Traffic congestion affects both urban and interurban roads, harming economic efficiency and sustainability. Hard Shoulder Running (HSR) has been implemented to enhance highway capacity. However, its effectiveness is constrained by diminished driver awareness resulting from the suboptimal performance of Variable Message Signs (VMSs). This study explores how Cooperative Intelligent Transport Systems (C-ITS) and Vehicle-to-Everything (V2X) technologies can enhance HSR utilization and highway performance. Focusing on the A4 Turin-Venice Italian highway, this work includes experimental measurements on radio propagation and a design study on the Road Side Unit (RSU) deployment. The benefits provided by the use of C-ITS for dynamic lane advisory on the vehicles, is assessed by simulating traffic conditions along the Milan's urban corridor of the A4 highway (one of the most congested in Europe) using a professional microsimulation, calibrated on real traffic data with varying V2X adoption rate at vehicles. Results show that V2X technology significantly improves traffic flow, particularly during peak congestion. The study underscores the importance ofC-ITS in future smart road infrastructure and traffic management.

Federated Learning (FL) has emerged as a pivotal privacy-preserving machine learning paradigm, enabling collaborative model train across distributed data sources. A main issue, however, is the lack of comprehensive testing environments that can accurately emulate real-world conditions at a large scale, particularly the impact of network dynamics and device capabilities on FL algorithm performance. To this end, we introduce FederNet, a novel platform designed to facilitate the development and testing of FL algorithms with realistic network and device emulation. We show how the proposed system provides a versatile platform for researchers to evaluate the performance, robustness, and scalability of FL algorithms under diverse and configurable scenarios. We describe the FederNet architecture, detail its network and device emulation capabilities, and outline potential use cases that demonstrate its utility in advancing FL research. By bridging the gap between algorithmic development and practical deployment challenges, FederNet aims to accelerate the innovation and adoption of FL technologies.

The widely adopted satellite-based positioning systems have shown limitations in meeting the needs of emerging mobile radio network services, which require consistent, high-quality, real-time positioning data. This study introduces RUN-O-RAN, an innovative network-based ranging system integrated as a micro-service within the 5th generation (5G) Open Radio Access Network (O-RAN). RUN-O-RAN makes opportunistic use of uplink reference signals and is robust against hardware and network impairments. It offers seamless deployment, user transparency, and adaptability to varying application requirements, leveraging the programmability of the network. Through a custom-built testbed based on software-defined 5G base stations (gNBs) and commercial user equipment, we comprehensively evaluate this solution across diverse scenario sets and compare its accuracy against satellite-based positioning. The achieved results demonstrate how this system represents the first effective localization O-RAN micro-service.

Vehicle-to-Everything (V2X) communication enhances road safety and traffic efficiency by supporting Cooperative Intelligent Transport System (C-ITS) services. This study introduces the Vehicle Telemetry Information Message (VTIM), designed to share real-time tyre and road surface condition data from vehicle to roadside unit (RSU). We validate the system in a field test using a connected vehicle equipped with ITS-G5 communication and smart tyre technology. Our analyses evaluate V2X latency in telemetry transmission, along with bitrate and packet error rate at the receiving end. Experimental results confirm the system's ability to facilitate timely and reliable data exchange, enabling vehicle telemetry and road surface monitoring (e.g., water detection) for enhanced road safety services.

Connected and Automated Vehicles (CAVs) are revolutionizing road transport by offering enhanced safety, efficiency, and sustainability. A key requirement for their safe operation on roads is the availability of highly accurate positioning information with ultra-low latency. This study presents the design and assessment of an infrastructure-based vehicle positioning system, focusing on the latency involved in transmitting position information to vehicles using Vehicle-to-Everything (V2X) connectivity. Specifically, we consider a roadside positioning infrastructure that integrates an Ultra Wideband (UWB) technology for positioning and an ITS-G5 V2X connectivity for communication. We measure and analyze the round trip time of the V2X communication link to gain insights on the latency performance. The positioning performance is also analyzed by comparing the trajectory followed by the vehicle with the one planned by the onboard control system. On-field evaluations are conducted in a highway tunnel, demonstrating the ability of the infrastructure localization system in successfully enabling autonomous navigation.

Federated learning (FL) can be used to distribute machine learning (ML) tasks across edge and Internet of Things (IoT) devices with limited resources. FL provides an alternative and much more practical solution to classical artificial intelligence (AI), which requires moving large data volumes to energy-hungry data centers. On the other hand, sustainability of FL processes should be accurately quantified as limiting energy consumption might require sacrificing accuracy. This article proposes a framework for real-time monitoring of energy and green house gas (GHG) emissions (carbon footprints) of FL systems. The framework is developed for both classical FL policies relying on the parameter server and emerging fully decentralized ones. The proposed approach considers, for the first time, the impact of ML model quantization and sparsification on the energy/carbon budget while also discussing novel gradient tracking (GT) FL strategies that are robust to data heterogeneity but require higher communication bandwidth. General guidelines for energy-efficient designs are discussed based on several case studies on real datasets. This article quantifies the energy footprint of continual FL processes that implement periodic adaptation on new data as foreseen by emerging IoT industry verticals. Results show that centralized FL is advantageous when strict carbon budgets are imposed or energy-inefficient (80%), provided the ML model compression is properly tuned.

Bayesian Federated Learning (FL) policies enable multiple nodes to collaboratively train a shared Machine Learning (ML) model while accounting for the uncertainty of its predictions. This is accomplished by estimating the global posterior distribution in the model parameter space. Currently, Bayesian FL strategies are impaired by large communication costs that need to be reduced to provide more sustainable training platforms. This letter investigates the impact of compression strategies in centralized Bayesian FL setups, where a Parameter Sever (PS) is tasked to supervise the learning process. The goal is to study how compression affects the ability of Bayesian FL systems to provide high-quality, yet well-calibrated ML models. The analysis is carried out in the healthcare domain, where the prediction reliability is particularly critical, focusing on a medical imaging task. Numerical results show that applying aggressive compression policies highly reduces the ability of Bayesian FL systems to provide accurate and reliable ML models. On the contrary, light compression stages maximize accuracy and calibration at the cost of larger communication overheads.

This paper explores the potentialities of deploying vehicular Cooperative Positioning (CP) systems in urban scenarios utilizing real-world data collected via experimental campaigns. We examine the case of two prototype vehicles equipped with LiDAR sensors for perceiving their surrounding environment and with Global Navigation Satellite System (GNSS) receivers for positioning. The considered use case focuses on the cooperative detection of static landmarks, to be used for improving the vehicles’ GNSS positioning. The experimental campaign points out a severe degradation in ego vehicle localization performances due to complex multipath propagation experienced in the urban scenario. To cope with such a problem, we integrate into the CP system a compensation method able to mitigate the position bias originating from the adverse propagating conditions. Experimental results show that integrating the developed compensation into the CP solution enables an accurate detection of the landmark positions, leading to an enhancement of the vehicle localization accuracy.

Global air traffic has been steadily growing since the beginning of the new century, increasing the need for accurate and reliable positioning in real-time tracking of multiple aircrafts. This paper presents an Interacting Multiple Model (IMM) tracking solution and an assessment of a real Wide Area Multilateration (WAM) aircraft tracking scenario, where measurements from distributed Ground Stations (GSs) are gathered by a Central Processing Station (CPS) running the tracker. The assessment considers a main European airport, where a network of 44 GSs is used to monitor a congested area of $300 \times 250 \mathrm{~km}$. Tracking measurements refer to time differences of arrival (TDOAs) computed starting from the time of arrival (TOA) measured over downlink signals. Specifically, this work considers messages sent over the aviation transponder interrogation mode S. We present the results on IMM-based WAM tracking on airborne maneuvering targets, showcasing the improvements with respect to the conventional Automatic Dependent Surveillance - Broadcast (ADS-B) solution based on global navigation satellite systems (GNSSs).

In recent years, cooperative positioning technologies have emerged as promising augmentation systems for providing high-accuracy positioning (HAP) in cooperative intelligent transportation systems (C-ITS). Among the approaches, implicit cooperative positioning (ICP) takes advantage of shared target detections between vehicles to create common reference points for localization refinement. Their performance, however, is limited by reliance on predefined parametric models, low scalability and communication overhead. To address these problems, this paper introduces a deep multi-agent reinforcement learning (MARL) framework modelled as a decentralized-partially observable Markov decision process (Dec-POMDP). We propose an ICP-multi-agent proximal policy optimization (MAPPO) algorithm, where distributed agents (i.e., the connected vehicles) learn their dynamics and those of the surrounding targets by performing belief estimation over dynamic cooperation graphs that are continuously adjusted by de/activating communication links with neighbors agents. A C-ITS scenario is simulated in a CARLA environment accounting for realistic vehicle dynamics and inter-vehicle communications. The findings reveal that our ICPMAPPO algorithm, leveraging dynamic decentralized execution and centralized training, outperforms ICP in terms of positioning accuracy and communication efficiency.

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

This paper focuses on highly precise localization and tracking of vehicles in race circuits, where centimeter-level accuracy is required for safety and for enabling complex maneuvering. Recently, the Annealed Stein Particle Filter (ASPF) has been proposed as a promising Bayesian tracking tool for tracking, showing its superior performances against conventional Bayesian filtering methods, such as the Extended Kalman Filter (EKF) and the Particle Filter (PF). Despite its excellent performances, the ASPF entails large computational complexity, making it unsuitable for highly dynamic vehicular scenarios. To address this shortcoming, we propose a Deep Unfolded ASPF (DU-ASPF), a novel Bayesian tracking algorithm integrating the deep unfolding paradigm where the ASPF operations are rearranged into a sequential structure with learnable weights. Experimental results using raw Ultra-Wide Band (UWB) measurements show that the DU-ASPF is able to substantially speed up the tracking process while maintaining the ASPF accuracy.

In the rapidly evolving domain of forthcoming 6th generation (6G) networks, achieving precise dynamic positioning down to the centimeter becomes critical, particularly in complex urban scenarios as those envisioned for cooperative intelligent transport systems (C-ITSs). To face the challenges introduced by severe path loss and blockages in new 6G frequency bands, machine learning (ML) provides innovative strategies to extract locational intelligence from wide-band space-time radio signals. This paper proposes the integration of Bayesian neural networks (BNNs) into cellular multi-base station (BS) tracking systems, where uncertainties of BNNs account for finite training sets and measurement errors. Our approach utilizes a deep learning (DL)-based autoencoder (AE) structure that exploits the full channel impulse response (CIR) to infer location-centric attributes in both line-of-sight (LoS) and non-LoS (NLoS) conditions. Validations in a 3rd Generation Partnership Project (3GPP) compliant urban micro (UMi) setting, simulated with ray-tracing and traffic simulations, demonstrate the superior performances of BNN-based tracking with respect to both traditional geometric-based tracking methods and state-of-the-art DL models.

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particu-larly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed archi-tecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication pro-tocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and to increase trustworthiness.

In the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm.

Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posterior distributions over the model parameters space. This paper focuses on Bayesian FL implemented in a Device-to-Device (D2D) network via Decentralized Stochastic Gradient Langevin Dynamics (DSGLD), a recently introduced gradient-based Markov Chain Monte Carlo (MCMC) method. Based on the observation that DSGLD applies random Gaussian perturbations to the model parameters, we propose to leverage channel noise on the D2D links as a mechanism for MCMC sampling. The proposed approach is compared against a conventional implementation of frequentist FL based on compression and digital transmission, highlighting advantages and limitations.

This paper considers the problem of cooperative localization of passive objects in a vehicular environment through the fusion of lidar point clouds collected at different moving vehicles and sent to the road infrastructure. Object localization is then used to improve the position estimate of vehicles according to the implicit cooperative positioning paradigm. At first, each vehicle uses a deep neural network (a 3D object detector) to process its lidar point cloud and localize static objects. Then, the set of estimated bounding boxes is sent to the road infrastructure, which performs data association through a message passing neural network to identify the set of measurements originating from the same detected object. Lastly, cooperative localization of objects is backward used to improve vehicle positioning. Simulations of a realistic cooperative lidar sensing scenario with CARLA software highlight improved positioning compared to non-cooperative tracking.

The exchange of sensing information through Vehicle-to-Everything (V2X) communications enables the development of cooperative systems for localization augmentation in connected automated vehicles. In V2X scenarios, the integration of measurements from multiple vehicles enhances the environmental perception which is of the utmost importance for enhanced safety services. In this paper, we propose a Deep Neural Network (DNN)-assisted cooperative localization method that relies on a centralized road infrastructure and a network of lidar sensors at vehicles. The proposed algorithm is referred to as DNN Implicit Cooperative Positioning (DNN-ICP) and performs two tasks. At first, each vehicle processes its lidar point cloud by a 3D object detector to identify static objects in the surrounding. Then, the estimated objects are collected at the road infrastructure which uses the aggregated information to improve the localization. Numerical results in a realistic vehicular scenario are presented to quantify the improvement provided by DNN-ICP with respect to a non-cooperative vehicle positioning scheme, showing the reduction of uncertainty on vehicle positioning.

This paper addresses the problem of indoor positioning, where complex propagation characteristics call for advanced Bayesian filters for accurate position tracking. We propose to employ the Stein Particle Filter (SPF) to approximate the posterior distribution with a set of particles, using the Stein Variational Gradient Descent (SVGD) method. A novel SPF tracking method, referred to as Annealed Stein Particle Filter (A-SPF), is designed by exploiting the annealed scheduling of SVGD. Compared to SPF, the A-SPF captures multi-modal distributions that easily arise in indoor localization problems from multipath without requiring higher number of particles. Experimentation activities are carried out in two indoor scenarios, an office and a machinery area, where Ultra Wide-Band (UWB) technology is used to collect raw data. Results show the improved positioning performance of the proposed A-SPF compared to conventional solutions based on extended Kalman filter and particle filter, as well as with standard SPF.

In application verticals that rely on mission-critical control, such as cooperative intelligent transport systems (C-ITS), 5G-Advanced networks must be able to provide dynamic positioning with accuracy down to the centimeter level. To achieve this level of precision, technology enablers, such as massive multiple-input multiple-output (mMIMO), millimeter waves (mmWave), machine learning and cooperation are of paramount importance. In this paper, we propose a cooperative deep learning (DL)-based positioning methodology that combines these key technologies into a new promising solution for precise 5G positioning. Sparse channel impulse response (CIR) data are used by the positioning infrastructure to extract position-dependent features. We model the problem as a joint task composed of non-line-of-sight (NLOS) identification and position estimation which permits to suitably handle geometrical location measurements and channel fingerprints. The network of base stations (BSs) automatically steers between egocentric (in case of NLOS) and cooperative (for LOS) positioning mode. We perform extensive standard-compliant simulations in a 5G urban micro (UMi) vehicular scenario obtained by ray-tracing and simulation of urban mobility (SUMO) software. Results show that the proposed cooperative DL architecture is able to outperform conventional geometrical positioning algorithms operating in LOS by 47%, achieving a median error of 71 cm on unseen trajectories.

In mission-critical verticals such as automated driving, 5G-advanced networks must provide centimeter-level dynamic positioning along with ultra-reliable low-latency communication services. Massive Multiple-Input Multiple-Output (mMIMO) and millimeter waves (mmWave) are the key enablers, allowing high accuracy angle and delay estimation. Still, extracting such information from highly-dimensional Channel Impulse Responses (CIRs) results in a complex task, due to channel sparsity and intermittent blockage. In this paper we focus on non-line-of-sight (NLOS) identification from CIR data, proposing a Deep Autoencoding Kernel Density Model (DAKDM) to characterize the statistics of the channel latent features. We formulate the problem as a semi-supervised anomaly detection task in which only LOS samples, i.e., normal data, are adopted for training. DAKDM is a single-stage training model that takes as input the full CIR thanks to an AutoEncoder (AE) structure. The proposed method is able to learn the latent distribution by means of a Kernel Density Estimator (KDE) in combination with a deep learning likelihood network. We validate the proposed solution in a 5G Urban micro (UMi) vehicular scenario. Results show that the proposed model can significantly outperform conventional algorithms and obtain similar performances to variational Bayes algorithms at one tenth of the inference time.

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle’s ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-calledautofocusalgorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocusrequirementsin typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets’ localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.

In recent years, automotive systems have been integrating Federated Learning (FL) tools to provide enhanced driving functionalities, exploiting sensor data at connected vehicles to cooperatively learn assistance information for safety and maneuvering systems. Conventional FL policies require a central coordinator, namely a Parameter Server (PS), to orchestrate the learning process which limits the scalability and robustness of the training platform. Consensus-driven FL methods, on the other hand, enable fully decentralized learning implementations where vehicles mutually share the Machine Learning (ML) model parameters, possibly via Vehicle-to-Everything (V2X) networking, at the expense of larger communication resource consumption compared to vanilla FL approaches. This paper proposes a communication-efficient consensus-driven FL design tailored for the training of Deep Neural Networks (DNN) in vehicular networks. The vehicles taking part in the FL process independently select a pre-determined percentage of model parameters to be quantized and exchanged on each training round. The proposed technique is validated on a cooperative sensing use case where vehicles rely on Lidar point clouds to detect possible road objects/users in their surroundings via DNN. The validation considers latency, accuracy and communication efficiency trade-offs. Experimental results highlight the impact of parameter selection and quantization on the communication overhead in varying settings.

Automated driving requires a huge number of on-board sensors to provide advanced functionalities, from parking assistance to emergency braking and environment mapping for target recognition/classification. While low-cost automotive-legacy radars are mostly used for target detection due to their limited angular resolution, vehicular Synthetic Aperture Radar (SAR) is emerging as a promising imaging solution, provided that the motion is known with high accuracy. This paper assesses the benefits of a navigation-augmented SAR system exploiting multiple on-board sensors, e.g., Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometers and steering angle sensors. The results confirm the potential of the proposed multi-sensor-aided SAR system to obtain centimeter-level accurate images of the driving scenario.

This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different sub-problems, we propose a holistic approach for joint localization and tracking. The theory of graphical models is used to describe the statistical relationship between sensors, targets, and measurements. To jointly infer the states of sensors and targets, we use the statistical processing of belief propagation.

Federated learning (FL) is emerging as a new paradigm for training a machine learning model in cooperative networks. The model parameters are optimized collectively by large populations of interconnected devices, acting as cooperative learners that exchange local model updates with the server, rather than user data. The FL framework is however centralized, as it relies on the server for fusion of the model updates and as such it is limited by a single point of failure. In this paper we propose a distributed FL approach that performs a decentralized fusion of local model parameters by leveraging mutual cooperation between the devices and local (in-network) data operations via consensus-based methods. Communication with the server can be partially, or fully, replaced by in-network operations, scaling down the traffic load on the server as well as paving the way towards a fully serverless FL approach. This proposal also lays the groundwork for integration of FL methods within future (beyond 5G) wireless networks characterized by distributed and decentralized connectivity. The proposed algorithms are implemented and published as open source. They are also designed and verified by experimental data.

This paper proposes techniques for Beam Alignment (BA) in millimeter wave (mm-wave) Vehicle-to-Everything (V2X) communications with realistic modeling of the dynamic space-time multipath channels. Starting from existing mm-wave channel models, an extension to simulate consistent dynamics of the multipath parameters as vehicles move is introduced. Different BA techniques are then presented, where side location information is used to assist the selection of the optimal pair of beam-pointers. We claim the possibility to exploit the low-rank (LR) structure of the sparse mm-wave channel matrix, jointly with location-related long-term statistics, to avoid time-consuming scanning of the beamformer codebook. The proposed method uses pre-computed eigen-beamformers based on predicted vehicle location and pre-acquired dataset of geo-referenced long-term channel state information to align the beams. Performance analysis in realistic dynamic channel scenarios indicate that the proposed method outperforms conventional BA strategies, avoiding time-consuming beam sweeping procedures.

Millimeter wave (mmWave) wideband channels in a multiple-input multiple-output (MIMO) transmission are described by a sparse set of impulse responses in the angle-delay, or space-time (ST), domain. In this paper we consider the problem of channel estimation and we discuss subspace methods which exploit the low-rank (LR) algebraic structure of the MIMO channel matrix and the related slowly- and fast-varying features (angles/delays of arrival and fading amplitudes, respectively). The main drawback of the optimal LR method is the excessively slow convergence to the mean square error lower bound for invariant angles/delay and time-varying fading. In this paper, new suboptimal LR techniques are proposed to reduce the complexity and accelerate the convergence. Numerical results show that the proposed methods closely approach the asymptotic bound with a number of slots that is two order of magnitudes lower than the optimal method, providing significant performance gains in realistic mmWave propagation scenarios.

In Industry 4.0, real-time location systems are emerging as a key technology to improve the efficiency of industrial processes, as they allow to track any assets or material movement and collect data on their usage. Ultra Wideband (UWB) systems offer unrivaled localization accuracy, but they call for augmentation strategies in environments with complex propagation conditions such as plants or factories with high density of scattering objects and obstructions. In this paper, we focus on Bayesian filtering techniques to counterbalance the detrimental effects induced by non line of sight and dense multipath in a smart factory scenario. We first conduct a set of experimental tests with commercial devices in an industrial facility of Pirelli Tyre S.p.A. located in Milan, Italy. We then use the collected data to design and test augmentation algorithms based on Extended Kalman Filter (EKF) and Particle Filter (PF), fusing Time Difference of Arrival (TDoA) and Angle of Arrival (AoA) signals. Experimental results show that, despite the harsh environment, accurate localization is possible by fusion of hybrid measurements and integration of prior information on the target dynamics and the industrial propagation environment.

This paper focuses on advanced pointing strategies enabling high data-rate directional vehicular communications. New emerging technologies aim to meet the challenging performance requirements of enhanced Vehicle-to-Everything (eV2X) applications by using highly collimated beams, which must rely on a very precise beam alignment. In this work, Free-Space Optics (FSO) is considered, and a system architecture is introduced together with algorithms for an accurate alignment of laser beam. The presented architecture exploits on-board sensor data sharing among vehicles to predict the pointing directions for FSO, thus counteracting the detrimental effect of motion, vibrations and tilting of vehicles. A solution is proposed for an accurate prediction of the FSO pointing direction, based on the sharing of vehicle kinematic data over a parallel wireless control link, augmented by prior information extracted from digital maps of the driving environment. Simulation results point out the challenges of a FSO V2V communication and highlight the feasibility of the proposed solution, considering both state of the art technology and future perspective hardware.

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems. Rather than sharing and disclosing the training data set with the server, the model parameters (e.g., neural networks' weights and biases) are optimized collectively by large populations of interconnected devices, acting as local learners. FL can be applied to power-constrained Internet of Things (IoT) devices with slow and sporadic connections. In addition, it does not need data to be exported to third parties, preserving privacy. Despite these benefits, a main limit of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters; this has the drawback of a single point of failure and scaling issues for increasing network size. This article proposes a fully distributed (or serverless) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network by iterating local computations and mutual interactions via consensus-based methods. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing, with intelligence distributed over the end devices. The proposed methodology is verified by the experimental data sets collected inside an Industrial IoT (IIoT) environment.

Millimeter wave (mmWave) wideband channels in a multiple-input multiple-output (MIMO) transmission are described by a sparse set of impulse responses in the angle-delay, or space-time (ST), domain. These characteristics will be even more prominent in the THz band used in future systems. We consider two approaches for channel estimation: compressed-sensing (CS), exploiting the sparsity in the angular/delay domain, and low-rank (LR), exploiting the algebraic structure of channel matrix. Both approaches share several commonalities, and this paper provides for the first time i) a comparison of the two approaches, and ii) new versions of CS and LR methods that significantly improve performance in terms of mean squared error (MSE), computational complexity, and latency. We derive the asymptotic MSE bound for any estimator of the ST-MIMO multipath channels with invariant angles/delays and time-varying fading, with unknown angle/delay diversity order: the bound also accounts for the degradation introduced by sub-optimal separable channel models. We will show that in the considered scenarios both CS and LR approaches attain the bound. Our performance assessment over ideal and 3rdgeneration partnership project (3GPP) channel models, suitable for the fifth-generation (5G) and beyond of cellular networks, shows the trade-off obtained by the methods over various metrics: i) CS methods are converging faster than the LR methods, both attaining the asymptotic MSE bound; ii) the CS methods depend on the array manifold, while LR methods are independent of the array calibration; iii) CS solutions are more complex than LR solutions.

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSSs) combined with on-board sensors and high-resolution maps. In cooperative intelligent transportation systems, the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an implicit cooperative positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights, or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, and a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.

In this letter, an analytically tractable model based on diffraction theory is proposed to describe the perturbations of the electromagnetic propagation of radio signals caused by the presence of a moving object in the two-dimensional (2-D) area near the transmitting/receiving devices. This novel model is instrumental to the evaluation of non-cooperative device-free localization (DFL) systems as it allows to relate the received signal strength measurements of multiple radio links to the object size, orientation and position. The proposed model is validated experimentally using radio devices and it is used to derive closed-form fundamental limits to DFL accuracy, providing an analytical tool for DFL system design and network 2-D pre-deployment assessment.

Pedestrian dead reckoning (PDR) is widely used for indoor localisation. Its principle is to recursively update the location of the pedestrian by using step length and step heading. A common method to estimate the heading in PDR is to use magnetometer measurements. However, unlike outdoor environments, the Earth's magnetic field is strongly perturbed inside buildings making the magnetometer measurements unreliable for heading estimation. This paper presents a new method to reduce heading estimation errors when magnetometers are used. The method consists of two components. The first component uses a machine learning algorithm to detect whether a heading estimate is within a specific error margin. Only heading estimates within the error margin are retained and passed to the second component, while the other estimates are discarded. The second component uses data fusion to average the heading estimates from multiple people walking in the same direction. The rationale of this component is based on the observation that magnetic perturbations are often highly localised in space and if multiple people are walking in the same direction, then only some of their magnetometers are likely to be perturbed. Data fusion between users can be carried out in a distributed manner by using a consensus algorithm with information sharing over wireless links. We tested the performance of our method using 92 datasets. The method is shown to provide an average heading estimate error of approximately 2°, which is more than 6-fold lower than the error of the heading estimate based only on raw magnetometer measurements (without any filtering and fusion). Assuming highly accurate step-length observation, the improved heading estimation leads to an average localisation accuracy of 55cm, which is an 80% improvement over PDR localisation using only raw magnetometer measurements.

In peer-to-peer wireless networks, knowledge of the channel quality information of multiple links is fundamental to calibrate cooperative communication/processing techniques and design efficient resource sharing strategies. This paper is focused on distributed estimation algorithms that enable the network to self-learn key environment-dependent parameters that rule the channel quality of all links in the network. Considering an indoor scenario with fixed wireless terminals and moving objects/people in the environment, we parameterize the channel quality of each link in terms of path-loss and Rician K-factor, modelling these macro-parameters according to a site-specific stochastic model. Contribution of the paper is twofold: a measurement campaign carried out with IEEE 802.15.4 devices to validated the stochastic model; distributed algorithms to estimate the environment-dependent parameters of the model. Various schemes of weighted average consensus are proposed to enable the convergence to the equivalent global (centralized) estimate. Performance analysis is carried out in terms of convergence speed, error at convergence and communication overhead using both experimental and simulated data.

Intelligent transportation systems have enormous potential for improving the quality of our lives. They rely on traffic monitoring and control infrastructures to enable an efficient management of mobility. A crucial task is the estimation or prediction of traffic flows by large-scale sensor networks, which is a topic that has been attracting increasing attention in recent years because of its relevance in traffic control over urban areas or freeways. In this paper, we propose an innovative stochastic method for vehicular traffic estimation based on a distributed reconstruction of the density field through the cooperation of smaller monitoring subnetworks. The method guarantees high accuracy (because of information sharing) and, at the same time, moderate computational cost (due to distributed processing). Moreover, subnetworks do not need to exchange sensitive information (e.g., raw data) but simply traffic beliefs. We evaluate the performance of the method on simulated single-lane road scenarios, highlighting the potential benefits of the cooperative approach. As an example of application, we consider a fragmented monitoring scenario characterized by several sensor failures and we show how the proposed approach can overcome the problem related to the sensor malfunctions leveraging on information shared with neighboring subnetworks.

Traffic control is essential for the achievement of a sustainable and safe mobility. Monitoring systems deployed over the roads collect a great amount of traffic data that must be efficiently processed by statistical methods to draw traffic macroparameters that are needed for control operations. In this paper we propose a particle filtering approach to estimate the density over a road network starting from noisy and sparse measurements provided by road-embedded sensors. We propose a new Bayesian framework based on the link-node cell transmission model to take into account the stochastic behavior of traffic and the hysteresis phenomenon that are typically observed in real data. Numerical tests show that the estimation method is able to reliably reconstruct the traffic field even in case of very sparse sensor deployments.

In this paper, we develop a partner selection protocol for enhancing the network lifetime in cooperative wireless networks. The case-study is the cooperative relayed transmission from fixed indoor nodes to a common outdoor access point. A stochastic bivariate model for the spatial distribution of the fading parameters that govern the link performance, namely the Rician K-factor and the path-loss, is proposed and validated by means of real channel measurements. The partner selection protocol is based on the real-time estimation of a function of these fading parameters, i.e., the coding gain. To reduce the complexity of the link quality assessment, a Bayesian approach is proposed that uses the site-specific bivariate model as a-priori information for the coding gain estimation. This link quality estimator allows network lifetime gains almost as if all K-factor values were known. Furthermore, it suits IEEE 802.15.4 compliant networks as it efficiently exploits the information acquired from the received signal strength indicator. Extensive numerical results highlight the trade-off between complexity, robustness to model mismatches and network lifetime performance. We show for instance that infrequent updates of the site-specific model through K-factor estimation over a subset of links are sufficient to at least double the network lifetime with respect to existing algorithms based on path loss information only.

This paper is focused on evolved GSM/EDGE systems complying to the new feature Voice services over Adaptive Multi-user channels on One Slot (VAMOS), recently introduced in the 3GPP GSM/EDGE radio access network (GERAN) standard. VAMOS enables the transmission of two GSM voice streams on the same radio resource through the so called Orthogonal Sub Channel (OSC) multiple access technique which aims at doubling the number of users served by a cell. When adopting this feature, the GSM network experiences a mixture of in-cell and out-of-cell interference which has to be handled by advanced receivers with interference rejection capabilities. In this paper, a novel two-stage receiver is proposed and tested for the uplink of GSM VAMOS systems. The new scheme combines a pre-filtering stage for out-of-cell interference mitigation with a multi-user detector (MUD) for joint equalization of the two multiplexed OSC streams. The approach is suited for either single-antenna or multi-antenna base stations and can accommodate multiple filters for each user to cope with complex multipath propagation scenarios. The new solution is compared with other existing interference cancellation techniques here applied to the specific OSC scenario. Numerical results show significant performance gains in realistic multi-cell simulated scenarios.

Radio imaging allows to locate and track passive targets (i.e., not carrying electronic device) moving in an area monitored by a dense network of low-power and battery-operated wireless sensors. The technology is promising for a wide number of applications ranging from intrusion detection to emergency and rescue operations in critical areas. In this paper, a new approach is proposed where both the average and the variance of the fluctuations of the received signal strength (RSS) induced by the target movement over the links are jointly and optimally exploited for sensing the target location. A link-layer protocol is developed on top of an existing IEEE 802.15.4 compliant PHY/MAC layer to allow the wireless nodes to cooperatively exchange RSS measurements. A log-normal model is defined to relate these measurements to the target location. Grid-based Bayesian estimation is proposed for real-time mobile positioning. The proposed system is validated by an indoor experimental study that analyzes the problem of model calibration and compares the performance of different localization algorithms.

We consider one of the latest feature included in the Release 9 of the GSM/EDGE standard: the Orthogonal Sub Channel (OSC) transmission scheme. OSC aims at doubling the cell capacity by multiplexing two co-cell users on the same radio resource. In this work we deal with the challenge of finding the optimum pairing strategy among co-cell OSC users exploiting the Adaptive QPSK (AQPSK) modulation in both up- and down-link scenarios. The aim of the proposed scheduling algorithm is to i) find the best association among the users and the available OSC logical channels, and ii) select the optimum transmitting powers. The criterion for optimization is the minimization of the overall transmitted power constrained to service quality targets. The proposed scheduling algorithm is performed locally at the BS, exploiting channel state information reported by the users. Numerical results show significant power saving provided by the algorithm in heterogeneous scenarios with variable cell load.

Multiple input multiple output (MIMO) antenna systems with orthogonal frequency division multiple access (OFDMA) is the most promising combination of technologies for high data-rate services in next generation wireless networks. Performance assessment of multi-cell systems based on these technologies is of crucial importance in the deployment of broadband wireless standards such as WiMAX and 3GPP LTE. In this paper, we define an analytical framework for the assessment of the average error probability of multi-cell bit-interleaved convolutionally-coded MIMO-OFDMA systems. Both coordinated and randomized multi-user access strategies are considered for interference mitigation. In such a scenario, the analytical framework must account for the correlation of the fading channel over the space-frequency domain and possible non-stationary features of the multicell interference (due to subcarrier randomization). The analysis is carried out for different multi-antenna strategies, ranging from beamforming systems for mitigation of out-of-cell directional interference to spatial diversity schemes based on orthogonal space-time coding. Numerical results corroborate the proposed analytical framework for heterogeneous environments and a wide range of system configurations.

Cooperative transmission techniques for ad hoc and wireless sensor networks are known to increase the network lifetime. Indeed, the improved spatial diversity allows a more efficient energy usage. Under the Rayleigh fading assumption, the selection of cooperative partners is typically based on the knowledge of the average channel power. However, Rayleigh fading is not a suitable model in a large number of practical scenarios, in particular for indoor-to-outdoor applications. In these scenarios additional information of the fading distribution is needed for partner selection. The main focus of this work is to provide an analytical framework to evaluate the impact of the fading statistics on partner selection algorithms. A distributed multi-link channel model is derived from indoor-to-indoor and indoor-to-outdoor channel measurements in order to simulate practical scenarios where the proposed analytical framework is tested. Finally, we introduce a novel partner selection strategy that exploits the distributed knowledge of the effective coding gains provided by the wireless links fading statistics.

Self-localization in ad-hoc sensor networks is becoming a crucial issue for several location-aware applications. This technology implies the combination of absolute anchor locations with relative inter-node information exchanged on a peer-to-peer basis. In this paper we investigate a distributed algorithm and fundamental performance bounds for Bayesian cooperative localization in stochastic networks. Nodes are assumed to be randomly deployed within a finite space according to a prior distribution. Bayesian inference is performed through an iterative local message passing procedure based on belief propagation and particle-filtering message representation. The algorithm performance is analyzed for a simplified scenario in which unknown node positions are randomly scattered along a line segment and anchors are fixed. Global Cramer-Rao bounds are derived and compared to the performance of the distributed algorithm.

This paper focuses on the uplink of multicell IEEE 802.16-d WiMax systems with OFDM modulation and antenna array at the base stations. We propose an analytical framework to assess the average error probability of all the different transmission modes over space-time dispersive Rayleigh fading channels. The proposed method takes into account the effects of the multilevel modulation, the error correction capability of the concatenated code, the interleaving scheme, the power-angle structure of the inter-cell interference and the array processing at the base station. Simulation results corroborate the proposed analysis for a IEEE 802.16-d cellular system over different propagation scenarios.

A multicell WiMax system which supports orthogonal frequency division multiplexing (OFDM) and antenna arrays at the base stations is considered in this paper as conforming to the IEEE 802.16-2004 standard. Focusing on the uplink, we propose an analytical framework to assess the average error probability of the system over time-dispersive (or, equivalently, frequency selective) and space-dispersive (due to the antenna array) Rayleigh fading channels. The proposed method takes into account the effects of the correlation of the channel gains over the space-frequency domain, the power-angle structure of the inter-cell interference, the array processing at the base station and the interleaving scheme. Simulation results corroborate the proposed analysis for a IEEE 802.16-2004 cellular system over different propagation scenarios.

This letter presents an adaptative soft-based method for channel estimation in turbo receivers. The proposed approach is based on the particular algebraic structure of multipath Rayleigh-fading channels, and it is suited for mobile systems where the multipath pattern (namely, the times of delay) changes slowly over the time. The method is implemented through a rank-and-subspace tracking algorithm that allows to adapt the estimate to the multipath variations and also to reduce the computational cost with respect to the batch implementation based on eigenvalue decomposition. A performance analysis, in terms of mean square error of the channel estimate and bit error rate, shows the advantages of the proposed technique in communications over time-varying wireless channels

This paper focuses on the development of a radio localization technique for a wireless sensor network infrastructure where a large number of simple power-aware nodes are spread in indoor environments. Fixed and moving nodes exchange radio messages but can only measure mutual power figures such as the received signal strength (RSS) indicator. Local maximum likelihood estimation from propagation models suffers from false alarm problems due to incorrect position information, complex indoor propagation effects and simple hardware radio architectures. Here, we propose a Bayesian approach to estimate and track the position of a moving node from power maps obtained through field measurements. To lower the computational power required by grid-based algorithms, we exploit particle filter techniques that implement an irregular sampling of the a-posteriori probability space. Finally, experimental results are presented and discussed

In this paper we propose a spatial multiplexing technique for the downlink of a multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) system. For outdoor environments with a limited angular spread at the base station, the proposed technique is able to separate the users' streams through a joint spatial processing at both the transmitter and the receiver requiring only a limited feedback from each user. Adaptive transmission is adopted on each stream to set a fixed probability of error. Numerical simulations show that the proposed technique is able to provide significant throughput gains compared to fixed-beams based approaches proposed in the literature.

In this work, we propose an adaptive technique for interference mitigation based on minimum variance distortionless response (MVDR) beamforming for the uplink of a WiMAX-compliant system. This method is designed to cope with time-varying interference due to the asynchronous access of users in the neighboring cells. Channel parameters needed for beamforming are obtained by exploiting both the preambles in the transmitted frames and the pilot subcarriers embedded in each information-bearing OFDM symbol. The effectiveness of the proposed technique is shown through numerical simulations of a standard WiMAX uplink over standard multipath channels

In mobile communications the movement of the users makes the propagation channel to be time-varying. Algorithms that track channel variations have to trade between complexity and accuracy. Since the second-order statistic of time-varying channels is stationary, estimation of the channel can be reduced to track a set of r uncorrelated parameters. Based on this decomposition, in this paper we propose to simplify the optimum Kalman filter (KF) by tracking the r channel modes separately and by using the steady-state solution of the KF gain.

This paper deals with the problem of radio localization of moving terminals in wideband indoor applications with mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions. In dense multipath scenarios, the bias introduced by N-LOS in angle and/or time of arrival estimates is reduced by employing a hidden Markov model (HMM) based algorithm. The proposed algorithm jointly tracks both the mobile station position and the LOS/NLOS conditions exploiting continuity information. Numerical results show that the HMM-based algorithm experiences non meaningful degradation in mixed LOS/NLOS propagation with dense multipath.

In antenna array systems, downlink beamforming and uplink maximum likelihood structured channel estimation can be formulated under a common framework related to the algebraic structure of the two problems. The slow variations of the uplink and downlink spatial subspaces, due to moving terminals, can be tracked by using an adaptive structure based on a common processing block, namely a subspace tracker. Simulations for realistic propagation conditions show that the structure is able to efficiently cope with fast-varying fading channels, allowing relevant gains compared to conventional techniques.

In mobile communications, the movement of terminals renders the multipath channel time varying. Even though the faded amplitudes are fast varying, the delays can be considered as stationary on a large temporal scale. We propose a new subspace-based method that estimates the channel response from multiple slots by capitalizing on these different varying rates without explicitly computing the delays of the multipath. The temporal subspace is obtained from multiple single-slot training-based estimates of the (single-user or multiuser) channel response. Provided that the number of slots is large enough, the time basis can be calculated with some accuracy. As a consequence, the mean-square error of the channel response depends only on the number of fast-varying parameters that have to be estimated in a slot-by-slot fashion. Performance analysis and simulations confirm the expected benefits of the multislot approach in improving the efficiency of systems with short training sequences.

This paper investigates the estimation and tracking of time varying propagation channels in the uplink of a time slotted CDMA system. An antenna array is adopted at the base station. Both the estimation and tracking are performed by exploiting the low-rank nature of the channel matrix. The accuracy of the estimate is improved by using a multi-slot approach: the slowly varying component of the low-rank channel is estimated from the observation of successive midambles (inter-slot tracking), while the fast varying component is updated over the burst interval in decision-directed mode (intra-slot tracking).

In subsurface sensing, the estimation of the delays (wavefronts) of the backscattered wavefields is a very time-consuming, mostly manual task. We propose delay estimation by exploiting the continuity of the wavefronts modeled as a Markov chain. Each wavefront is a realization of Brownian motion with a correlation that depends on the distance between each source/receiver pair. Therefore, the delay profiles can be tracked with any known method by assuming that the ordered sequence of signals is described by a hidden Markov model (HMM). Linear array provides the most natural data-ordering, and in this case the tracking algorithms can preserve the target/tracker association. However, when measurements are multidimensional, the volume-slicing strategies, that are able to get a linear array of (virtually) ordered signals, select the measurements independently of the target. When different estimates along slices are merged mis-ties can occur easily. Since data-ordering is a main issue for irregularly positioned sources and receivers, we propose a region growing tracking technique that orders (for each specified target) the data while tracking. The ordering is based on the maximum a posteriori probability of detection. Experiments based on multidimensional measurements show that this region growing tracking algorithm based on HMM preserves the target/tracker association.

This paper investigates the problem of channel estimation in the uplink of CDMA systems with base station antenna array. The estimation is based on the transmission of training sequences with limited length. In order to improve multiuser receiver performance it is proposed to reduce the number of the unknowns in the channel estimation by constraining the space-time channel matrix of each user to be low rank. The rank-order is estimated according to the MDL criterion as this method provides the best trade-off between distortion (due to under-parametrization) and variance (due to the limited training length).

Linear multiuser detection (MUD) for frequency selective channels has always been considered a prohibitive computational task in CDMA systems. In time slotted CDMA, the block-type MUD involves the inversion of a large matrix that depends on the block size and on the number of users. Sub-optimal techniques are computationally efficient but show some performance degradation. The reduced complexity detectors can be either block-type or one-shot. Compared to one-shot approximation of MUD, the block-type detectors have less computational complexity and large latency. However, the tracking of channel variations within the block is not feasible with any block-type processing (e.g., for the adaptive receiver). These compelling aspects force us to use a one-shot MUD algorithm for space-time channels such as the sliding window decorrelator (SWD). Block-based MUD and SWD algorithms for TDD-UTRA systems are compared in terms of performance, computational complexity, parallelism and hardware implementation.

In a mobile communication system there is the need to define a channel length that could be useful to describe many different and varying propagation environments, over-parameterization is a simple solution. The channel matrix estimated when using an array of antennas may contain more parameters than those really needed to parsimoniously describe the space-time channel. The problem is even worse when a long channel has to be estimated from short training sequences. Classically the reduction of complexity is carried out by masking some samples of the channel estimate according to a threshold heuristically defined. We propose to adaptively classify (and mask) the estimated channel samples by minimizing the Bayes risk for space-time systems. The parameters of the probability densities are iteratively estimated from the estimated channel samples and contribute to define the optimum threshold. Simulation shows that in Rayleigh fading channels the adaptive threshold is close to the one that minimize the mean square error. The performance can improve by approx. 3-5 dB in signal to noise ratio when the method is applied to reduce the complexity of space-time channels in a CDMA system with realistic propagation environments.

This paper addresses the problem of estimating the multiuser channels in CDMA systems when using a linear array of antennas. The training sequences to be used for multiuser channel estimation have limited lengths. In order to control the errors of the (unstructured) estimate of space-time (S-T) channel we propose to reduce the complexity when parameterizing the channel. This is achieved by constraining each matrix that models the S-T channel of each user to have a low-rank. The rank of the matrix accounts for the degrees of space and/or time diversity in the S-T channel. Here we propose to estimate the reduced-rank channel simultaneously for all the users so as to control the multiaccess interference (i.e., all the users are considered signals for each other). The covariance matrix of the intercell interference is constrained accordingly. Simulation results for realistic propagation/interference environments confirm the expected benefits.

In several remote sensing applications, multitarget detection/tracking (D/T) of the backscattered wavefields is a very demanding task. Wavefield signals, sampled by an array of sensors, can be described by an hidden Markov model (HMM). As a consequence, the time of delay (TOD) profiles for each of the wavefield (or target) can be estimated by any of the known methods for state-sequence estimation such as the Viterbi (VA) and the backward/forward (BFA) algorithms. Some assumptions, that arise in the wavefield separation problem, allow one to include some additional constraints that preserve the target/tracker association. When an improved resolution is required, the choice of the multitarget Viterbi algorithm (MVA) is mandatory even if its complexity increases exponentially.