Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and collective proficiency. While PSA has been studied for single agents, its extension to a team of agents remains underexplored. This letter addresses this gap by introducing a framework for team PSA in centralized settings. Specifically, we investigate two metrics for centralized team PSA: the MPB and the KS statistic. These metrics quantify the in-situ discrepancy between predicted and actual measurements. Then, we use the KL divergence as a reference metric. Simulations in a target tracking scenario demonstrate that both MPB and KS metrics accurately capture model mismatches, align with the KL divergence reference, and enable real-time proficiency assessment.

With the emergence of extremely large-scale antenna arrays (ELAAs), the next generation of wireless communication is likely to occur in the radiating near-field region of base stations (BS). In such regions, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and coverage hole (dead zone). To cope with those issues, we propose a novel codebook design guideline for the uniform circular array by maximizing the overlapping coverage between the beam coverage (BC) and near-field region, where the energy spread effect in the near-field region is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we construct the beam coverage-based codebook structure and two-stage beam training (TSBT) scheme. Numerical simulations show that the TSBT scheme with the proposed codebook can potentially reduce beam training overhead while improving the success rate and patching the dead zone.

Radio frequency wireless power transfer enables charging low-power mobile devices without relying on wired infrastructures. Current existing wireless power transfer systems are typically designed assuming far-field propagation, where the radiated energy is steered to towards given angles, resulting in limited efficiency and possible radiation in undesired locations. An emerging technology for wireless signaling is based on dynamic metasurface antennas (DMAs), which efficiently realize electrically large arrays. When such arrays are employed at high frequencies, wireless power transfer might take place in the radiating near-field (Fresnel) region, where spherical wave propagation holds, providing more degrees-of-freedom and improved performance. In this article, we study wireless power transfer systems charging multiple devices in the Fresnel region, where the energy transmitter is equipped with a DMA, exploring how the antenna configuration can exploit the spherical wavefront to generate focused energy beams. In particular, after presenting a mathematical model for DMA-based radiating near-field wireless power transfer systems, we characterize the weighted sum-harvested energy maximization problem of the considered system, and we propose an efficient solution to jointly design the DMA weights and digital precoding vector. Then, by accounting for hardware constraints, we further extend our study to encompass practical scenarios with discrete phase shifts in DMA elements. Simulation results show that our design generates focused energy beams capable of improving energy transfer efficiency in the radiating near-field with minimal energy pollution.

Extremely large-scale antenna arrays (ELAAs) introduce a new communication paradigm called near-field communications, where users are likely to operate in the near-field region of the base-stations (BSs). In such a region, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and a coverage hole (dead zone). To cope with this issue, we developed a beam depth-based codebook and training scheme for near-field ELAA systems. As the performance of codebook design is mainly dictated by the array configurations, we study the codebook design considering uniform linear, circular and planar antenna arrays. Specifically, we first offer an integrated model to characterize the near-field channel for the considered array configurations. Then, we propose a novel codebook design guideline by maximizing the overlap depth between the near-field codeword (beam) coverage and near-field region, where the energy spread effect is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we respectively construct the beam depth based on two-stage and hierarchical codebooks as well as the corresponding beam training schemes. Numerical simulations show that the proposed codebook based beam training schemes can potentially reduce beam training overhead while improving the success rate and beam gain inside the dead zone.

In the context of fully distributed systems, we consider a scenario densely populated with wireless nodes which are equipped with simultaneous wireless information and power transfer (SWIPT) capabilities and can act as relays between a source and a destination. In this scenario, the probability of finding a node able to provide a useful contribution to the quality of the link tends to be high and can be exploited adopting a suitable relay selection scheme. Assuming nodes distributed following a Poisson point process and communicating only with source and destination to ensure the scalability of the system, we consider two well-known relay selection schemes, which are typically used as performance benchmarks, and derive an analytical framework to investigate the performance of the source-destination link in terms of outage probability. In our analysis we include, through the use of a simple but effective model, the non-linear effects of the energy harvesting circuitry of the devices and introduce a simple signaling mechanism between source, SWIPT nodes and destination to identify the most suitable relay among the various SWIPT devices. The impact of multiple access during the phase of relay selection is also modeled and investigated.

Emerging scenarios envision a widespread deployment of reconfigurable intelligent surfaces (RISs) across structures and objects to manage wave propagation and create smart radio environments (SREs). While numerous studies have explored indoor and outdoor integration of large-scale RIS, many of these analyses oversimplify channel characteristics and assume ideal or random phase profiles for RISs, which may not align with realistic situations. Thus, this paper introduces a comprehensive communication outage analysis that, unlike existing literature, accounts for practical phase profiles on the cascaded double Rician fading channel BS-RIS-UE. We validate our model through extensive numerical simulations, discussing the trade-offs between the number of RIS antennas and quantization bits per RIS.

Future automated driving relies on two pillars, (i) ultra-low-latency and reliable communications, and (ii) accurate positioning information. In particular, the knowledge of vehicle positions is becoming fundamental with the increase of the automation level, allowing autonomous navigation of the environment. Today’s positioning techniques cannot provide the accuracy, robustness, and latency required for stringent applications, like platooning, where vehicles are expected to travel at extremely short distances. In this paper, we leverage vehicle-to-everything (V2X) sidelink communication for localization purposes, capitalizing on the near-field propagation attributes of signals generated utilizing high carrier frequencies and/or large antenna arrays. Consequently, a receiving vehicle can accurately determine the transmitting vehicle’s location through V2X sidelink packet reception, obviating the need for supplementary reference nodes or stringent synchronization. Fundamental limits on localization accuracy are derived to characterize the positioning performance in vehicular contexts. A case study based on 5G new radio (NR) V2X sidelink shows how this technique is extremely promising and capable of providing high accuracy, low latency, high update rate, and high availability of position information in realistic vehicular scenarios.

Future scenarios foresee the deployment of a large amount of reconfigurable intelligent surfaces (RISs) covering buildings and objects to control the propagation of waves and to realize smart radio environments (SREs). Several works have already investigated the possibility to have a large-scale deployment of RISs indoors and outdoors, providing insightful considerations about the coverage and outage probability, especially in the absence of a direct base station (BS)-user (UE) link. Unfortunately, such works typically consider simplified propagation channels and assume ideal or random phase profiles at the RIS, which do not always fit real scenarios. This paper proposes a communication outage analysis that accounts for realistic RIS selection mechanisms and phase shift profiles. Differently from the literature, we first discuss some RIS association mechanisms and provide a general outage analysis, which is then specialized to the cascaded double Rician fading channel BS-RIS-UE. Finally, we provide extensive numerical evaluation, validated through simulations, to corroborate the proposed model to allow discussing the trade-off in terms of the number of employable RIS antennas and the number of quantization bits for each RIS element, which should be accounted for in the system design.

Sixth generation (6G) cellular communications are expected to support enhanced wireless localization capabilities. The widespread deployment of large arrays and high-frequency bandwidths give rise to new considerations for localization applications. Emerging antenna architectures, such as dynamic metasurface antennas (DMAs), are expected to be frequently utilized thanks to the achievable high angular resolution and low hardware complexity. Further, wireless localization is likely to take place in the radiating near-field (Fresnel) region, which provides new degrees of freedom, because of the adoption of arrays with large apertures. While current studies mostly focus on the use of costly fully-digital antenna arrays, in this paper we investigate how DMAs can be applied for near-field localization of a single user. We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy. Next, we propose an algorithm for configuring the DMA to optimize near-field localization, by first tuning the adjustable DMA coefficients to minimize the estimation error using postulated knowledge of the actual user position. Finally, we propose a sub-optimal iterative algorithm that does not rely on such knowledge. Simulation results show that the DMA-based near-field localization accuracy could approach that of fully-digital arrays at lower cost.

This paper considers a scenario where a reconfigurable intelligent surface (RIS) is deployed to allow the localization of mobile users adopting a single anchor node, even under non-line-of-sight (NLOS) channel conditions. When the RIS is large and the operating frequency is high, as in the millimeter-wave band, the system is likely to operate in the near-field propagation regime, which can be exploited to obtain robust localization. To this purpose, two practical signaling and positioning algorithms, based on an orthogonal frequency division multiplexing (OFDM) downlink system, are proposed along with methods to design the RIS time-varying reflection coefficients. In the numerical results, the two algorithms are compared in terms of performance in the presence of a synchronization mismatch and considering trade-offs between bandwidth, overhead, operating frequency, and latency. Finally, we provide an analysis of the soft-coverage capability, i.e., on the possibility of maintaining a high level of localization accuracy when in the presence of increasing levels of obstruction of the RIS.

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction.

Recently, dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing massive multiple-input multiple-output (MIMO) wireless systems. The usage of large arrays, jointly with higher transmitted frequencies, often results in the communicating devices operating in the near-field (Fresnel) region, thus requiring different considerations compared to traditional systems, assumed to operate in the far-field regime. In this paper, we study the potential of beam focusing, feasible in near-field operation, for multi-user MIMO systems, where the base station is equipped with a DMA. We introduce a mathematical model for DMA-based near-field MIMO communications. Then, we characterize the sum-rate maximization problem of the considered system, and propose an efficient solution to jointly design the DMA weights and digital precoding vector. Simulation results show that our design generates focused beams such that users residing at the same angular direction can communicate reliably without interfering, which is not achievable using conventional far-field beam steering.

Next 5G and beyond applications have attracted a tremendous interest towards systems using antenna arrays with an extremely large number of antennas where the technology conceived for communication might also be exploited for high-accuracy positioning applications. In this paper, we investigate the possibility to infer the position of a single antenna transmitter using a single asynchronous receiving node by retrieving information from the incident spherical wavefront. To this end, we consider the adoption of a suitable mix of processing at electromagnetic (EM) and signal levels, as a lower complexity alternative to classical massive array systems where the processing is done entirely at signal level. Thus, we first introduce a dedicated general model for different EM processing architectures, entailing the use or not of a lens that can have either a reconfigurable or a fixed phase profile, and successively we investigate their attainable positioning performance. The effect of the interference is also investigated to evaluate the robustness of the considered system to the presence of multiple simultaneous transmitting sources. Results, obtained for different apertures of the exploited lens/array, confirm the possibility to achieve interesting positioning performance using a single antenna array with a limited aperture.

Next 5G of mobile wireless systems is expected to employ mmWave antenna arrays at both user-equipment (UE) and base station (BS) side, with the possibility to precisely focus the power on the desired spatial directions and boost communications-based applications at an unprecedented scale. On the other side, the initial access (IA) procedure might entail a high latency due to the need of the BS to scan the surrounding space in order to detect new UEs. In this paper we consider a stand-alone 5G system capable to exploit the memory of past UEs detection to speed-up the IA process for new UEs entering the area. In particular, it considers the creation of a knowledge database that accounts for the UE received power and for both the beamsteering and half power beamwidth (HPBW) set at the BS. Numerical results show that the proposed procedure allows to reduce the average number of scans with respect to traditional approaches adopted in the literature, while preserving the same detection performance.

Recently, EM lens-embedded massive array antennas have been proposed for next 5G mobile wireless communications, as the adoption of a lens allows to discriminate the AOA of signals in the analog domain, with the possibility to preserve the processing complexity lower with respect to traditional massive arrays. In fact, in such a way, complex ADC chains can be avoided and the number of required antennas can be decreased. By exploiting these advantages, in this paper we study the possibility to use a single EM lens massive array at mm-wave for the AOA estimation of the received signal. In this perspective, ML estimator and practical approaches, tailored for the considered scenario, are derived. Results, obtained for different number of antennas, confirm the possibility to achieve interesting AOA estimation performance with an extremely compact architecture.

In the next generation of cellular networks, it is desirable to use single access points both for communication and localization. This could be made possible thanks to the combination of femtocells, mm-wave technology and massive antenna arrays, and would overcome the problem of having an over-sized infrastructure for positioning which is, nowadays, the bottleneck for the widespread diffusion of indoor localization systems. In this context, our paper aims at investigating the localization and orientation performance limits employing massive arrays both at the access point and mobile side. To this end, we first asymptotically demonstrate the tightness of the Cramér-Rao bound (CRB) in the massive array regime and that the effect of multipath can be made negligible even for practical values of SNR levels. Successively, we propose a comparison between two different transmitter configurations, namely multiple-input multiple-output (MIMO), where orthogonal waveforms are sent, and beamforming, which takes advantage of highly correlated waveforms and directive array patterns. We also consider random weighting as a trade-off between the diversity gain of MIMO and the high directivity guaranteed by the beamforming. CRB results show the interplay between diversity and beamforming gain as well as the benefits achievable by varying the number of antennas in terms of localization accuracy and multipath mitigation.

Presents comments on the paper, “Personal mobile radars with millimeter-wave massive arrays for indoor mapping,” (Guidi, F. et al), IEEE Trans. Mobile Comput., vol. 15, no. 6, pp. 1471–1484, Jun. 2016.

The adoption of massive arrays for simultaneous localization and mapping or personal radar applications enables the possibility to detect and localize surrounding objects through an accurate beamforming procedure. Unfortunately, when a classical constant false alarm rate approach accounting for ideal-pencil beam pattern is adopted, ambiguities in signal detection could arise due to the presence of side-lobes which can cause non-negligible errors in target detection and ranging. To counteract such effect, in this paper we propose a joint threshold-array design approach, where the antenna characteristics are taken into account to best set the threshold and to guarantee the desired detection and ranging performance at the non-coherent receiver section. In order to consider realistic arrays impairments, we focus our attention on the number of antenna elements and of phase shifter bits used for beamforming as key players in defining a trade-off between structural complexity, well-defined radiation pattern, and localization performance. Simulation and measurement results show that the number of bits per phase shifter can be relaxed in favor of a simpler array design, if the number of antennas is sufficiently high and the side-lobes are kept within a suitable level allowing a desired robustness to interference signals.

The adoption of millimeter-wave technology could open the possibility to integrate massive antenna arrays inside future 5G user mobile devices, with the possibility to enable new interesting applications. Within this context, in this paper we put forth the concept of a personal mobile radar operating at millimeter-waves and consisting of a massive array for accurate environmental mapping. Frequency selectivity and phase quantization effects are accounted for to characterize the achievable angle and range resolution necessary to collect environmental information. Successively, we propose an effective grid-based Bayesian mapping approach by introducing a new state-space model, which profits of the beneficial effects of the massive antenna array characteristics. Numerical results show that the idea herein investigated is feasible, and that a significant mapping performance is attainable even employing coarse antenna arrays provided that the number of antenna elements is sufficiently high.

Passive and semipassive ultrawideband (UWB) radio-frequency identification (RFID) technology has been recently proposed to offer high-accuracy localization capabilities in next-generation RFID systems. This technology relies on the modulation of backscattered signals, i.e., backscatter modulation, from multiple tags present in the environment. The detection of multiple tags based on backscattered signals is challenging in harsh environments with nonideal conditions such as clutter, near-far interference effects, and clock drift. This paper analyzes the detection of multiple tags employing UWB backscatter modulation and proposes practical signaling, spreading codes, and detection schemes that are robust to nonideal conditions. A case study is presented to evaluate the performance of the proposed technique for the detection of multiple tags based on impulsive backscattered signals.

Commercial ultrasound (US) equipment, although widely employed in diagnostic applications, is not suitable for the development and test of new investigation methods. Their typical architecture, designed for clinical use, is often "closed" and does not fit the requirements of flexibility, data access, programmability, which are necessary for the implementation of original approaches. More flexibility is achieved in high-level platforms, but they are typically characterized by high cost and dimensions. In this paper, a novel US system, specifically designed for research purposes, is presented. Its architecture is based on hi-end programmable devices to obtain the maximum flexibility with minimum cost and size. A preliminary example of application involving simultaneous B-mode and an experimental Doppler technique is discussed.

Ultrasound contrast agents (UCA) are widely used for the diagnosis of cardiovascular diseases. They typically consist of shell encapsulated microbubbles which, when injected in blood, increase the blood/tissue ratio in ultrasound medical images. Characterizing the behavior of microbubbles hit by US energy, it is important to optimize their performance in clinical applications. In this paper, the movement of microbubbles pushed away from US probes by the radiation force is considered. A simple experimental set-up is used to obtain M-mode images in which each microbubble describes a trace of variable brightness position and slope. A Kalman filter model allows an iterative estimation of the instantaneous values of variables such as the bubble position, velocity and acceleration, in a discrete time process. The model constrains the solution of the estimation by two control parameters: the variance of acceleration and the maneuverability, characterizing the bubble inertia. The accuracy of the method has been evaluated on artificial images reporting different phenomena: trace crossing, intensity variation, sudden interruption and different background noise levels. In most cases, the traces have been detected with a mean error lower than one pixel