Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure. As such, it is expected to play a key role in the deployment of limited-battery communicating
devices, as part of the 6G enabled Internet-of-Everything (IoE) vision. To date, radiating WPT technologies are mainly studied and designed assuming that the devices are located in the far-field region of the power radiating antenna, resulting in a relatively low energy transfer efficiency. However, with the transition of 6G systems to mmWave frequencies combined with the usage of large-scale antennas, future WPT devices are likely to operate in the radiating near-field (Fresnel) region. In this article, we provide an overview of the opportunities and challenges which arise from radiating near-field WPT. In particular, we discuss about the possibility to realize beam focusing in near-field radiating conditions, and highlight its possible implications for WPT in future {IoE} networks. Besides, we overview some of the design challenges and research directions which arise from this emerging paradigm, including its simultaneous operation with wireless communications, radiating waveform considerations, hardware aspects, and operation with typical antenna architectures.
Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance (e.g., tracking the state of a malicious user). Since there is
the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence (UAV-I). The UAVs intelligence includes the UAVs capability to process information and to make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. In this paper, we provide an overview of possible solutions for the design of UAVs of low complexity, showing some of the needs of the UAVs for running efficient localization operations, performed either as a team or individually. Further, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.
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-f
ield (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 signaling 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 are such that users residing at the same angular direction can communicate reliably without interfering with each other, which is not achievable using conventional far-field beam steering.
In the last years, we have experienced the evolution of wireless localization from being a simple add-on feature for enabling specific applications, to becoming an essential characteristic of wireless cellular networks, as for sixth generation cellul
ar networks. This perspective paper will revolve around the role of radio localization in all the cellular generations, from first generation to 6G, and it speculates about the idea of holographic localization enabled by the advent of large intelligent surfaces, made of metamaterials. Along this line, we briefly introduce the current state-of-the-art of the available technologies, and we illustrate the road ahead with the inherent challenges brought by this new technology.
Applications towards 6G have brought a huge interest towards arrays with a high number of antennas and operating within the millimeter and sub-THz bandwidths for joint communication and localization. With such large arrays, the plane wave approximati
on is often not accurate because the system may operate in the near-field propagation region (Fresnel region) where the electromagnetic field wavefront is spherical. In this case, the curvature of arrival (CoA) is a measure of the spherical wavefront that can be used to infer the source position using only a single large array. In this paper, we study a near-field tracking problem for inferring the state (i.e., the position and velocity) of a moving source with an ad-hoc observation model that accounts for the phase profile of a large receiving array. For this tracking problem, we derive the posterior Cramer-Rao Lower Bound (P-CRLB) and show the effects when the source moves inside and outside the Fresnel region. We provide insights on how the loss of positioning information outside Fresnel comes from an increase of the ranging error rather than from inaccuracies of angular estimation. Then, we investigate the performance of different Bayesian tracking algorithms in the presence of model mismatches and abrupt trajectory changes. Our results demonstrate the feasibility and high accuracy for most of the tracking approaches without the need of wideband signals and of any synchronization scheme. signals and of any synchronization scheme.
In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of
maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a reinforcement learning (RL) algorithm to infer its own policy of navigation (i.e., the control law). Numerical results show the feasibility of the proposed idea, highlighting the UAVs capability of autonomously exploring areas with high probability of target detection while reconstructing the surrounding environment.
Digital maps will revolutionize our experience of perceiving and navigating indoor environments. While today we rely only on the representation of the outdoors, the mapping of indoors is mainly a part of the traditional SLAM problem where robots disc
over the surrounding and perform self-localization. Nonetheless, robot deployment prevents from a large diffusion and fast mapping of indoors and, further, they are usually equipped with laser and vision technology that fail in scarce visibility conditions. To this end, a possible solution is to turn future personal devices into personal radars as a milestone towards the automatic generation of indoor maps using massive array technology at millimeter-waves, already in place for communications. In this application-oriented paper, we will describe the main achievements attained so far to develop the personal radar concept, using ad-hoc collected experimental data, and by discussing possible future directions of investigation.
