The design of accurate Digital Twins (DTs) of electromagnetic environments strictly depends on the fidelity of the underlying environmental modeling. Evaluating the differences among diverse levels of modeling accuracy is key to determine the relevance of the model features towards both efficient and accurate DT simulations. In this paper, we propose two metrics, the Hausdorff ray tracing (HRT) and chamfer ray tracing (CRT) distances, to consistently compare the temporal, angular and power features between two ray tracing simulations performed on 3D scenarios featured by environmental changes. To evaluate the introduced metrics, we considered a high-fidelity digital twin model of an area of Milan, Italy and we enriched it with two different types of environmental changes: (i) the inclusion of parked vehicles meshes, and (ii) the segmentation of the buildings facade faces to separate the windows mesh components from the rest of the building. We performed grid-based and vehicular ray tracing simulations at 28 GHz carrier frequency on the obtained scenarios integrating the NVIDIA Sionna RT ray tracing simulator with the SUMO vehicular traffic simulator. Both the HRT and CRT metrics highlighted the areas of the scenarios where the simulated radio propagation features differ owing to the introduced mesh integrations, while the vehicular ray tracing simulations allowed to uncover the distance patterns arising along realistic vehicular trajectories.

The Line-of-Sight (LoS) identification is crucial to ensure reliable high-frequency communication links, especially those vulnerable to blockages. Network Digital Twins and Artificial Intelligence are key technologies enabling blockage detection (LoS identification) for high-frequency wireless systems, e.g., 6 > GHz. In this work, we enhance Network Digital Twins by incorporating Age of Information (AoI) metrics, a quantification of status update freshness, enabling reliable real-time blockage detection (LoS identification) in dynamic wireless environments. By integrating raytracing techniques, we automate large-scale collection and labeling of channel data, specifically tailored to the evolving conditions of the environment. The introduced AoI is integrated with the loss function to prioritize more recent information to fine-tune deep learning models in case of performance degradation (model drift). The effectiveness of the proposed solution is demonstrated in realistic urban simulations, highlighting the trade-off between input resolution, computational cost, and model performance. A resolution reduction of 4 × 8 from an original channel sample size of ( 32 , 1024 ) along the angle and subcarrier dimension results in a computational speedup of 32 times. The proposed fine-tuning successfully mitigates performance degradation while requiring only 1 % of the available data samples, enabling automated and fast mitigation of model drifts.

This paper addresses the challenge of achieving simultaneous synchronization and localization of all the active terminals within a cellular network from only one Base Station (BS). We propose a novel approach leveraging Digital Network Twins (DNT), which integrates these two critical tasks within a unified framework.We begin by analyzing User Equipment (UE)-to-network time synchronization, both theoretically and through experimental validation using a 5th generation (5G) testbed, identifying it as the primary obstacle to accurate localization. Then, to address this challenge, we introduce a DNT-based framework that leverages high-fidelity ray-tracing simulations on a 3D digital replica of the environment. This enables precise UE-to-network alignment, dynamic environmental mapping, and accurate real-time localization starting from one Next Generation Node Base (gNB). The proposed method integrates Angle Delay Channel Power Matrix (ADCPM) characterization and Time of Flight (ToF) data with the DNT prior knowledge of the environment, eliminating the need for network cooperation or prior on-field channel measurements for precise localization. We first validate the proposed approach through an outdoor measurement campaign and then demonstrate its effectiveness via numerical simulations, compared to existing localization techniques in scenarios where only a single gNB is available. The method achieves on average a positioning accuracy of less than 6m in the static case and 8m in the dynamic scenario, using a ray-tracing granularity that is not excessively fine (4 × 4 m), even under worst-case synchronization and Non-Line of Sight (NLoS) conditions.

Neural Decoding and Stimulation (ND&S) systems offer a promising alternative to conventional treatments for peripheral nerve injuries by decoding neural signals and delivering targeted stimulation via implantable devices. A primary challenge in ND&S development is the accurate classification of electroneurography (ENG) signals under strict constraints on computational resources, processing time and power constraints. To address this, we propose a Parallel Spiking Neural Network (PSNN) architecture based on a novel spiking neuron model called Parallel Spiking Neurons (PSNs), optimized for ENG classification. The model combines event-driven processing with low computational complexity, making it well-suited for implantable applications. Compared to the state-of-the-art ESCAPE-Net, the PSNN achieves higher test accuracy (87.21%±4.9%) and macro F1-score (84.98%±9.39%), while reducing the number of the required model parameters by 99.97%. These results underscore the effectiveness of PSNNs in achieving high classification accuracy with minimal computational overhead, aligning with the stringent requirements of ND&S systems.

The increasing demand for reliable Vehicle-to-Everything (V2X) communications and autonomous mobility necessitates sophisticated simulation frameworks and intelligent optimization strategies. This paper presents a Network Digital Twin (NDT) that integrates high-fidelity ray-tracing with real vehicular traffic data to model wireless propagation in dynamic urban environments and derive theoretical localization bounds. By explicitly exploiting multipath reflections, both line-of-sight (LOS) and non-line-of-sight (NLOS), from static and mobile reflectors such as vehicles, the framework supports the design of an optimized precoding scheme for enhanced user equipment (UE) positioning. Numerical results indicate that the proposed NDT-guided method reduces the Position Error Bound (PEB) by approximately 35%, underscoring NDT benefits and the utility of NLOS exploitation for high-accuracy localization in dense urban scenarios.

The growing demand for high-speed, reliable wireless connectivity in 6 G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Numerical results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

Digital Twin (DT) has emerged as a promising solution for channel estimation. By leveraging high-resolution 3D models of the scenario and ray-tracing simulations, DT could provide valuable site-specific prior knowledge on the channel’s space-time (ST) invariant features of the multipath environment, such as angles of arrival, angles of departure, and propagation delays. However, the real-time characterization of these features imposes computational constraints on ray-tracing simulations, hence limiting the prior knowledge of the multipath environment and corresponding ST features, and degrading estimation accuracy. In this paper, we propose and investigate, for the first time, three distinct DT-empowered low-rank methods for channel estimation, under different degrees of prior knowledge corresponding to limited number of paths provided by DT. Specifically, these methods perform modal projection onto a joint space-time, a spatial, and a temporal subspace. We compare our proposed methods with state-of-the-art techniques, and evaluate their performance in a synthetic scenario. Numerical results show that robustness, when prior knowledge is limited to few paths, is achieved when exploiting only temporal features, while estimation accuracy is attained when joint space-time features are considered.

Sensing in non-line-of-sight (NLOS) is a well-known issue that limits the range of radar-like sensors. Existing approaches rely on either metallic mirrors, that only work under specular reflection, or dynamically-reconfigurable metasurfaces that steer the signal to cover a desired area in NLOS, with the drawback of cost and control signaling. This paper proposes a novel vehicular sensing method, here referred as Stroboscopic Sensing, where a moving source images a desired region of interest (ROI) in NLOS leveraging on source beam sweeping over a sufficiently large portion of a reflection plane, passively pre-configured as a periodic angular deflecting function to illuminate the ROI. Consequently, the source is able to cover the ROI and enhance the spatial resolution of the image, thanks to multiple diverse observation angles of ROI. In the context of vehicular sensing systems, our method allows a moving vehicle to perceive its surroundings beyond obstacles, improving situational awareness. Remarkably, the proposed method achieves near-field imaging with a sequence of far-field acquisitions, thus limiting the implementation complexity. We detail the system design criteria and trade-offs, demonstrating the remarkable benefits of such sensing method, where a possibly moving source can observe a ROI through multiple points of view as if it were static.

Wireless communication systems can significantly benefit from the availability of spatially consistent representations of the wireless channel to efficiently perform a wide range of communication tasks. Towards this purpose, channel charting has been introduced as an effective unsupervised learning technique to achieve both locally and globally consistent radio maps. In this letter, we propose Chartwin, a case study on the integration of localization-oriented channel charting with dynamic Digital Network Twins (DNTs). Numerical results showcase the significant performance of semi-supervised channel charting in constructing a spatially consistent chart of the considered extended urban environment. The considered method results in ≈ 4.5 m localization error for the static DNT and ≈ 6 m in the dynamic DNT, fostering DNT-aided channel charting and localization.

The increasing complexity of 6G networks demands advanced tools for network management and simulation. This demo pioneers the integration of ns-3 and NVIDIA Sienna®RT, laying the foundation for the first multi-Radio Access Technologies (RAT) full-stack, open-source Digital Network Twin (DNT). The introduction of a deterministic ray tracer for an accurate channel modeling into ns-3 enables realistic and site-specific simulation which cannot be achieved via stochastic channel models. Tested in a challenging vehicular urban scenario, the proposed framework demonstrates significant improvements in predicting dynamic wireless channels and its impact at higher network layers.

Electromagnetic skins (EMSs) have been considered as boosters for communication performance, from coverage to capacity. The vast majority of the scientific literature considers reconfigurable EMSs, a.k.a. reconfigurable intelligent surfaces, whose phase configuration can be dynamically changed in time as needed. Differently, this work considers low-cost static passive curved EMSs (CEMSs) to be lodged on car doors to alleviate the blockage issue in vehicular networks. CEMSs are pre-configured during the manufacturing process, following the shape of the car door, and they are designed to make car doors behave as anomalous mirrors, and vehicles to operate as opportunistic passive reflectors. This paper delves into the optimization of the CEMS phase profile knowing the coarse a-priori distribution of incident and reflection angles onto the CEMS, as a function of the vehicular traffic pattern. A penalty-based method to optimize both the average spectral efficiency (SE) and average coverage probability is benchmarked against a novel lower complexity and physically intuitivemodular architecture, employing a codebook-based discrete optimization technique. Our numerical results show that whenever the direct path is blocked, by smart preconfiguration of the CEMS, average SE and coverage can be significantly enhanced.

Free Space Optics (FSO) has met a renovated interest for 6G applications. The large availability of unlicensed bandwidth, and the lack of mutual interference between the apparata, make FSO a promising technology for next-generation transport networks. However, propagation impairments severely affect FSO, like clear air turbulence, which induces irradiance and angle-of-arrival (AOA) fluctuations across the wavefront. Receiving diversity can be applied to mitigate turbulence fading. Recently, all-optical processing has emerged as the paradigm for coherent FSO systems, where the received field, coupled inside waveguides, e.g., single-mode fiber, is processed optically before detection. Coupling over-the-air propagation to fiber introduces limitations on the angular acceptance of the receiver, hence, the interaction of the AOA with the numerical aperture of the optical antenna might induce an increase of fading, on top of the irradiance. Appropriate modeling of the latter is necessary for predicting the performance of FSO systems. In this paper, we propose a channel model for fiber-coupled FSO in turbulence, with the aim of fully characterizing the fading, including angle and irradiance fluctuations, and we derive approximate expressions for the Bit-Error Rate (BER) with OOK modulation. When compared with the classical results obtained ignoring the AOA dispersion, we evaluate several remarkable differences.

Smart Radio Environment (SRE) is a central paradigm in 6 G and beyond, where integrating Smart Radio Environment (SRE) components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and channel models to analyze diverse SRE components, including Reconfigurable Intelligent Surface (RIS), Network-Controlled Repeater (NCR), and advanced technologies like Simultaneous Transmitting and Reflecting RIS (STAR-RIS) and Trisectoral NCR (3SNCR). We propose two optimization strategies—Full Coverage Minimum Cost (FCMC) and Maximum Budget-Constrained Coverage (MBCC)—that address key cost and coverage objectives by considering both physical characteristics and scalable costs of each component, influenced by factors such as NCR amplification gain and RIS dimensions. Extensive numerical results demonstrate the significant impact of these models in enhancing network planning efficiency for high-density urban environments.

The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across time, frequency, space, and energy. Despite its popularity in communication, the orthogonal frequency division multiplexing (OFDM) waveform, while advantageous for communication, has limitations in sensing performance within an ISAC network. This paper delves into OFDM waveform design methods through optimal resource allocation over time, frequency, and energy, maximizing sensing performance while preserving communication quality. During quasi-normal operation, the Base Station (BS) does not utilize all available time-frequency resources, resulting in high sidelobes in the OFDM waveform’s ambiguity function, as well as decreased sensing accuracy. To address these latter issues, the paper proposes a novel interpolation technique using matrix completion through the Schatten p quasi-normal approximation, which requires fewer samples than the traditional nuclear norm for effective matrix completion and interpolation. This approach effectively suppresses the sidelobes, enhancing the sensing performance. Numerical simulations confirm that the proposed method outperforms state-of-the-art frameworks, such as standard complaint resource scheduling and interpolation, particularly in scenarios with limited resource occupancy.

This paper tackles the challenge of wideband MIMO channel estimation within indoor millimeter-wave scenarios. Our proposed approach exploits the integrated sensing and communication paradigm, where sensing information aids in channel estimation. The key innovation consists of employing both spatial and temporal sensing modes to significantly reduce the number of required training pilots. Moreover, our algorithm addresses and corrects potential mismatches between sensing and communication modes, which can arise from differing sensing and communication propagation paths. Extensive simulations demonstrate that the proposed method requires 4× less pilots compared to the current state-of-the-art, marking a substantial advancement in channel estimation efficiency.

Reconfigurable intelligent surfaces (RISs) have been considered recently for target localization. While existing literature typically uses fixed RISs in the environment, mounting RISs on targets is a novel approach that can improve target visibility and positioning. This study derives the Cramér-Rao bound (CRB) for pose estimation (i.e., RIS position and orientation) under a generic wideband and near-field model. The theoretical findings show that a pose-dependent filtering phenomenon occurs, impacting the CRB, which is neglected under narrowband approximation. The extent of this effect varies based on factors such as RIS dimensions and signal bandwidth.

The rapid expansion of connected and autonomous vehicles (CAVs) and the shift towards millimiter-wave (mmWave) frequencies offer unprecedented opportunities to enhance road safety and traffic efficiency. Sidelink communication, enabling direct Vehicle-to-Vehicle (V2V) communications, play a pivotal role in this transformation. As communication technologies transit to higher frequencies, the associated increase in bandwidth comes at the cost of a severe path and penetration loss. In response to these challenges, we investigate a network configuration that deploys beamforming-capable Unmanned Aerial Vehicles (UAVs) as relay nodes. In this work, we present a comprehensive analytical framework with a groundbreaking performance metric, i.e. average access probability, that quantifies user satisfaction, considering factors across different protocol stack layers. Additionally, we introduce two Radio Resources Assignment (RRA) methods tailored for UAVs. These methods consider parameters such as resource availability, vehicle distribution, and latency requirements. Through our analytical approach, we optimize the average access probability by controlling UAV altitude based on traffic density. Our numerical findings validate the proposed model and strategy, which ensures that Quality of Service (QoS) standards are met in the domain of Vehicle-to-Anything (V2X) sidelink communications.

As the deployment of fifth generation (5G) mobile wireless networks continues to gain momentum, researchers are already focusing on the challenges and opportunities of the next sixth generation (6G). To meet the ever-increasing demand for higher data rates and support the development of new services, 6G is expected to exploit millimeter wave (mmWave) frequencies. However, the complex propagation characteristics at mmWave require beamforming technology, which introduces significant complexity in the communication system. Herein, we propose a real-time testbed platform to evaluate beamforming and other communication solutions designed for multiple-input multiple-output (MIMO) mmWave-based 6G networks. This platform serves as an enabler for 6G technologies evaluation under realistic propagation conditions, accelerating the development and deployment of robust and efficient 6G networks. To demonstrate the capabilities of our platform, we have implemented a smart relay with real-time beam control and tracking. The platform is able to perform an exhaustive search of 64 reception beams in less than 256 μs. Additionally, the platform can maintain the optimal beam even in mobility scenarios using a gradient-based tracking system that achieves a low overhead of less than 5%, with an update rate of 100 Hz.

Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment. At high-frequency bands, DTs can help to overcome the challenges emerging in high mobility conditions featuring vehicular environments. In this paper, we propose a novel data-driven workflow for the creation of the DT of a Vehicle-to-Everything (V2X) communication scenario and a multi-modal simulation framework for the generation of realistic sensor data and accurate mmWave/sub-THz wireless channels. The proposed method leverages an automotive simulation and testing framework and an accurate ray-tracing channel simulator. Simulations over an urban scenario show the achievable realistic sensor and channel modelling both at the infrastructure and at ego-vehicles. We showcase the proposed framework on the DT-aided blockage handover task for V2X link restoration, leveraging the framework’s dynamic channel generation capabilities for realistic vehicular blockage simulation.

Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person’s life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous challenges. In this article, we address one of them, which consists of the classification of motor/sensory stimuli. The task is performed by exploring four different types of artificial neural networks (ANNs) to extract various sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification through a comparison of their performance in terms of accuracy, F1-score, and prediction time. The design of the ANNs takes advantage of the modelling of the ENG signal as a multiple-input multiple-output (MIMO) system to describe the measures taken by state-of-the-art implanted nerve interfaces. These are based on the use of multi-contact cuff electrodes to achieve nanoscale spatial discrimination of the nerve activity. The MIMO ENG signal model is another contribution of this paper. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over 90% for signal windows of 100 and 200 ms with a low enough processing time to be effective for pathology recovery.

Space-time modulated metasurfaces (STMMs) are a newly investigated technology for the next 6G generation wireless communication networks. An STMM augments the spatial phase function with a time-varying one across the meta-atoms, allowing for the conveyance of information that possibly modulates the impinging signal. Hence, STMM represents an evolution of reconfigurable intelligent surfaces (RIS), which only design the spatial phase pattern. STMMs convey signals without a relevant increase in the energy budget, which is convenient for applications where energy is a strong constraint. This paper proposes a mathematical model for STMM-based wireless communication, that creates the basics for two potential STMM architectures. One has excellent design flexibility, whereas the other is more cost-effective. The model describes STMM’s distinguishing features, such as space-time coupling, and their impact on system performance. The proposed STMM model addresses the design criteria of a full-duplex system architecture, in which the temporal signal originating at the STMM generates a modulation overlapped with the incident one. The presented numerical results demonstrate the efficacy of the proposed model and its potential to revolutionize wireless communication.

Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users’ protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as coherent processing of several sensors’ data requires very tight accuracy on clock synchronization and sensor’s positioning—referred to as phase synchronization—(such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cramér-Rao bound.

Integrated sensing and communication (ISAC) systems are recognized as one of the key ingredients of the sixth generation (6G) network. A challenging topic in ISAC is the design of a single waveform combining both communication and sensing functionalities on the same time-frequency-space resources, allowing tuning the performance of both with partial or full hardware sharing. This paper proposes a dual-domain waveform design approach that superposes onto the frequency-time (FT) domain both the legacy orthogonal frequency division multiplexing (OFDM) signal and a sensing one, purposely designed in the delay-Doppler domain. With a proper power downscaling of the sensing signal w.r.t. OFDM, it is possible to exceed regulatory bandwidth limitations proper of legacy multicarrier systems to increase the sensing performance while leaving communication substantially unaffected. Numerical and experimental results prove the effectiveness of the dual-domain waveform, notwithstanding a power abatement of at least 30 dB of the signal used for sensing compared to the one used for communication. The dual-domain ISAC waveform outperforms both OFDM and orthogonal time-frequency-space (OTFS) in terms of Cramér-Rao bound on delay estimation (up to 20 dB), thanks to its superior resolution, with a negligible penalty on the achievable rate.

Electromagnetic skins (EMSs) have been recently considered as a booster for wireless sensing, but their usage on mobile targets is relatively novel and could be of interest when the target reflectivity can/must be increased to improve its detection or the estimation of parameters. In particular, when illuminated by a wide-bandwidth signal (e.g., from a radar operating at millimeter waves), vehicles behave like extended targets, since multiple parts of the vehicle’s body effectively contribute to the back-scattering. Moreover, in some cases perspective deformations challenge the correct localization of the vehicle. To address these issues, we propose lodging EMSs on vehicles’ roof to act as high-reflectivity planar retro-reflectors toward the sensing terminal. The advantage is twofold: 1) by introducing a compact high-reflectivity structure on the target, we make vehicles behave like point targets, avoiding perspective deformations and related ranging biases and 2) we increase the reflectivity of the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wave-level considering both reconfigurable intelligent surfaces (RIS) and cost-effective static passive electromagnetic skins (SP-EMSs). Localization performance of the EMS-aided sensing system is also assessed by Cramér-Rao bound analysis in both narrowband and spatial wideband operating conditions.

Space-time modulated metasurfaces (STMMs) are a recently proposed generalization of reconfigurable intelligent surfaces, which include a proper time-varying phase at the metasurface elements, enabling higher flexibility and control of the reflected signals. The spatial component can be designed to control the direction of reflection, while the temporal one can be adjusted to change the frequency of the reflected signal or to convey information. However, the coupling between the spatial and temporal phases at the STMM can adversely affect its performance. Therefore, this paper analyzes the system parameters that affect the space-time coupling. Furthermore, two methods for space-time decoupling are investigated. Numerical results highlight the effectiveness of the proposed decoupling methods and reveal that the space-time phase coupling increases with the bandwidth of the temporal phase, the size of the STMM, and with grazing angles of incidence onto the STMM.

Wireless communication systems are the backbone of today's digital society. To achieve unprecedented throughput and efficiency, 5G and 6G networks will leverage parallel communication over multiple spatial channels under the massive MIMO (multiple-input multiple-output) framework. One of the main limitations of MIMO is its heavy load of matrix operations, a task for which von Neumann-based computers are deeply unoptimized. This bottleneck can be solved by in-memory computing (IMC), where computation is performed directly within the memory, thus eliminating the constant data shuttling between the memory and the processing unit. Here, we provide an experimental validation of a closed-loop IMC (CL-IMC) system on a 90 nm CMOS integrated circuit. Zero-forcing and regularized-zero-forcing decoding are executed with$14\times 7$MIMO link. The hardware demonstration shows 99.91% accuracy, which is close to a floating-point precision decoder. These results support CL-IMC as a promising candidate for data processing in massive MIMO for next-generation cellular networks.

Integrated sensing and communication (ISAC) is one of the key technologies of future 6G communication networks. Waveform design for 6G ISAC systems shall guarantee a flexible communication and sensing performance trade-off with full time-frequency-space resource sharing and minimal added hardware/complexity. Legacy ISAC schemes based on orthogonal frequency division multiplexing (OFDM) or orthogonal time-frequency-space (OTFS) are currently subject to regulatory bandwidth constraints, limiting the delay/range resolution and requiring advanced processing schemes. This paper proposes to exploit a low-power, wide-bandwidth out-of-band (OOB) sensing signal superposed to the legacy OFDM one to enhance the delay/range resolution compared to standalone OFDM and OTFS ISAC systems. The proper power control of the sensing signal allows for complying with adjacent channel leakage ratio requirements. The analytical findings demonstrate the advantages of the proposed ISAC scheme over existing solutions.

High precision navigation in urban environment is a fundamental requirement for modern transportation systems. Global Navigation Satellite System (GNSS) and Inertial Measurement Units (IMUs) are the typical sensors on commercial vehicles. In urban environments, however, shadowing and multi-path effects on satellite signals can severely degrade the localization performance of GNSS receivers. Navigation with IMUs, on the other side, requires a precise GNSS signal to correct the typical biases of low-cost IMUs. In order to mitigate these effects, we propose a processing scheme that leverages on recurrent patterns of repeated passages of vehicles in a given region to make the positioning system much more robust. An edge cloud is used to store past trajectory data to form the a-priori dynamical state within any pre-defined area. The estimated a-priori is passed to the incoming vehicles and is combined with their internal state to reduce the GNSS dependency. An experimental campaign validates the proposed scheme, and results are favourably compared with standard Kalman filter navigation.

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 future 6G millimeter-wave/sub-THz vehicle-to-everything communication systems, vehicles are expected to be equipped with massive antenna arrays to compensate for the severe path loss. However, vehicle-to-vehicle (V2V) direct links are prone to blockage by surrounding vehicles. Emerging metasurface technologies enable the control of the electromagnetic wave reflection towards the desired direction to boost communication performance. Reconfigurable intelligent surfaces (RIS), and the pre-configured counterpart intelligent reflecting surfaces (IRS), are promising low-cost relaying systems for 6G. This paper proposes the usage of conformal metasurfaces (C-RIS or C-IRS) deployed on vehicles’ body to mitigate the blockage impact by creating artificial reflections, compensating for the non-flat shape of the vehicle’s body with proper phase patterns. We analytically derive the phase pattern for a cylindrical C-RIS/C-IRS, accounting for the shape of the car body, which generalizes the conventional planar ones. We propose a novel design for optimally pre-configured C-IRS proving its benefits in a V2V highway scenario. The traffic simulation results show a reduction of blockage probability by 20% for C-IRS and 70% for C-RIS as a benchmark, as well as a remarkable improvement in average signal-to-noise ratio by 10–20 dB for C-IRS and 30–40 dB for C-RIS.

Intelligent reflecting surfaces (IRS) will represent a key technology in the upcoming sixth-generation (6G) communication networks to extend the network coverage and overcome link blockage. Research on IRS is expected to take a giant leap in the coming years to address the current technological limitations, mainly regarding the IRS re-configuration in highly dynamic scenarios, such as vehicle-to-vehicle (V2V) ones. This paper proposes a fully passive and low-cost solution based on pre-configured IRS to be lodged on the vehicle's body, which does not require any signaling for re-configuration. However, conventional IRS are planar array and cannot fit with most vehicles' silhouettes. Hence, the proposed design targets conformal surfaces (C-IRS) with an arbitrary shape. In particular, this paper reveals the first experimental findings on the realization of C-IRS operating at 26 GHz and measurements in anechoic chamber, which validate the analytical derivations. To demonstrate the benefits of the proposed solution, numerical simulations in a V2V scenario show that, when the percentage of vehicles equipping a C-IRS increases, the communication becomes more robust to vehicle blockage and the average end-to-end SNR is enhanced up to 25 dB.

Vehicle-to-Everything (V2X) communications are revolutionizing the connectivity of transportation systems supporting safe and efficient road mobility. To meet the growing bandwidth eagerness of V2X services, millimeter-wave (e.g., 5G new radio over spectrum 26.50 - 48.20 GHz) and sub-THz (e.g., 120 GHz) frequencies are being investigated for the large available spectrum. Communication at these frequencies requires beam-type connectivity as a solution for the severe path loss attenuation. However, beams can be blocked, with negative consequences for communication reliability. Blockage prediction is necessary and challenging when the blocker is dynamic in high mobility scenarios such as Vehicle-to-Vehicle (V2V). This paper presents an analytical model to derive the unconditional probability of blockage in a highway multi-lane scenario. The proposed model accounts for the traffic density, the 3D dimensions of the vehicles, and the position of the antennas. Moreover, by setting the communication parameters and a target quality of service, it is possible to predict the signal-to-noise ratio distribution and the service probability, which can be used for resource scheduling. Exhaustive numerical results confirm the validity of the proposed model.

Vehicle-to-vehicle (V2V) resource allocation (RA) schemes have been introduced in the cellular V2V (C-V2V) standard for sidelink (SL) communications to allow for an efficient sharing of the time-frequency resources in sub-6 GHz bands. However, the recent progress in connected and automated vehicles and the introduction of new bandwidth-eager mobility services are driving towards the use of millimeter-wave (mmW) frequencies (24.25-52.6 GHz). A characteristic of propagation at mmW frequencies is the severe path loss attenuation that can be compensated through beamforming. Therefore, its introduction adds a spatial dimension that must be considered in the design of RA schemes. The current fifth-generation (5G) RA standard for SL communication, which is inherited from the previous C-V2V standard, is not designed for directional communication and does not take into account the interference impact. Hence, this paper proposes a novel RA scheme to manage spatial-interference by adding the spatial dimension, i.e. the spatial beam directivity, and cooperation between vehicles for resource selection. The simulation results confirm that the three-dimensional cooperative RA (3D-CRA) has an average improvement of 10% in packet delivery ratio, 50% in collision probability, and 60% in channel busy ratio compared to the standard RA.

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (UML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less).

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.

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.

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.

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.

MIMO Radio-over-Copper (MIMO-RoC) is a transport system for indoor coverage that leverages the pre-existing building's copper cabling infrastructure. In MIMO-RoC, the overall channel from the Base Band Units (BBU) to the end-user is the cascade of a MIMO-radio over a MIMO-cable channel and the analog fronthaul together with a wireless channel enables the transport to the mobile users. The entire system poses two main challenges: i) design low-complexity and flexible allocation strategies between wired and wireless resources; ii) design interference cancellation techniques tailored to the mutually coupled wired-wireless interference. While the former has been extensively investigated, the optimal design of precoding algorithms for the MIMO-RoC downlink is still an open issue and is covered here. In particular, in this paper, the Linear Successive Allocation (LISA) algorithm has been revised and adapted to the MIMO amplify-and-forward (AF) structure of MIMO-RoC. Numerical results validate the proposed method considering a realistic radio environment with 100m copper cable remotization.

Multiuser centralized radio access networks (C-RANs) need to adapt to time-varying radio environments, and adaptation is advantageously split between remote antenna units (RAUs) and centralized baseband units (BBUs) to minimize the required fronthaul throughput. To this end, hybrid beamforming to spatially multiplex signals to/from a pool of users is split between RAU and BBU, and optical analog signal processing can be performed at the RAU. The goal here is to reduce the number of fronthaul channels and the corresponding number of analog/digital converters at the BBU in wideband (>100 MHz) millimeter wave (mmWave) radio communication. An algorithm for hybrid beamforming design is developed, and shown to approach the performance of ideal digital beamforming with a number of fronthaul channels comparable to the number of simultaneously served users rather than with the number of array elements. To cope with the radio-access of multiple users, optical analog processing is made adaptive by tuning delay lines built with optical ring resonators (ORRs). The tunability of analog beamforming by means of thermo-optic phase shifters is designed for time-varying configuration compliant to the 5G new radio specifications and the performance degradation of beamforming transients due to the tunability of optical components is assessed.

In diffusion-based communication, for molecular systems, the achievable data rate depends on the stochastic nature of diffusion, which exhibits a severe inter-symbol-interference (ISI). Multiple-input multiple-output (MIMO) multiplexing improves the data rate at the expense of an inter-link interference (ILI). This paper investigates training-based channel estimation schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization methods. Maximum likelihood and least-squares estimators of mean channel are derived, and the training sequence is designed to minimize the mean square error (MSE). The numerical validations in terms of MSE are compared with Cramér-Rao bound derived herein. Equalization is based on decision feedback equalizer (DFE) structure as this is effective in mitigating diffusive ISI/ILI. Zero-forcing, minimum MSE, and least-squares criteria have been paired to DFE, and their performances are evaluated in terms of bit error probability. D-MIMO time interleaving is exploited as an additional countermeasure to mitigate the ILI with remarkable performance improvements. The configuration of nano-transceivers is not static but affected by a Brownian motion. A block-type communication is proposed for D-MIMO channel estimation and equalization, and the corresponding time-varying D-MIMO MC system is numerically evaluated.

Radio access network (RAN) centralization is at the basis of current mobile networks, in which BaseBand Units (BBUs) and radio antenna units (RAUs) exchange over the FrontHaul (FH) digitized radio-frequency signals through protocols such as the common public radio interface. However, such architecture, as it stands, does not scale to the demands of multiple-antennas 5G systems, thus leading to drastic RAN paradigm changes. Differently from digital RAN architectures, we propose to overcome bandwidth/latency issues due to digitization by employing an all-analog FH for multiple-antenna RAUs based on the analog radio-over-copper (A-RoC) paradigm. The A-RoC is an alternative/complementary solution to FH for the last 200 m, such as for indoor, to reuse existing local area network (LAN) cables with remarkable economic benefits. Although LAN cables contain 4 twisted-pairs with up to 500 MHz bandwidth/ea., their usage is limited by cable attenuation and crosstalk among pairs. This paper demonstrates that a judicious mapping of each radio-frequency signal of each antenna onto a combination of cable pair-frequency allocations, referred to as space-frequency to space-frequency multiplexing, optimized together with the design of the digital precoding at the BBU, substantially mitigates the cable impairments. The LAN cables can be exploited for last 100-200 m analog transport FH to meet the requirements of 5G indoor networks.

High accuracy pulse width modulation (PWM) is crucial for saturating devices such as optical communication systems, control and energy efficient amplification. In this paper we propose a closed-loop PWM architecture, called Sigma-Delta (ΣΔ) PWM, showing jitter robustness and compensation for some PWM inaccuracies arising from clock imprecision. The ΣΔ-PWM architecture trades oversampling and some increases in architectural complexity to gain in signal quality, leveraging on the noise shaping capability of the ΣΔ loop. Analytical results as well as numerical simulations assess the impact of timing jitter as key parameter in PW modulators.

Massive co-located devices require new paradigms to allow proper network connectivity. Internet of things (IoT) is the paradigm that offers a solution for the inter-connectivity of devices, but in dense IoT networks time synchronization is a critical aspect. Further, the scalability is another crucial aspect. This paper focuses on synchronization for uncoordinated dense networks without any external timing reference. Two synchronization methods are proposed and compared: i) conventional synchronization that copes with the high density of nodes by frame collision-avoidance methods (e.g., CSMA/CA) to avoid the superimposition (or collision) of synchronization signals; and ii) distributed synchronization that exploits the frames' collision to drive the network to a global synchronization. The distributed synchronization algorithm allows the network to reach a timing synchronization status based on a common beacon with the same signature broadcasted by every device. The superimposition of beacons from all the other devices enables the network synchronization, rather than preventing it. Numerical analysis evaluates the synchronization performance based on the convergence time and synchronization dispersion, both on collision and non-collision scenario, by investigating the scalability of the network. Results prove that in dense network the ensemble of signatures provides remarkable improvements of synchronization performance compared to conventional master-slave reference.

In this paper we consider a centralized radio access network (C-RAN) architecture with a fully analog fronthaul link between remote radio heads (RRHs) and baseband units (BBUs) based on the radio over fiber (RoF) paradigm. Mode division multiplexing (MDM) and frequency division multiplexing (FDM) are employed to provide an additional multiplexing signal dimension to meet the huge bandwidth requirements of next generation (5G) wireless mobile systems. The main contribution of the paper is to prove that a smart resource assignment between the radio antennas and the mode/frequency dimensions allows the communication over the RRH-BBU link at rates that are comparable to those achieved by an ideal fronthauling where BBU and RRH are assumed to be co-located, even without any complex and costly optical equalization technique. Validation is on the radio-link capabilities employing multiple antennas to meet the demand for massive MIMO technology.

Centralized Radio Access Network (C-RAN) architecture is considered the only viable solution to handle the complex interference scenarios generated by massive antenna and small cell deployment foreseen for next generation (5G) mobile networks. The fronthaul links used to exchange the signal between Base Band Units (BBUs) and Remote Radio Heads (RRHs) are conventionally based on optical fiber, due to its huge bandwidth. In this paper we propose the radio over copper (RoC) as a complementary technology for the fronthaul architectures leveraging on the pre-existing LAN cables that are largely deployed in buildings and enterprises. To fully exploit the capabilities of LAN cable over the last 50–200m, a joint space and frequency division multiplexing is proposed. In particular, the main contribution of the paper is to prove that a smart mapping between the radio antennas/spectrum and the copper cable space/frequency dimensions allows an efficient communication over the RRH-BBU link with performances that are comparable to those achieved by an ideal fronthauling where BBU and RRH are assumed to be co-located. Validation is on the radio-link capabilities of RoC over Cat-6 cable to meet the demand for massive MIMO technology.

Centralized Radio Access Network (C-RAN) architecture is the only viable solution to handle the complex interference scenario generated by massive antennas and small cells deployment as required by next generation (5G) mobile networks. In conventional C- RAN, the fronthaul links used to exchange the signal between Base Band Units (BBUs) and Remote Antenna Units (RAUs) are based on digital baseband (BB) signals over optical fibers due to the huge bandwidth required. In this paper we evaluate the transport capability of copper-based all-analog fronthaul architecture called Radio over Copper (RoC) that leverages on the pre-existing LAN cables that are already deployed in buildings and enterprises. In particular, the main contribution of the paper is to evaluate the number of independent BB signals for multiple antennas system that can be transported over multi-pair Cat-5#x002F;6#x002F;7 cables under a predefined fronthauling transparency condition in terms of maximum BB signal degradation. The MIMO-RoC proves to be a complementary solution to optical fiber for the last 200m toward the RAUs, mostly to reuse the existing LAN cables and to power-supply the RAUs over the same cable.

Linear precoding for Far-End CrossTalk (FEXT) cancellation is supported by the first release of the next generation DSL standard G.fast utilizing bandwidth up to 106 MHz. The cancellation precoding for the second release (bandwidth up to 212 MHz with much stronger FEXT) has not been standardized yet. Non-linear Tomlinson-Harashima Precoding (THP) is a promising candidate as it outperforms the linear precoding. Nevertheless, sequential processing of THP introduces unfavorable additional delay proportional to the number of lines. In this paper, we focus on non-linear precoding based on Lattice Reduction aided Zero-Forcing (ZF-LR) which can be implemented in parallel without any sequential processing delay. ZF-LR precoding has been proposed in wireless scenario using scalar scaling to adjust average transmit power. We modify this scheme to comply with per-line per-carrier power constraint and enable power control of each line individually (e.g., to adapt power for different line lengths). Optimized Power Allocation (OPA) for the modified scheme maximizing weighted sum-rate leads to the signomial optimization problem. Similarly to THP, ZF-LR precoding with OPA outperforms the linear precoding as well.

Abstract High‐speed train (HST) is revitalizing the train as a preferred mid‐range transportation system. The provision of broadband Internet connectivity onboard trains is one of the key challenges in the competition among train operators. Unprecedented spectral efficiency and data rates (up to 100Mbps in high mobility) of the Universal Mobile Telecommunications System long‐term evolution (LTE) are expected to offer the solution for high‐speed Internet access onboard in HSTs. Massive wireless access and frequent handovers (HOs) of a large number of users might potentially cause service interruptions, and this, in turn, degrades intolerably the quality of experience (QoE) of users' onboard Internet access. In this paper, we propose a solution based on distributed antenna system that combines directional and omni‐directional antennas as train‐to‐ground radio‐access terminals (T‐RATs) and LTE femtocells in each carriage. Directional antennas are deployed at both ends of the HST to provide multi‐cell access by diversifying the HOs over multiple LTE cells. This mechanism virtually elongates the train size by connecting the front and rear carriages' T‐RATs to the faraway eNodeBs and augmenting the number of cells the HST can be simultaneously connected to. An ad hoc distributed load‐balancing mechanism that consists in offloading backlogged packets to the on‐service T‐RATs is mandatorily paired with multi‐cell access scheme to tie up with the request of seamless onboard Internet service at high QoE level. Copyright © 2015 John Wiley & Sons, Ltd.

G.fast is an upcoming next generation DSL standard envisioned to use bandwidth up to 212 MHz. Far-end crosstalk (FEXT) at these frequencies greatly overcomes direct links. Its cancellation based on non-linear Tomlinson-Harashima Precoding (THP) proved to show significant advantage over standard linear precoding. This paper proposes a novel THP structure in which ordering of successive interference pre-cancellation can be optimized for downstream with non-cooperating receivers. The optimized scheme is compared to existing THP structure denoted as equal-rate THP which is widely adopted in wireless downlink. Structure and performance of both methods differ significantly favoring the proposed scheme. The ordering that maximizes the minimum rate (max-min fairness) for each tone of the discrete multi-tone modulation is the familiar V-BLAST ordering. However, V- BLAST does not lead to the global maximum when applied independently on each tone. The proposed novel Dynamic Ordering (DO) strategy takes into account asymmetric channel statistics to yield the highest minimum aggregated rate.

Synchronization of carrier frequency and frame alignment guarantees the proper communication and coding. Half duplex constraint represents a limit in synchronization as each device can only transmit its synchronization frames, or receive (and update) the synchronization from the other nodes. In this paper we investigate duplex scheduling as the trade-off between the time allocated to transmit or receive in dense cooperative networks. The proposed method is based on the same synchronization frame transmitted from multiple nodes used as a compound reference signal in synchronization process. The frame structure is designed from the family of CAZAC sequence properly arranged to achieve jointly timing and frequency synchronization. A modified distributed-phase locked loop (D-PLL) algorithm is adapted for timing and frequency correction. The paper shows that to maximize the convergence of timing and frequency synchronization the duplex scheduling can be random and independent on each node, with probability of transmission p ≪ 0.5.

For a connected network, consensus based algorithms guarantee that local estimates are iteratively shared and refined among neighbors to reach the same weighted average on all nodes. Parameter estimation for linear models are common problems where average consensus are routinely adopted to mimic a centralized approach without the need of any fusion center. Convergence speed, accuracy and the amount of signalling involved in consensus iterations are all relevant for practical usage (e.g., spectrum sensing in cognitive radios). In this paper we propose to exchange at initialization stage of consensus the covariance of each local estimate in form of long-term information on regressors, so that consensus steps are weighted accordingly. In spite of the simplicity, the preliminary exchange of the reliability among neighbors improves MSE performance close to the Cramér Rao bound for centralized approach, with a reduced convergence time. In time-varying settings, the balance between estimate refinements from consensus steps and reliability updates are also discussed.

High speed train (HST) is becoming one of the preferred mid-range transportation and the on-board Internet service is a must for train operators. LTE networks paved the road toward high quality and cost effective on-board Internet in HSTs. However, frequent handovers (HO) for several onboard users overload sequentially cells along the train-track and increases the service interruptions, that in turn degrade untolerably the quality of service (QoS). HO service interruptions could be largely mitigated if HST could be virtually longer to access multiple cells from multiple devices coordinated in bundle. This is the paradigm of multi-cell access investigated here. More specifically, we propose a novel (and practically viable) onboard architecture for train-to-ground LTE backhauling. Multiple directional antennas with fixed-beams are deployed along the train to provide multi-cell access to distribute on-board traffics over at least three cells. Multiple antennas are paired with a load balancing mechanism for seamless on-board Internet. The combined use of multi-cell access and distributed load balancing mechanism provide a balanced QoS across all carriages that heavily mitigates the service discontinuities due to LTE HOs. The proposed architecture does not imply any change on network side. Conclusions are supported by numerical results for realistic LTE parameters in current HST settings.

Large-scale adoption of dense cloud-based wireless network technologies in industrial plants is mandatorily paired with the development of methods and tools for connectivity prediction and deployment validation. Layout design procedures must be able to certify the quality (or reliability) of network information flow in industrial scenarios characterized by harsh propagation environments. In addition, these procedures must account for possibly coexisting heterogeneous radio access technologies as part of the Internet of Things (IoT) paradigm, easily allow post-layout validation steps, and be integrated by industry-standard CAD-based planning systems. The goal of the paper is to set the fundamentals for comprehensive industry-standard methods and procedures supporting plant designer during wireless coverage prediction, virtual network deployment, and post-layout verification. The proposed methods carry out the prediction of radio signal coverage considering typical industrial environments characterized by highly dense building blockage. They also provide a design framework to properly deploy the wireless infrastructure in interference-limited radio access scenarios. In addition, the model can be effectively used to certify the quality of machine-type communication by considering also imperfect descriptions of the network layout. The design procedures are corroborated by experimental measurements in an oil refinery site [modeled by three-dimensional (3-D) CAD] using industry-standard ISA IEC 62734 devices operating at 2.4 GHz. A graph-theoretic approach to node deployment is discussed by focusing on practical case studies, and also by looking at fundamental connectivity properties for random deployments.

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.

The design of Medium Access Control (MAC) protocols for wireless sensor networks (WSNs) has been conventionally tackled by assuming battery-powered devices and by adopting the network lifetime as the main performance criterion. While WSNs operated by energy-harvesting (EH) devices are not limited by network lifetime, they pose new design challenges due to the uncertain amount of energy that can be harvested from the environment. Novel design criteria are thus required to capture the trade-offs between the potentially infinite network lifetime and the uncertain energy availability. This paper addresses the analysis and design of WSNs with EH devices by focusing on conventional MAC protocols, namely TDMA, framed-ALOHA (FA) and dynamic-FA (DFA), and by accounting for the performance trade-offs and design issues arising due to EH. A novel metric, referred to as delivery probability, is introduced to measure the capability of a MAC protocol to deliver the measurement of any sensor in the network to the intended destination (or fusion center, FC). The interplay between delivery efficiency and time efficiency (i.e., the data collection rate at the FC), is investigated analytically using Markov models. Numerical results validate the analysis and emphasize the critical importance of accounting for both delivery probability and time efficiency in the design of EH-WSNs.

The establishment of a common frequency reference in a distributed wireless network is a critical factor in enabling any degree of node cooperation in communication and sensing functions. In this paper we introduce Distributed Frequency-Locked Loops (D-FLL's) to control the carrier frequencies of autonomous nodes with wireless communication capabilities. A novel frequency difference detector (FDD) is introduced allowing each node to estimate the frequency difference with respect to the ensemble of its neighbors. D-FLL's are characterized in terms of stability and tracking accuracy when the synchronization network has a peer-to-peer (mutually coupled - MC) or hierarchical (master slave - MS) topology. In particular, synchronization error accumulates in MS architectures as the distance from the reference node increases. MC architectures, instead, trade slower convergence times for a smoother error distribution. Overall, D-FLL's prove to be a robust and accurate technique for carrier frequency synchronization purposes that can be readily employed with any network architecture.

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.

The Dynamic Framed-ALOHA (DFA) protocol is studied for wireless sensor networks with energy limitations and energy-harvesting capability. The performance of DFA in this scenario is evaluated in terms of the time efficiency (or throughput), which is routinely used to evaluate medium access protocols, and by introducing a new metric, referred to as detection efficiency, which is tailored to scenarios with energy constraints. Specifically, detection efficiency measures the ability of a multiple access protocol to collect data from nodes without depleting their energy reserves. Analysis is first performed by assuming that DFA is operated with a perfect backlog (i.e., number of sensors left to be interrogated) knowledge. Then, a low-complexity backlog estimation algorithm is presented, which is shown by numerical results to perform close to the ideal case of perfect backlog knowledge.

A critical performance criterion in backscatter modulation-based RFID sensor networks is the distance at which a RFID reader can reliably communicate with passive RFID sensors (or tags). This paper proposes to introduce a power amplifier (PA) and an energy storage device (such as a capacitor or a battery), in the hardware architecture of conventional passive RFID tags, with the aim of allowing amplification of the backscatter signal to increase the read range. This new tag architecture, referred to as Amplified Backscattering via Energy Harvesting (ABEH), can still be considered as passive, since the energy storage device is charged exclusively by harvesting energy from the RF-signal transmitted by the reader and received by the tag during idle periods. The harvested and stored energy is then used by the tags to opportunistically amplify the backscatter signal. It is noted that this architecture is significantly different from active RFID tags where the battery, charged at the time of installation, is used to supply a complete onboard transceiver so that no backscatter modulation is employed. Energy scheduling strategies, based on the trade-off between energy harvesting rate and successful transmission probability, are proposed. Performance analysis of tags with the proposed ABEH architecture is carried out over quasi-static fading channels by framing the design problem as a Markov Decision Process. Numerical results show remarkable improvement of the ABEH approach with respect to conventional passive RFID tags and provide insight into the effect of system parameters on the energy scheduling.

Acquisition systems for sub-surface diagnostic (e.g., earthquake monitoring) require large number of sensors (geo-phones or accelerometers) to be deployed outdoor over large areas (tens of sqkm) to measure backscattered wave fields that are collected into a storage/processing unit (sink node). Aggregated data sets are analyzed to obtain an image of the sub-surface, monitor seismic activity, and declare possible alarm conditions. Cable based connectivity is the bottleneck of current systems, in terms of power consumption and degradation in accuracy. Replacing cables with wireless is now becoming attractive to improve the monitoring quality and reduce the probability of false negatives. Strict sampling synchronization constraint over large geographic areas, high precision sensor localization, high data-rate, and low delay are all topics that call for a scalable network system: Multi-Band Ultra Wide-Band radio transmissions (MB-UWB) play a key role as the only viable technology. This paper introduces the system and UWB network architecture based on ECMA-368 standard, moreover it provides a novel analytical tool to evaluate the energy consumption and delay during network set-up.

Wireless Sensor Networks (WSN) is the most promising technological paradigm to support the next generation highly efficient emergency management systems. Optimal design of WSN involves all the layers of the protocol stack: from the physical (PHY), the medium access layer (MAC) to the application layer. The design problem is conveniently cast in this paper for linear sensor network topologies where the terminals are equidistantly placed on the line between the source and the destination and are monitoring a correlated field. This simple topology can be adopted to provide insights to the performance of multihop networks used in several applications as monitoring systems, acoustic sensor arrays, seismic systems etc...

The Wireless over Cable (WoC) paradigm is a new technique to enable distributed antennas systems. WoC is based on the relay of radio frequency wireless signals over a cable infrastructure through a bidirectional (analog) amplify and forward device that translates the bandwidth of wireless signals to comply with the specifications of the wired links. Following the WoC concept, here the xDSL connection in femtocell architecture is replaced with a plain connection of the antenna with telephone lines after up/down-frequency conversion to enable Radio over Telephone Lines. Baseband processing and radio resource management is carried out simultaneously for multiple femtocells thus centralizing scheduling and mitigation of the radio frequency interference among neighboring femtocells. We propose the envisioned Radio over Telephone Lines architecture for femtocells and get insights into the hindrances of propagation over twisted pair copper lines via numerical analysis by employing OFDMA signals over the air interface (WiMAX/LTE) that coexist (and interfere) with xDSL services.

Dense monitoring of time-varying 2D field by Wireless Sensor Networks (WSN) needs to define new strategies to aggregate and encode data according to 2D sensor deployments. In this paper optimal design of communication protocol for WSN is conveniently cast for a set of linear sensor networks (sensor arrays) synchronously monitoring a correlated 2D time-varying field (e.g., for tracking time variations). Sensors aggregate data hop-by-hop by compressing the new samples gathered from the sensor board and forwarding towards the next sensor in the route. Compression is based on a linear predictive encoding to exploit the correlation properties of the field. Cross-layer design of source, channel coding and medium access control are jointly analyzed for optimal resource allocation and interference management to minimize energy consumption. Seismic monitoring application is analyzed to corroborate the design approach.

A novel distributed scheme that combines cooperative ARQ with the spectrum leasing paradigm is proposed and analyzed. The strategy harnesses the opportunistic gains of cooperative communications, while inherently providing a spectrum-rewarding incentive for the otherwise non-cooperative relays to assist the source's transmission. As in cooperative ARQ, the source might decide to hand over the possible retransmission slots to nearby stations that were able to decode the original transmission. In the proposed scheme, however, in exchange for the cooperation, the relaying station is also awarded an opportunity to exploit the retransmission slot for its own traffic. Arbitration of relays' retransmissions is performed via an auction mechanism, with the source, the competing relays and the transmission slot acting as the auctioneer, the bidders and the bidding article, respectively. Auction theory (more generally, the theory of Bayesian games) is applied to analyze the scheme performance. It is noted that the setting here can be alternatively seen as a practical framework for implementation of property-rights cognitive radio networks. Numerical results and analysis show that the proposed scheme enables an efficient dynamic resource allocation that provides relevant gains (e.g., transmission reliability) for both the original source (primary) and the cooperating nodes (secondary users).

The wireless network scenario here is based on N users that communicate one with the others through a central relay node by adopting the Analog Network Coding (ANC) paradigm. In this paper, we propose a communication scheme for ANC able to guaratee the simultaneous transmissions of N ¿ 2 interfering terminals using the concept of multiuser processing (i.e., channel estimation and detection). Under the assumption of slow-varying channels, multi-link channel estimation is achieved through the coherent summation at the physical layer of the invariant training sequences over the different frames, thus enabling multiuser detection. Benefits of the proposed ANC protocol are stressed through comparisons with the traditional TDMA-based routing scheme where the relay is time-shared among the different users. Impacts of the hindrances to our scheme (namely, hardware impairments at the nodes and finite coherence-time of the channels) are also investigated by numerical analysis. The proposed algorithm enables the employment of simple sense-before-talk policies at each user interested in accessing the network, thus avoiding complex forms of centralized coordination by the relay.

Dense frequency reuse makes the intercell interference control a crucial issue in cellular systems. Due to large signalling overhead, multicell processing and resource scheduling are not practicable solutions, at least today. We propose a decentralized transmission scheduling scheme where each base station exploits the knowledge of the intercell interference to locally allocate the resources. The idea is inspired by cognitive radio system where each (secondary) user accesses the spectrum according to the level of interference and thus it schedules the transmission over interference-free frequency intervals. Similarly, here each base station schedules the access to time or frequency resource so as to mitigate the generated interference and maximize its goodput. In this decentralized approach, the intercell signalling is replaced by the level of interference estimated locally and independently within each cell. Equilibria are analyzed using game theory with each scheduler acting as a player that locally maximizes its objective (goodput) while interacting (or interfering) with others.

We propose and analyze a novel decentralized mechanism that motivates otherwise non-cooperative stations to participate as relays in cooperative ARQ protocol. Cooperation incentive is provided by the possibility for the source to lease a portion of retransmission slot for the traffic of relaying terminals. To further leverage the opportunistic nature of cooperative ARQ and obtain a fully decentralized solution, the (motivated) relaying nodes compete for access to the retransmission slot by trying to make the best retransmission offer. Effective arbitration of cooperative retransmissions is performed using auction theory (bidding), with the source in the role of the auctioneer, the relaying nodes acting as the bidders and the (use of the) retransmission slot as the bidding article. It is noted that the proposed solution can be seen as a practical framework for the implementation of cognitive radio networks running according to the property-rights model (spectrum leasing). Numerical results and analysis confirm the efficient dynamic resource allocation property of the proposed scheme, revealing the relevant gains in terms of expected number of (re)transmissions required for successful data delivery for both the source (primary) and the cooperating (secondary) nodes.

The fluctuations of the price of crude oil is pushing the oil companies to increase the investments in seismic exploration of new oil and gas reservoir. Seismic exploration requires a large number (500 divide 2000 nodes/sqkm) of sensors (geophones or accelerometers) to be deployed in outdoor over large areas (ges 20 sqkm) to measure backscattered wave fields. A storage/processing unit (sink node) collects the measurements from all the geophones to obtain an image of the sub-surface in real-time. Current connectivity is cable based and requires hundreds of kilometers of cabling causing delays, high logistic costs and low imaging quality. This paper serves as a tutorial to introduce the basic principles of seismic acquisition systems from a wireless communication perspective and provides a number of requirements/specifications for the physical, MAC and network layer to develop wireless sensors networks tailored for oil (and gas) exploration. The wireless geophone network (WGN) system will replace the actual cabled systems used in on-shore seismic acquisition. Oil companies are currently pushing for wireless solutions. Early results suggested that a fully WiFi network does not satisfy all the requirements. This motivates the use of a mixture of technologies. In the proposed system wireless UWB devices/sensors are simultaneously sensing, self-localizing and synchronizing while delivering data to gateway devices in mesh mode. Gateways forward the aggregated traffic to storage unit over long range.

In direct sequence-code division multiple access (DS-CDMA) cellular systems spreading codes, fading and positions of the users can be modelled as independent random variables and the corresponding multiuser interference (MUI) experienced by the base station is non-stationary. Here we evaluate in closed form the bit error probability for space-time linear minimum mean square error (MMSE) multiuser receivers for symbol-synchronous DS-CDMA system (bounds are provided for symbol-asynchronous system) by extending the known asymptotic results for random spreading sequences to non-stationary MUI. The analysis is based on the effective interference at the decision variable that is carried out to account for the non-stationary MUI that results from the multiuser beamforming that adapts each spatial filter to the randomness of the angle of arrivals of all the users. Propagation for each user is Rayleigh-lognormal faded channels as it is fairly general to model the imperfect power-control. The numerical validation proves that a simple geometrical model is accurate to evaluate the error probability for any arbitrary system loading.

Time hopping is the strategy employed in impulse radio systems to increase the communication reliability by spreading the multiple access interference (MAI) over numerous time intervals. This results in a non-stationary interference that makes the error probability performance to be dominated by the worst-case. In this paper we employ a collision model analysis for this time-varying interference that is ruled by the system loading in term of the corresponding MAI collision probability. The model proves to be simple and accurate to characterize the decision variable conditioned to the collision events. This provides a reliable framework for evaluating the error probability in multipath faded channels with RAKE receiver either for coded and uncoded systems. In this paper we derive closed form approximations of the bit-error-rate for multipath channels with exponential power delay profile and Nakagami-m fading. Validations by numerical analysis show that the collision model provides an accurate framework to evaluate also performance of realistic channel models.

In this paper we evaluate outage performances of multirelay amplify and forward (AF) transmission over fading channels. We focus on the uplink of a two-hop based network where N (with N ges 1) single antenna relay stations (RSs) serve as non-regenerative repeaters for the message transmitted by a single antenna source node (or mobile station MS). Performances are derived at high SNR for arbitrary fading distributions over each cooperative link. In Rayleigh fading the high price of cooperation in terms of signalling (e.g. during MS-to-RSs transmissions and control messages broadcasts) causes a significant loss in spectral efficiency. Instead, provided that the RSs can be strategically positioned to benefit from a channel with marginal diffusive fading component compared to Rayleigh, this paper shows that this loss in efficiency can be significantly alleviated depending on the particular propagation environment. Closed form conditions on the fading statistics for the relayed links are derived to guarantee that collaborative transmission performs as if the MS node would benefit from the same diversity and bandwidth efficiency provided by multi-antenna transmission. The proposed conditions are computed for arbitrary distributed fading for the relays-to-BS link and are specialized for Rice faded RSs-to-BS links.

In common wireless systems pilot placing can be interpreted as a way to sample the channel with some degree of accuracy. In this letter we investigate the necessary conditions on the pilots spacing for time-varying fading to guarantee a specified average bit error rate. These constraints are evaluated in closed form for large SNR and small fading dynamics and specialized for varying fading correlation and coded/uncoded binary transmissions.

According to the commons model of cognitive radio, the activity of secondary (unlicensed) nodes is required to guarantee quality-of-service (QoS) constraints on the transmission of primary (licensed) terminals. Towards this goal, vertical handover between different radio interfaces is currently being investigated as a promising solution to enhance flexibility in unlicensed channel access. In this paper, we propose an analysis of cognitive radio with vertical handover capability in a simple scenario with one secondary node and two primary nodes that employ different radio interfaces with packet-based transmission. The maximum stable throughput of the secondary node is evaluated under maximum-delay QoS constraints on the primary activity as a function of system geometry, QoS constraints and sensing errors. Numerical results show the relevant advantages of optimal vertical handover in terms of the throughput of secondary nodes.

In infrastructure-less wireless systems network-wise time and frequency synchronization can be achieved by exchanging mutual synchronization errors among neighboring nodes. Cooperative synchronization is based on the use of distributed digital locked loops (D-DLLs), as the extension to distributed systems of the classical concept of (analog or digital) locked loops. The convergence to a synchronized state depends ultimately on the degree of connectivity of the network. D-DLLs can be specialized for time or frequency synchronization by adopting an appropriate error detector, but preserving the same control loop. The focus of this paper is on distributed frequency synchronization for packet-based communication. A novel detector is proposed, which approximates the local mean frequency error from the uncoordinated transmission of packets by neighboring nodes. The performance of distributed frequency locked loops (D-FLLs) is evaluated for a wireless network employing packet-based cooperative relaying. Numerical validations are used to compare different frequency synchronization protocols in terms of speed of convergence and degradation of end-to-end performances.

In this paper we consider a multicell and multi- antenna E-UTRA system as conforming to 3GPP recommendation. Focusing on the downlink, the system performance is severely hampered by the intercell interference due to the frequency reuse. For this reason, 3GPP is currently studying some policies to mitigate the interference impairments. We compare here two main approaches: interference coordination and interference randomization. A collision based model permits to model the intercell interference and to analytically assess the bit-error-rate for both the techniques. The analysis and the simulation results show that interference coordination is effective for moderate system load, whereas interference randomization achieves a definite performance improvement in case of heavy loaded systems due to the frequency diversity gain.

In fast-varying faded channels the transmission can be organized into frames where the channel estimation is mainly training-based. For fast-varying fading channels the training length and training interval can be optimized jointly to maximize the throughput. The optimal balance of training and payload depends on the combination of Doppler frequency and frame length. Here we show that, depending on the degree of mobility for large enough signal to noise ratio, there is a definite advantage in fragmenting the frame with dispersed segments of training symbols of smaller length rather than having a highly reliable channel estimate by concentrating all the training symbols at the beginning of the frame.

The concept of cognitive radio (or secondary spectrum access) is currently under investigation as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. According to the property-rights model of cognitive radio, the primary terminals own a given bandwidth and may decide to lease it for a fraction of time to secondary nodes in exchange for appropriate remuneration. In this paper, we propose and analyze an implementation of this framework, whereby a primary link has the possibility to lease the owned spectrum to an ad hoc network of secondary nodes in exchange for cooperation in the form of distributed space-time coding. On one hand, the primary link attempts to maximize its quality of service in terms of either rate or probability of outage, accounting for the possible contribution from cooperation. On the other hand, nodes in the secondary ad hoc network compete among themselves for transmission within the leased time-slot following a distributed power control mechanism. The investigated model is conveniently cast in the framework of Stackelberg games. We consider both a baseline scenario with full channel state information and information-theoretic transmission strategies, and a more practical model with long-term channel state information and randomized distributed space-time coding. Analysis and numerical results show that spectrum leasing based on trading secondary spectrum access for cooperation is a promising framework for cognitive radio.

Cooperative transmission protocols over fading channels are based on a number of relaying nodes to form virtual multi-antenna transmissions. Diversity provided by these techniques has been widely analyzed for the Rayleigh fading case. However, short range or fixed wireless communications often experience propagation environments where the fading envelope distribution is meaningfully different from Rayleigh. The main focus in this paper is to investigate the impact of fading distribution on performances of collaborative communication. Cooperative protocols are compared to co-located multi-antenna systems by introducing the concept of cooperative fading region. This is the collection of fading distributions for which relayed transmission can be regarded as a competitive option (in terms of performances) compared to multi-antenna direct (noncooperative) transmission. The analysis is dealt with by adopting the information theoretic outage probability as the performance metric. Cooperative link performances at high SNR are conveniently expressed here in terms of diversity and coding gain as outage parameters that are provided by the fading statistics of the channels involved in collaborative transmission. Advantages of cooperative transmission compared to multi-antenna are related to the propagation environment so that the analysis can be used in network design.

In this correspondence, joint (cooperative) decoding at the base stations combined with collaborative transmission (relaying) is investigated as a means to improve the uplink throughput of current cellular systems over fading channels. Intracell orthogonal medium access control (e.g., TDMA, FDMA, or orthogonal CDMA) and Decode-and-Forward relaying by either a mobile terminal or a fixed relay are assumed. Moreover, the cellular system is modeled according to a simplified framework introduced by Wyner. The per-cell achievable ergodic throughput is calculated for different system configurations and then characterized in the low-power (wideband) regime in terms of minimum energy per bit required for reliable communication and slope of the spectral efficiency. The analysis allows to clearly assess the relative merits of both cooperation among base stations and at the terminal level within the considered model.

In this letter, packet-by-packet vertical handover is investigated as a means to improve the performance of unlicensed spectrum access. The analysis focuses on the optimization of a channel access strategy for a cognitive multi-standard radio node that has the capability to switch between two orthogonal uplink radio interfaces, based on long-term channel state information. The radio interfaces differ for coverage and quality of service (QoS) requirements of primary users. The proposed strategy prescribes a cross-layer selection of physical-layer (transmitting powers) and medium access control layer (handover probability) parameters and is designed to maximize secondary throughput while guaranteeing the primary QoS constraints. Analysis and numerical results bring insight into the impact of network topology, measurement errors and primary QoS constraints on the optimal system design and corresponding performance.

Orthogonal random beamforming (ORB) constitutes a mean to exploit spatial multiplexing and multi-user diversity (MUD) gains in multi-antenna broadcast channels. To do so, as many random beamformers as transmit antennas (M) are generated and on each beam the user experiencing the most favorable channel conditions is scheduled. Whereas for a large number of users the sum-rate of ORB exhibits an identical growth rate as that of dirty paper coding, performance in sparse networks (or in networks with an uneven spatial distribution of users) is known to be severely impaired. To circumvent that, in this paper we modify the scheduling process in ORB in order to select a subset out of the M available beams. We propose several beam selection algorithms and assess their performance in terms of sum-rate and aggregated throughput (i.e., rate achieved with practical modulation and coding schemes), along with an analysis of their computational complexity. Since ORB schemes require partial channel state information (CSI) to be fed back to the transmitter, we finally investigate the impact of CSI quantization on system performance. More specifically, we prove that most of the MUD can be still exploited with very few quantization bits and we derive a beam selection approach trading-off system performance vs. feedback channel requirements.

In this paper we evaluate outage performance of a cooperative transmission protocol over fading channels that requires a number of relaying nodes to employ a distributed space-time coding scheme. Diversity provided by this technique has been widely analyzed for the Rayleigh fading case. However, ad-hoc and sensors networks often experience propagation environments where the line-of-sight component is either non zero or, in some cases, dominates compared to the random non line-of- sight components. By considering the Nakagami-m as a generic framework for describing the statistic of the fading impairments, this paper evaluates a set of fading inequalities that define settings where the benefits of collaborative transmission from multiple relays when varying fading parameters. These cooperative fading regions define the propagation settings that make cooperation preferable to multi-antenna non-cooperative transmission.

The performance of time hopping impulse radio systems is severely affected by asynchronous multiple access interference (MAI). The transmission reliability can be enhanced by using correction coding provided that some knowledge on the interference scenario is made available at the receiver. In this paper we design an efficient decoding and interference estimation receiver that embed interference estimation into maximum likelihood detector. The collision model approach permits to simplify the MAI estimation to the number of colliding users in each frame. We also analytically assess the error rate performance in the limiting case of perfect MAI estimation (lower bound on bit-error-rate) and no-estimation (upper bound). Simulation results validate the proposed scheme and the analytical analysis.

The capacity region of gaussian multi-input- multiple-output (MIMO) broadcast channels has been shown to be achieved by dirty paper coding (DPC). However, due to the high complexity of DPC and the infeasibility of the channel state information (CSI) at the transmitter, in this paper we focus on random beamforming with multiple beams (RB- MUX). The main advantages of RB-MUX are multiuser diversity and spatial multiplexing gain at limited feedback load. In this paper we analytically assess the ergodic sum-rate of RB-MUX scheme with multiple antennas at the receiver by considering three combining techniques: maximum ratio combiner (MRC), optimum combiner (OC) and antenna selection (AS). The analysis relies on the assumption of large average SNR, so that the additive noise can be assumed negligible as compared to the beams mutual interference. Simulation results corroborate our analysis.

A multicell system which supports multiple antennas and OFDMA modulation/access is considered in this paper as conforming IEEE 802.16-2005 standard. Focusing on the uplink, the system performance is severely hampered by the out-of-cell interference due to the frequency reuse. Here we analytically assess the error probability by employing a collision model to describe the non-stationarity of the out-of-cell interference due to random subcarriers permutation as prescribed by PUSC mode. The collision model permits an accurate analysis both of uncoded and coded transmissions. Two different decoding strategies are investigated according to the amount of information available at the receiver. Simulation results corroborate the proposed analysis.

Cooperation is increasingly regarded as a key technology for tackling the challenges of a practical implementation of cognitive radio. In this paper, we first give a brief overview of the envisioned applications of cooperative technology to cognitive radio, distinguishing among cooperative sensing for detection of the primary activity, cooperative transmission between secondary nodes and cooperative transmission of primary traffic by secondary users (cognitive relaying). Then, we focus on the latter scenario and investigate a simple wireless network, where one secondary transmitter has the option to relay traffic of the primary. Assuming that the primary is oblivious to the presence of the secondary (thus excluding the possibility of spectrum leasing), the secondary transmitter optimizes transmission/ relaying parameters towards the goal of maximizing the rate towards the secondary receiver. Numerical results are provided in order to discuss the advantage and limits of cognitive relaying.

Opportunistic scheduling in random beamforming maximizes the sum-rate by allocating resources to the users with the best channel condition, thus leveraging on multiuser diversity/multiplexing gain. Even if the scheduler assigns the resources according to the SINR, the main drawback to the practical deployment of these schemes is the intense feedback to be provided by every user. In this paper we address the sum-rate maximization of downlink multiuser MIMO space division multiple access systems based on 1-bit quantization of SINR feedback where each available spatial beam is assigned to a different user. To minimize signalling, here it is proposed a distributed method to locally estimate the optimal quantization thresholds among the terminals that guarantees a fair allocation of the available resources. Analytical results prove that the distributed scheme is asymptotically (when increasing the number of users) optimal in terms of sum-rate scaling law. Numerical simulations validate the analysis and highlight the capability of the distributed scheme to guarantee fairness constraints.

The large number of network nodes and the energy constraints make Wireless Sensor Networks (WSN) one of the most important application fields for Cooperative Diversity. Node cooperation increases the spatial diversity of wireless channels and, thus, reduces the transmitted power. In this paper, we propose a multi-hop WSN with nodes grouped in cooperative clusters that exploits transmit and receive cooperation among cluster nodes. Multi-hop transmission is carried out by concatenating single cluster-to-cluster hops, where every cluster-to-cluster link is defined as a cooperative distributed multiple-input-multiple-output (MIMO) channel. Transmit diversity is exploited through a time-division, decoder-and-forward, relaying scheme based upon two time slots: the Intracluster Slot, used for data sharing within the cluster, and the Intercluster Slot, used for transmission between clusters. At the receiver side, a distributed reception protocol is devised based upon a Selection Diversity algorithm. The proposed multi-hop cooperative WSN is optimally designed for minimum end-to-end outage probability by deriving the optimum time and power allocated on the intracluster and intercluster slots of every single hop, given a per-link energy constraint. A simplified suboptimum resource allocation is also proposed, which performs close to the optimal policy. Results show that the proposed scheme achieves diversity equal to the equivalent MIMO system and significantly reduces energy consumption with respect to. the non-cooperative channel.

This paper is focused on the optimization of transmitted power in a cooperative decoded relaying scheme for nodes belonging to the single primary route towards. a destination. The proposed transmission protocol, referred to as Multihop Cooperative Transmission Chain (MCTC), is based on the linear combination of copies of the same message by multiple previous terminals along the route in order to maximize the multihop diversity. Power allocations among transmitting nodes in the route can be obtained according to the average (not instantaneous) node-to-node path attenuation using a recursive power assignment. The latter can be employed locally on each node with limited signalling exchange (for fixed or nomadic terminals) among nodes. In this paper the power assignments for the MCTC strategy employing conventional linear combining schemes at receivers (i.e., selection combining, maximal ratio combining and equal gain combining) have been derived analytically when the power optimization is constrained to guarantee the end-to-end outage probability. In particular, we show that the power assignment that minimize the maximum spread of received power (min-max strategy) can efficiently exploit the multihop diversity. In addition, for ad hoc networks where the energy of each node is an issue, the MCTC protocol with the min-max power assignment increases considerably the network lifetime when compared to non-cooperative multihop schemes

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

A scenario with two single-user links, one licensed to use the spectral resource (primary) and one unlicensed (secondary or cognitive), is considered. According to the cognitive radio principle, the activity of the secondary link is required not to interfere with the performance of the primary. Therefore, in this paper, it is assumed that the cognitive link accesses the channel only when sensed idle. Moreover, the analysis includes: (1) random packet arrivals; (2) sensing errors due to fading at the secondary link; (3) power allocation at the secondary transmitter based on long-term measurements. In this framework, the maximum stable throughput of the cognitive link (in packets/slot) is derived for a fixed throughput selected by the primary link. The model is modified so as to allow the secondary transmitter to act as a ldquotransparentrdquo relay for the primary link. In particular, packets that are not received correctly by the intended destination might be decoded successfully by the secondary transmitter. The latter can, then, queue and forward these packets to the intended receiver. A stable throughput of the secondary link with relaying is derived under the same conditions as before. Results show that benefits of relaying strongly depend on the topology (i.e., average channel powers) of the network.

In this paper we consider a cooperative wireless network where each terminal communicates to a destination node with the aid of multiple relaying nodes. The focus is on cooperative transmission protocols that are based on the simultaneous transmission by a number of cooperating nodes. Outage performances are analyzed by assuming a distributed randomized orthogonal space-time coding scheme (DR-OSTC) to be employed by the relaying terminals during the transmission session. The DR-OSTC scheme requires that each cooperating node chooses randomly and independently to serve as one of the space-time virtual antennas. By avoiding any pre-defined terminal-to-codeword mapping, the random selection of the space-time codewords substantially reduces the needed control overhead with respect to other distributed space-time coding strategies simplifying the node coordination task. According to this scheme, in this paper it is tackled the problem of designing both the minimum number of cooperating nodes M and the spatial dimension L of the space-time code matrix so as to meet a specific outage probability requirement at the destination. Outage performances are also analyzed by developing simple but effective design rules tailored for two cooperative transmission protocols in realistic propagation environments.

Cooperation between base stations and collaborative transmission between mobile terminals are two technologies currently under study as promising paradigms for next generation communications systems. In this paper, we provide a first look to the interplay between these two approaches by studying the per-cell achievable sum-rate (throughput) of different cooperative protocols under a simplified model for the uplink of a TDMA cellular system. The analysis is limited to non-regenerative (amplify-and-forward) cooperation schemes between terminals for their'simplicity and appeal to a practical implementation. A closed form expression for the (asymptotic) achievable rate of multicell processing combined with amplify-and-forward collaboration at the terminals is derived for an AWGN (i.e., no fading) scenario. Moreover, the impact of fading is investigated numerically, allowing to draw some conclusions on the impact of multicell diversity (or macrodiversity) on the performance of collaborative schemes among the terminals. In particular, we show that while AF cooperation is generally advantageous for single cell processing (i.e., with no collaboration between base stations), its benefits when combined with multicell processing are limited to the regime of low to moderate transmission rates.

In presence of multiple access interference, the performance of impulse radio system is affected by collisions with other users. In this paper we evaluate the bit error probability by employing a collision model for describing the non-stationary interference caused by simultaneous transmissions in time hopping systems. The collision model provides a simple approximation for the error probability even in multipath channels since the decision variable can be conditioned to the collision events. Numerical validations of the error probability for several propagation settings (e.g., over Nakagami-m fading channels or realistic scenarios) employing the collision model show that the proposed method is simple and accurate for most practical applications.

This paper addresses the problem of estimating the shape parameters of seismic wavefields in linear array for the separation of waves by exploiting the diversity of the polarization state and propagation velocity between wavefields. We propose a method based on the shift-invariance properties of multiple wavefields impinging on a uniform linear array of sensors to separate different waves, by jointly estimating the velocity and polarization parameters. Furthermore, the surface waves are removed using a spatial filter specifically designed to exploit velocity and polarization of different waves. We introduce a model for wideband polarized signals received by an array of three-components sensors used as a framework for implementing the proposed algorithms. Examples on simulated and experimental data illustrate the applicability of the proposed methods

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 evaluate the capacity region of wireless ad hoc networks under a recently proposed space-time collaborative scheme. This protocol allows idle nodes to cooperate with the source opportunistically, i.e., whenever their wireless channel from the source is advantageous. The scheme is intrinsically distributed and well suited for an ad hoc scenario. It is shown analytically and through simulation that the capacity region of opportunistic collaboration is equal to or larger than (centralized) optimal multi-hop in case spatial reuse is not allowed by the transmission protocol. On the other hand, in case spatial reuse is possible, the relation between the two capacity regions has to be studied case by case. Simulation results prove that opportunistic collaborative communication is a promising paradigm for ad hoc networks that deserves further investigation.

In multi-antenna downlink systems the optimization of linear preceding is severely hampered by the amount of feedback. An efficient solution consists in opportunistic schemes, which generate M random beams and schedule the users with the highest signal-to-noise-plus-interference ratios (SINRs), which can be made available to the transmitter with very little feedback. Although this technique has been shown to be optimal for asymptotically large number of users, severe performance degradation occurs in practical system where the number of users is limited. In this paper we propose an enhancement of this strategy based on an adaptive beam selection procedure. Instead of transmitting all the generated beams, the scheduler picks the optimum subset of beams that maximizes the system sum-rate according to the feedback information. We propose and compare several beam selection algorithms according to different complexity requirements. In particular, we show that the proposed approaches give substantial gains with respect to conventional opportunistic schemes

Opportunistic schemes employing multiple beams have recently attracted significant interest due to the capability to achieve both multiuser diversity and spatial multiplexing gain at limited feedback load. In this paper we explore the effect of the feedback quantization on the performance of these schemes. We derive a closed-form expression of the system throughput with and without feedback quantization. By doing so, we analytically assess the impact of the number of terminals and the restriction in the feedback bandwidth. It is shown that most of the throughput is preserved by using a few quantization bits

Physical layer-based distributed timing synchronization among nodes of a wireless network is currently being investigated in the literature as an interesting alternative to packet synchronization. In this paper, we analyze the convergence properties of such a system through algebraic graph theory, by modelling the nodes as discrete-time oscillators and taking into account the specific features of wireless channels (e.g., reciprocity, fading). The analysis is corroborated by numerical results and by comparison with the performance of a practical implementation of the distributed synchronization algorithm over a bandlimited noisy channel

The cross-layer design is a promising strategy to maximize the spectral efficiency in communication systems. In this paper we focus on packet-oriented systems and we propose to combine adaptive modulation and coding (AMC) at physical layer and hybrid automatic repeat request (ARQ) at the data link layer. In the proposed multilevel hybrid ARQ (M-HARQ) strategy, the transmitted symbols are decomposed into a set of parallel sub-channels with an independent ARQ protocol over each sub-stream. It follows a layered structure where each sub-channel can be decoded only once the lower sub-channels have been resolved, thus subchannels are sequentially detected. The number of parallel streams is adaptively arranged according to the channel state information (CSI). Considerable throughput gain is achieved using the same encoder/decoder structure for all the sub-channels. Moreover, M-HARQ permits an adaptive and flexible implementation even when CSI is not available. The drawback is an increase in the decoding delay, which might be tolerable in some applications

In this paper, we investigate the benefits of exploiting the a priori information about the structure of the multipath channel on the performance of channel estimation for multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems. We first approach this problem from the point of view of estimation theory by computing a lower bound on the estimation error and studying its properties. Then, based on the insight obtained from the analysis, efficient channel estimators are designed that perform close to the derived limit. The proposed channel estimators compute the long-term features of the multipath channel model through a subspace tracking algorithm by identifying the invariant (over multiple OFDM symbols) space/time modes of the channel (modal analysis). On the other hand, the fast-varying fading amplitudes are tracked by using least-squares techniques that exploit temporal correlation of the fading process (modal filtering). The analytic treatment is complemented by thorough numerical investigation in order to validate the performance of the proposed techniques. MIMO-OFDM with bit-interleaved coded modulation and MIMO-turbo equalization is selected as a benchmark for performance evaluation in terms of bit-error rate.

In this paper, we investigate the benefits of exploiting the a priori information about the structure of the multipath channel on the performance of channel estimation for multiple-input multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems. We first approach this problem from the point of view of estimation theory by computing a lower bound on the estimation error and studying its properties. Then, based on the insight obtained from the analysis, efficient channel estimators are designed that perform close to the derived limit. The proposed channel estimators compute the long-term features of the multipath channel model through a subspace tracking algorithm by identifying the invariant (over multiple OFDM symbols) space/time modes of the channel (modal analysis). On the other hand, the fast-varying fading amplitudes are tracked by using least-squares techniques that exploit temporal correlation of the fading process (modal filtering). The analytic treatment is complemented by thorough numerical investigation in order to validate the performance of the proposed techniques. MIMO-OFDM with bit-interleaved coded modulation and MIMO-turbo equalization is selected as a benchmark for performance evaluation in terms of bit-error rate

This paper is focused on the performance of convolutional codes with bit interleaving in presence of non-stationary noise due to time-varying interference scenario. Two different decoding schemes are considered based on the knowledge of the instantaneous or the average noise power at the receiver. Analytical performance on average error probability has been derived under the assumption that interference can be modelled as Gaussian noise with random power. More specifically, the closed-form is for central /spl Xscr/E2 noise power with any degree of freedom, while upper bounds are derived for either static or fading channels. Simulations validate the analytical relationships and show that there is a definite advantage in exploiting the instantaneous noise power when the non-stationary interference can be modelled as time-varying power with heavily skewed distributions (e.g., lognormal).

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 the downlink of a broadcast fading channel, the base station can capitalize on multiuser diversity through channel aware scheduling. In MIMO systems, the design of the scheduler has to take into account the processing performed at the transmitter and the receivers. In this work, we consider channel aware scheduling for orthogonal linear precoding at the base station that guarantees interference free reception for each scheduled users. The problem is set in a novel mathematical framework and a scheduling algorithm is proposed that is shown by simulation to guarantee superior performance as compared to know techniques. Moreover, fairness constraints inspired by the proportional fair criterion are introduced in the scheduling process in order to guarantee the desired long term fairness properties.

In the downlink of a multi-user MIMO system over a fading channel, the base station can assign the available spatial streams to different users by capitalizing on multiuser diversity or enforcing fairness constraints. Assuming linear precoding (beamforming) at the base station, the problem amounts to the joint design of precoding matrices and channel aware scheduling, according to the cross-layer paradigm. In this paper we formulate the joint optimization of scheduling and linear precoding within a physical layer-oriented framework, where the performance metric is the transmission rate. Moreover, we constraint the precoding scheme to ensure interference-free reception of spatial streams (orthogonal space division multiple access, OSDMA). Fairness constraints are either inspired by the max-min or the proportional fair criterion. Performance of different schemes is evaluated by numerical simulations allowing to assess the tradeoffs between sum-rate (multi-user diversity) and fairness

High speed downlink packet access (HSDPA) is an evolutionary step to boost the WCDMA performance for downlink packet traffic as it increases spectral efficiency by supporting high user data rates. Because of the time varying channel fluctuations of wireless links, efficient use of the large available bandwidth cannot be achieved without adopting channel aware packet scheduling strategies. The HSDPA scheduling algorithm is based on splitting the scheduling decision into user selection and resource allocation. In this paper, we propose two user selection algorithms that can be adjusted (or pre-configured) in terms of cell throughput and/or user throughput fairness. Finally, we discuss a channel aware resource allocation algorithm that aims to maximize the number of served users among the selected ones, without any loss of the overall cell throughput with respect to the traditional greedy resource assignment.

Optimization of beamforming in broadcast MIMO channels is severely hampered by the amount of feedback. In this paper, we propose to reduce the feedback when employing a random beamforming where multiple streams can be linearly precoded before being transmitted on the antenna array and the users with channel/interference matched to the beamforming configurations are scheduled at each time. In MIMO system, the users can further improve the overall throughput by minimizing the inter-stream interference. The feedback necessary to employ the spatial multiplexing can be limited to the signal to interference ratios for a sub-set of users that are compatible for the random beamforming configuration. In spite of the simplicity, this scheduling strategy can take advantage of the multiuser diversity for a large number of independent channels (or users) as in OFDMA system. The numerical analysis validates the strategy for the selection of compatible users and the spectral efficiency for this opportunistic spatial multiplexing scheme

In OFDM communication systems over fading channels, link adaptation based on the available channel state information at the transmitter (i.e., power allocation, adaptive modulation and coding rate) increases the spectral efficiency and reliability of the link. In this work, we explore the possibility to extend the set of transmission parameters to be adaptively selected to the pilot arrangement (i.e., to the collocation of pilot subcarriers on the time-frequency grid). Based on the prediction of the channel estimation error at the receiver, the transmitter can minimize the number of pilot subcarriers that guarantees a sufficiently reliable channel estimate according to quality of service requirements. Prediction of the channel estimation error is performed by computing the error covariance matrix of Kalman filters. The adaptive pilot arrangement problem is formulated and a "greedy" solution is proposed. Simulations show the effectiveness of the algorithm with respect to periodic re-training.

In mobile communications the antenna arrays make it possible to estimate the path delay, angle of arrival (or angle), and amplitude for the multipath propagation channel. This paper considers the problem of joint angle and delay estimation (JADE) in a time-division code-division multiple access system. The angle/delay estimation is made from multiple slots by exploiting the angle/delay invariance of the channel regardless of the fast faded amplitude variation (i.e., angle/delay is assumed quasistatic). JADE can be approximated by methods that estimate the frequencies of the two-dimensional complex sinusoids where the faded amplitudes act as sources. The shift invariance method (JADE-ESPRIT) is chosen for its capacity to pair delay and angle for each path and each user. The consistency of JADE-ESPRIT for a large enough number of slots guarantees the feasibility of the method for spatially correlated noise. The performance of the JADE method for channel estimation is evaluated analytically and numerically in propagation environments with increasing complexity.

In orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels, channel estimation and tracking is generally carried out by transmitting known pilot symbols in given positions of the frequency-time grid. The traditional approach consists of two steps. First, the least-squares (LS) estimate is obtained over the pilot subcarriers. Then, this preliminary estimate is interpolated/smoothed over the entire frequency-time grid. In this paper, we propose to add an intermediate step, whose purpose is to increase the accuracy of the estimate over the pilot subcarriers. The presented techniques are based on the observation that the wireless radio channel can be parametrized as a combination of paths, each characterized by a delay and a complex amplitude. The amplitudes show fast temporal variations due to the mobility of terminals while the delays (and their associated delay-subspace) are almost constant over a large number of OFDM symbols. We propose to track the delay-subspace by a subspace tracking algorithm and the amplitudes by the least mean square algorithm (or modifications of the latter). The approach can be extended to multiple input multiple output OFDM or multicarrier code-division multiple-access systems. Analytical results and simulations prove the relevant benefits of the novel structure.

A transceiver structure for frequency-flat multiple-input multiple-output (MIMO) systems that comprises linear/nonlinear preequalization/equalization is optimized according to the minimum mean square error (MMSE) criterion under the assumption that only long-term channel state information (i.e., correlation matrices of fading channel and noise) is available at the transmitter. The structure generalizes different techniques known from the literature, such as BLAST, linear preequalization and equalization, and Tomlinson-Harashima precoding (THP). Simulations show that relevant benefits can be obtained by exploiting the long term channel state information at the transmitter in both dense multipath channels with relatively large correlation at the transmitter side and in sparse multipath channels.

The multiaccess interference and the array gain have been modeled to evaluate the average probability of error in direct-sequence code-division multiple-access receivers with adaptive antenna arrays. The angular gain of the spatial filter (array beampattern) is modeled by a piece-line function that approximate the passband (or in-beam) with a fixed gain and the stopband (or out-beam) with an equivalent attenuation. The users are partitioned into in-beam/out-beam interferers and are counted differently for the evaluation of the probability of error. This simple way to account for the multiaccess interference can be exploited to evaluate the average probability of error when the users are randomly distributed within an angular sector. The analytical model can be employed to evaluate the average performance for two-dimensional RAKE and linear minimum mean square error multiuser detection receivers over frequency selective Rayleigh fading channels.

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 this paper, we derive a lower bound on the MSE matrix of training-based channel estimators for MIMO systems over fast-varying fading channels. To this end, we consider an ideal estimator that is able to estimate the long-term features of the channel (e.g., second order statistics, delays...) with high accuracy while tracking the fast-varying fading fluctuations in an optimum (MMSE) way. The bound on the MSE matrix is a valuable tool as a reference to assess the performance of any proposed estimator and it is proved to reduce to known results for simplified settings.

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 mobile communications, the multipath space-time channel has some fast-varying (faded amplitudes of the paths) and slowly-varying (delays and directions of arrival) features. We propose to estimate the channel from limited-length training sequences by exploiting these different varying rates without explicitly computing delays and directions of arrival. Therefore, the multi-slot channel estimate is composed of two terms: the slowly-varying space-time bases estimated from L consecutive slots and the fast-varying amplitudes estimated on a slot-by-slot basis. Performance analysis and simulations confirm the expected benefits of the multi-slot approach and demonstrate that for large L the mean square error (MSE) on the channel estimate depends only on the number of fast-varying parameters.

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.

Blind signal separation (BSS) is a recurrent problem in many multi-sensors applications where observations can be modelled as mixtures of N statistical independent source signals. We propose the estimation of the orthonormal transformation matrix Q in the case of whitened observations and a cost function based on the fourth-order moments. Q is described as combination of elementary Givens rotations and the optimization is carried out jointly for all the rotations. When sub-sets of angles are optimized separately the method reduces to the deflation approach which has been proved to be globally convergent. The joint estimation of Givens rotation matrices has a computational complexity O(7N/sup 2/) and, compared to other adaptive BSS, simulations demonstrate that it converges faster and achieves a better crosstalk attenuation.

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).

The multi-access interference and the array gain has been modelled to evaluate the average probability of error in DS-CDMA receivers with adaptive antenna arrays. The angular gain of the spatial filter (array beam pattern) is modelled by a piece-line function that approximates the pass-band (or in-beam) with a fixed beamwidth and the stop-band (or out-beam) with an equivalent attenuation. The interferers are partitioned into in-beam and out-beam users and are counted differently for the evaluation of the probability of error. The in-beam users are counted as interference while the out-beam users increase the additive Gaussian noise level. This simple model adapted to 2D RAKE receiver structures and to space-time multiuser detection shows good agreements with simulation results.

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.

The propagating wavefield measured by complex sources-receivers arrangements can be characterized by a delayed waveform in the n-dimensional (n-D) space of measurements. The authors propose a method for the nonparametric estimation of the time of delay function (also referred to as wavefront) that does not require the knowledge of the waveform. The n-D wavefront is estimated by using a least squares (LS) approach that integrates the local estimates of the differential times of delay of the waveform between neighboring sensors. The method shows a lower sensitivity to errors in local estimates when the overall multidimensionality (and redundancy) of the data is exploited. Examples considered are derived from reflection seismology applications.

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.

A comprehensive analysis of electromagnetic (EM) inversion applied to pavement profiling by using a monostatic ground penetrating radar (GPR) is presented. Since in GPR systems using transfers EM (TEM) horns, the antenna is positioned close to the the investigated medium and a strong EM interaction occurs. This effect is taken into account by modeling the antenna with equivalent sources placed on the aperture section and by using an accurate EM modeling within the inversion loop in order to account for the contribution of near-field effects. Experimental results on the antenna modeling procedure and on a pavement profile estimation validate the feasibility of both the calibration and the inverse problem proposed.

The iterative delay estimation method is based on its analogy with parameter estimation of sinusoids in correlated noise. It assumes a-priori knowledge of echo waveform, their number, and the structure of the covariance matrix of noise. The advantages of this method are improved resolution as compared to the conventional matched filter approach and convergence, in a few iterations, to maximum likelihood estimate.

Monostatic ground penetrating radar (GPR) has proven to be a useful technique in pavement profiling. In road and highway pavements, layer thickness and permittivity of asphalt and concrete can be estimated by using an inverse scattering approach. Layer-stripping inversion refers to the iterative estimation of layer properties from amplitude and time of delay (TOD) of echoes after their detection. This method is attractive for real-time implementation, in that accuracy is improved by reducing false alarms. To make layer stripping useful, a multitarget detection/tracking (D/T) algorithm is proposed. It exploits the lateral continuity of echoes arising from a multilayered medium. Interface D/T means that both detection and tracking are employed simultaneously (not sequentially). For each scan, both detection of the target and tracking of the corresponding TOD of the backscattered echoes are based on the evaluated a posteriori probability density. The TOD is then estimated by using the maximum a posteriori (MAP) or the minimum mean square error (MMSE) criterion. The statistical properties of a scan are related to those of the neighboring ones by assuming, for the interface, a first-order Markov model.

The time of delay (TOD) estimation of multiple echoes is solved with an iterative multitarget detection/tracking algorithm. The evaluation of the TODs is based on their a-posteriori probability, while a first-order Markov model is used for a-priori probability estimation. The effectiveness of the algorithm (low false-alarm rate and robustness) is also experimentally proven. Moreover the algorithm exhibits a better noise rejection and an improved target resolution with respect to algorithms that perform separate detection and tracking.

Electromagnetic (EM) inversion is a useful tool for quantitative analysis in short-range applications of pulse radars. To estimate multilayered media properties using monostatic radar, two inverse scattering approaches are discussed: (a) layer-stripping algorithm by exploiting amplitude and time delay of radar echoes after their detection, and (b) EM inverse problem of parameter optimization by minimizing the mean square error between measured and modeled data. Redundancy in the estimation of media properties is given by spatial continuous measurements of the investigated media. This property is exploited in both the approaches investigated. In the layer-stripping approach the medium within each layer is homogeneous and the interfaces are assumed laterally continuous. In the inverse problem permittivity is assumed to be laterally smooth, implicit smoothing being given in the model parameterization. It is implicit in both methods that the inversion accuracy is strictly related to the amplitude stability of the radar and plane wave approximation. Therefore, the system calibration and the compensation of some propagation effects (e.g., near field, losses due to conductivity and to scattering from particles distributed between layers and on interfaces, pulse distortion) become crucial aspects for each specific application.

The phase of complex signals is wrapped since it can only be measured modulo-2/spl pi/; unwrapping searches for the 2/spl pi/-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing. Given an estimate of the phase gradient (i.e., of the instantaneous frequency), the 2-D unwrapped phase can be obtained as a solution of a variational problem. The analysis of unwrapping is done quite separately from instantaneous frequency estimation so that the reliability of both steps can be assessed independently. Various methods for evaluating 2-D instantaneous frequency are presented and compared in the presence of noise and amplitude variations. A study has also been made on aliasing arising in areas where, with respect to instantaneous frequency, spatial sampling is insufficient. The presence of noise in the data further complicates phase aliasing analysis since there is no way to distinguish between the aliasing due to noise or that due to steep phase slopes.>