the presence of a fictitious signal under the null hypothesis is modeled probabilistically, so that an additional degree of freedom is introduced in the statistic of the detector. The resulting parametric GLRT shows a range of possible behaviors, from Kelly’s detector to the plain W-ABORT. Monte Carlo simulations reveal that an improved compromise can be obtained. Simple closed-form approximations are also given with near-optimal performance, so that the ultimate computational complexity remains comparable to that of the W-ABORT.

We consider the Euclidean norm of a bivariate normal vector, equivalently, the amplitude or envelope of a complex Gaussian variable with correlated real and imaginary parts - known also as Hoyt or Nakagami-q distribution. Such a model, popular in fading and other communication contexts, is also relevant for the analysis of spatial errors in positioning-related applications, namely localization. In this paper an efficient expression in terms of complete elliptic integrals is given for the expected value, and good approximations in simple algebraic form are derived. Results are generalized to moments of any order.

This paper deals with adaptive compensation of gain and phase errors possibly present in FMCW radars. In particular, we show that previously-proposed compensation procedures can be related to the LMMSE approach. In addition, we assess by Monte Carlo simulation the performance of gain and phase estimators and, eventually, the effectiveness of the compensation procedure.

The design of anomaly detection (AD) methods for network traffic has been intensively investigated by the research community in recent years. However, less attention has been devoted to the issues which eventually arise when deploying such tools in a real operational context. We designed a statistical based change detection algorithm for identifying deviations in distribution time series. The proposed method has been applied to the analysis of a large dataset from an operational 3G mobile network, in the perspective of the adoption of such a tool in production. Our algorithm is designed to cope with the marked non-stationarity and daily/weekly seasonality that characterize the traffic mix in a large public network. Several practical issues emerged during the study, including the need to handle incompleteness of the collected data, the difficulty in drilling down the cause of certain alarms, and the need for human assistance in resetting the algorithm after a persistent change in network configuration (e.g. a capacity upgrade). We report on our practical experience, highlighting the key lessons learned and the hands-on experience gained from such an analysis. Finally, we propose a novel methodology based on semisynthetic traces for tuning and performance assessment of the proposed AD algorithm. Copyright © 2010 John Wiley & Sons, Ltd.

This paper addresses vehicle positioning based on TDOA ranging for an infrastructured scenario. In particular, a mobile vehicle can determine its current position by combining TDOA measurements collected from a certain number of road side units. At the design stage the road side units are assumed to be synchronized, but the effects of synchronization errors are taken into account at the analysis stage. The performance assessment shows that the proposed approach can outperform a GPS receiver typically available for the automotive market.

We propose a new approach to adaptive detection of coherent signals backscattered by possible point-like targets in the context of electronic warfare; in fact, the possible target signal is buried in thermal noise, clutter, noise-like interferers and, possibly, coherent (i.e., deceptive ECM) interferers. We assume a network of radars: for a given cell under test only a subset of the radars receives ECM signals. Training data containing thermal noise, clutter, and noise-like interferers are available. The problem at hand is solved resorting to a two-stage detection strategy: first, the subset of radars under ECM is estimated; then, a proper detection strategy resorting to W-ABORT based detectors for radars under ECM is implemented. The performance assessment shows that the proposed solution is effective in presence of ECM systems.

We consider the problem of estimating instantaneous rates for a set of independent arrival flows from (possibly incomplete) passive observations. We introduce a hierarchical Bayesian model with an unknown hyperparameter, whose estimation yields in turn the minimum mean square error (MMSE) estimate of arrival rates for each flow. Such an approach is able to leverage the information from the ensemble of flows in order to improve the local estimate. Since hyperparameter estimation is not available in closed-form, we propose a much simpler estimator based on the best linear unbiased predictor (BLUP) that is computationally comparable to the conventional approach. Simulation results show that our scheme improves the estimation accuracy compared to conventional estimation based on the raw cumulative sum of the arrivals at each flow, especially for small sample sizes, and performs extremely close to the (much more complex) optimal MMSE estimator.

In this paper we consider a network of agents monitoring a spatially distributed traffic process. Each node measures the number of arrivals seen at its monitoring point in a given time-interval. We propose an asynchronous distributed approach based on a hierarchical Bayes model with unknown hyperparameter, which allows each node to compute the minimum mean square error (MMSE) estimator of the local arrival rate by suitably fusing the information from the whole network. Simulation results show that the distributed scheme improves the estimation accuracy compared to a purely decentralized setup and is reliable even in presence of limited local data. An ad-hoc algorithm with reduced complexity is also proposed, which performs very closely to the optimal MMSE estimator.

In this paper we present a simple procedure for estimating the mean probability of binary events from samples of heterogeneous size. The estimator is specifically designed to cope with heavy-tailed distribution of the data sample size. Starting from an heuristic identified in a previous work in the context of packet loss estimation, we distill a general hierarchical estimation problem where different-sized samples from multiple binary sources are available and the goal is to estimate the mean overall probability. We show by formal analysis that common estimators fail to provide a low-variance estimator for this problem when the distribution of the sample size is heavy-tailed. Then we propose a minimax optimization procedure to inject robustness, which ultimately yields a low-complexity estimator with general applicability. The derivation highlights also a surprising connection between the piecewise-linear structure of the estimator and the “water-filling” (max-min fair) structure arising as solution of a some resource allocation problems. Performance comparisons against a theoretical benchmark and conventional estimator are provided via Monte Carlo simulation.

Nowadays many transceivers adhere to full-digital design principles, with the basic operations performed in the analog domain (filtering, down-conversion) and the whole remaining processing postponed to the digital domain (including matched filtering and synchronization). Though the operations in the analog front-end are ideally reversible, hence no signal degradation should occur before the analog-to-digital conversion (thus preserving the information integrity), however the intrinsic non-idealities in the circuitry may introduce a certain level of distortion. In this paper we provide a simple and flexible approach for the compensation of these effects. We derive useful formulas for obtaining a digital compensation filter by means of a direct transformation of the analog system coefficients. This allows to easily perform the compensation in the digital domain via fast Fourier transform. To illustrate the procedure, the case-study of a ultra-wide band receiver is addressed. Numerical results show that an improvement of more than 1 dB can be obtained at bit-error rate of interest for the applications. Sample code is also provided for an easy implementation.

In this Chapter we address the problem of detecting "anomalies" in the global network traffic produced by a large population of end-users. Empirical distributions across users are considered for several traffic variables at different timescales, and the goal is to identify statistically-significant deviations from the past behavior. This problem is casted into the framework of hypothesis testing. We first address the methodology for dynamically identifying a reference for the null hypothesis ("normal" traffic) that takes into account the typical non-stationarity of real traffic in volume and composition. Then, we illustrate two general distribution-based detection approaches based on both heuristic and formal methods. We discuss also operational criteria for dynamically tuning the detector, so as to track the physiological variation of traffic profiles and number of active users. The Chapter includes a final evaluation based on the analysis of a dataset from an operational 3G network, so as to show in practice the detection of real-world traffic anomalies.

In this paper we present a Maximum Likelihood (ML) trajectory estimation of a mobile node from Received Signal Strength (RSS) measurements. The reference scenario includes a number of nodes in fixed and known positions (anchors) and a target node (blind) in motion whose instantaneous position is unknown. We first consider the dynamic estimation of the channel parameters from anchor-to-anchor measurements, statistically modeled according to the well-known Path-Loss propagation model. Then, we address the ML estimation problem for the position and velocity of the blind node based on a set of blind-to-anchor RSS measurements. We compare also the algorithm with a ML-based single-point localization algorithm, and discuss the applicability of both methods for slowly moving nodes. We present simulation results to assess the accuracy of the proposed solution in terms of localization error and velocity estimation (modulus and angle). The distribution of the localization error on the initial and final point is analyzed and closed-form expressions are derived.

Current Radio-Frequency Identification (RFID) technology involves two types of physical devices: tags and reader. The reader combines in a single physical device transmission (to the tags) and reception (from the tags) functions. In this paper we discuss an alternative approach, where receive functions are performed by a separate device called "RFID listener". This allows distributed tag-sensing schemes where one transmitter coexists with multiple listeners. We discuss pros and cons of both approaches and present our implementation of a passive RFID listener on GNU Radio. Our implementation is a basis for experimenting with future distributed listener-based systems, but it can be also used as a cheap and flexible protocol analyzer for currently available commercial RFID readers.

In this work we address the problem of estimating the network-wide packet loss rate across the radio access section of a 3G cellular network. The reference scenario consists of a passive monitoring probe located in the Core Network. The probe counts the number of TCP packets directed to each individual mobile terminal and, from the analysis of (un)acknowledged packets, infers the loss ratio for each individual terminal. The problem is then to derive a synthetic indicator representative of the network-wide packet loss, which can be used to detect large- scale performance drifts and network anomalies. We show that common simplistic indicators like the total rate of lost packets (across all terminals) and the average per-terminal loss rate do not work well in the general case. The key problem is the large disparity of traffic volume across individual terminals. In this contribution we formulate the problem in terms of optimal statistical inference and provide a set of robust near-optimum estimators that are relatively simple to implement. We validate the proposed estimators with simulations in synthetic scenarios and provide results from a real operational 3G network. Complex network infrastructures like third-generation (3G) cellular networks are exposed to errors and attacks. Passive packet-level monitoring can play an important role in support- ing the operation and troubleshooting of such systems. Sum- mary statistics of network-layer performance variables (delay, RTT, loss, throughput etc.) can be extracted from passive probes and used as synthetic indicators for the network health status. Sudden deviations in the otherwise regular temporal behavior of such indicators might point to network problems. Change-point detection methods can be used to automatically identify anomalies and raise timely alarms. In this work we focus on the use of network-wide packet loss statistics obtained from the passive analysis of TCP packets at a single measurement point. The reference scenario consists of a passive monitoring probe located in the Core Network (ref. Fig. 1). The probe counts the number of TCP packets directed to each individual mobile terminal and, from the analysis of (un)acknowledged packets, infers the number of packet loss events. The problem is then to derive a synthetic indicator representative of the network-wide packet loss. The ultimate goal is to use such indicator to detect network problems and large-scale performance drifts. A common simple approach is to use as the summary indicator the ratio of loss events over the total transmitted packets across all the terminals. Another possible method is to compute the individual per-terminal loss ratios and take the arithmetic average. We show that such simplistic approaches do not always work well in practical scenarios. The key issue is the large disparity found in the distribution of the number of packets per terminal, which is due to the typically large disparity in traffic intensity of individual users: many users generate very low traffic, while a few ones generate very high volumes. For the two simplistic summary indicators introduced above, such disparity translates directly into an increased variance of the estimate. In this work we formalize the problem at hand as an optimal estimation problem. The optimal solution can be found via Bayesian approach (9), but at the cost of a relatively high resolution complexity which might hamper the practical adoption of such schemes. In this contribution we focus on the derivation of simple yet robust near-optimum estimators.

In this paper we investigate the dynamics of one-way delays in an operational mobile core network. Our final goal is to develop anomaly detection schemes for the packet delay process in order to reveal network and equipment problems. This requires a preliminary exploration of the delay distribution in the core network, which we undertake in this study. We present one-way delay measurements extracted from passive traces captured at an operational General Packet Radio System (GPRS)/Universal Mobile Telecommunications System (UMTS) network. We find that queuing is the only source of delay at Gateway GPRS Support Nodes (GGSN), while for Serving GPRS Support Nodes (SGSN) the mobility and flow-control further complicate the characterization of the ldquonormalrdquo delay behavior. Moreover, the presence of unwanted traffic has an impact on the delay statistics and should be taken into account. We find that information about actual bandwidth conditions in the Radio Network may be inferred by investigating packet delays in the core network. Our explorative results are promising about the possibility of leveraging one-way delay measurement for troubleshooting in such networks.

This paper presents an open source software–defined radio (SDR) tool compliant with IEEE 802.15.4, which incorporates features for the collection and processing of received signal strength (RSS) measurements from incoming packets. The implementation includes RSS indicator (RSSI) feature, data handling and application code for channel estimation, ranging and localisation. The tool can be used for experimenting with RSSI measurements from over–the–air IEEE 802.15.4 packets. Moreover, it enables experimental research on RSS–related aspects that is not possible with commercial devices, even development boards, which are closed platforms. To illustrate the tool usage, we show experimental results based on packets sniffed from commercial ZigBee nodes. Moreover, we present a complete application to the problem of localisation in wireless sensor networks (WSNs), with experimental comparison of different algorithms. Finally, we highlight some issues in the RSSI calculation, showing how different aspects of the RSS computation can be investigated at the finest granularity, hence allowing researchers and practitioners to experiment down to the PHY layer.

In this work we discuss the use of passive measurements of TCP performance indicators in support of network operation and troubleshooting, presenting a case-study from a real 3G cellular network. From the analysis of TCP handshaking packets measured in the core network we infer Round-Trip-Times (RTT) on both the client and server sides separately for UMTS/HSPA and GPRS/EDGE sections. We also keep track of the relative share of packet pairs which did not lead to a valid RTT sample, e.g. due to loss and/or retransmission events, and use this metric as an additional performance signal. In a previous work we identified the risk of measurement bias due to early retransmission of TCP SYNACK packets by some popular servers. In order to mitigate this problem we introduce here a novel algorithm for dynamic classification and filtering of early retransmitters. We present a few illustrative cases of abrupt-change observed in the real network, based on which we derive some lessons learned about using such data for detecting anomalies in a real network. Thanks to such measurements we were able to discover a hidden congestion bottleneck in the network under study.