Uncrewed vehicles (UVs) functioning as autonomous agents are anticipated to play a crucial role in the sixth generation (6G) of wireless networks. Their seamless integration, cost-effectiveness, and additional controllability through motion planning make them an attractive deployment option for a wide range of applications, both as assets in the network e.g., mobile base stations (BSs) and as consumers of network services (e.g., autonomous delivery systems). However, despite their potential, the convergence of UVs and wireless systems brings forth numerous challenges that require attention from both academia and industry. This article then aims to offer a comprehensive overview, encompassing the transformative possibilities as well as the significant challenges associated with UV-assisted next-generation wireless communications. Considering the diverse landscape of possible application scenarios, problem formulations, and mathematical tools related to UV-assisted wireless systems, the underlying core theme of this article is the unification of the problem space, providing a structured framework to understand the use cases, problem formulations, and necessary mathematical tools. Overall, this article sets forth a clear understanding of how UVs can be integrated in the 6G ecosystem, paving the way toward harnessing the full potential at this intersection.

The advances of 6G systems have prompted the study of new communication paradigms, including relay networks enabled by wireless power transfer (WPT). While existing literature focuses on the cooperative case, this paper examines a non-cooperative scenario in which a source must incentivize one of several battery-powered user equipments (UEs) to act as a relay by offering payment in the form of WPT, while the utility-maximizing UEs seeking to extract as much energy from the source as possible. We propose a protocol based on a reverse auction that enables the source to determine which candidate UE to select as the relay and the amount of energy to be transferred as payment, even when the channel quality between the candidates and the destination is unknown to the source. We first examine the performance of the system under the classical Vickrey auction. We prove that our protocol achieves the best possible outage probability and point out ways to mitigate the gap in energy efficiency when compared to a cooperative baseline. We then analyze system performance under a Myerson auction, which maximizes the auctioneer’s utility. To ensure computational tractability, we extend the forward auction regularity condition to the reverse setting, providing a mathematical characterization of regularity and the associated pricing mechanism. We then prove the regularity of the WPT-based auction under both lognormal and Rayleigh fading. To validate our analytical findings, we present extensive numerical results demonstrating how system parameters affect energy efficiency and outage probability. Our results show that auction-based protocols can reduce both outage probability and communication energy by more than 50% with as few as two relay candidates, compared to direct transmission by the source. Additionally, they demonstrate exponential convergence to the cooperative lower performance bound and indicate conditions under which one auction type is preferred over the other. Overall, the auction-based system offers a foundation for improved energy efficiency and communication reliability in non-cooperative environments.

This paper addresses the challenge of incentivizing energy-constrained, non-cooperative user equipment (UE) to serve as cooperative relays. We consider a source UE with a non-line-of-sight channel to an access point (AP), where direct communication may be infeasible or may necessitate a substantial transmit power. Other UEs in the vicinity are viewed as relay candidates, and our aim is to enable energy-efficient connectivity for the source, while accounting for the self-interested behavior and private channel state information of these candidates, by allowing the source to "pay" the candidates via wireless power transfer (WPT). We propose a cooperation-inducing protocol, inspired by Myerson auction theory, which ensures that candidates truthfully report power requirements while minimizing the expected power used by the source. Through rigorous analysis, we establish the regularity of valuations for lognormal fading channels, which allows for the efficient determination of the optimal source transmit power. Extensive simulation experiments, employing real-world communication and WPT parameters, validate our theoretical framework. Our results demonstrate over 71% reduction in outage probability with as few as 4 relay candidates, compared to the non-cooperative scenario, and as much as 70% source power savings compared to a baseline approach, highlighting the efficacy of our proposed methodology.

Recent advances in robotics present new paradigms in communication systems, particularly the use of autonomous vehicles to enhance communication capabilities in a number of settings. Polling systems, in which a single server services multiple queues and incurs some idle time when switching between these, provide useful models for a number of scenarios encountered in such systems. In this paper, we extend traditional polling systems to model these mobility-enabled communication scenarios by embedding the polling systems in 2D Euclidean space and defining service regions in this space where the queues' requests may be serviced. We show that this model can be used to capture the Age of Information (AoI) in the system as well. We pose the fundamental problem of minimizing the average wait time for a request, a key metric of the system's performance, and we provide lower and upper bounds on the optimal solution. Focusing on a cyclic visit order, we find provably optimal trajectories for the low- and high-traffic cases. Finally, we present numerical results from an IoT data collection scenario which illustrate the derived theory and show significant improvements in average wait time when compared to a move-stop-communicate baseline.

This paper presents a novel approach for crowd analytics using a single monostatic mmWave radar. We propose a new mathematical model that infers the crowd size for dynamic and quasi-dynamic crowd behaviors. More specifically, we derive a novel closed-form mathematical expression that describes the statistical dynamics of undercounting due to crowd shadowing. This new methodical finding allows for significantly improved crowd density estimates. For spatially-patterned crowds where the mathematical solution does not extend, we then develop a Temporal Convolutional Network (TCN) which is purely trained on simulated data. We perform extensive testing over a total of 22 experiments, with up to (and including) 21 people and in 4 different areas, including indoors, and the proposed mathematical solution achieves a Mean Absolute Error (MAE) of 1.53. Lastly, we show how our framework can infer anomalies, bottlenecks, and crowd engagement level. Overall, the paper can have a significant impact on crowd management and urban planning.

In this paper, we propose a new paradigm in intelligent surface design for field programming and multi-point focusing. We approach this problem from an entirely different vantage point by leveraging edges and the corresponding Geometrical Theory of Diffraction (GTD), allowing us to avoid the use of highly specialized and expensive element designs. More specifically, we show that a lattice of edge elements (i.e., cheap, thin, rectangular metal plates with length long enough as compared to width) can provide a rich repertoire for programming the RF field. When a wave is incident on an edge, a cone of outgoing rays emerges, known as a Keller cone. When considering a lattice of such edge elements, we then have a rich set of "knobs" for RF field programming, via changing the orientation of the edge elements and exploiting the exiting Keller cones. We then show how to electromagnetically model and design a practical edge element. We further propose an efficient algorithm to configure the orientations of the edges to achieve the desired multi-point focusing. We build sample prototypes of our proposed paradigm, using off-the-shelf material (i.e., 7 cent metal plates). We then show several real-world experiments in three different indoor areas, where an edge lattice focuses the transmitted wave of a WiFi card of a laptop on up to and including 4 focal points (maximum achieved in the literature albeit with much more expensive element designs). Overall, the paper shows the rich potential of edges for RF field programming.

This paper considers trajectory planning for a mobile robot which persistently relays data between pairs of far-away communication nodes. Data accumulates stochastically at each source, and the robot must move to appropriate positions to enable data offload to the corresponding destination. The robot needs to minimize the average time that data waits at a source before being serviced. We are interested in finding optimal robotic routing policies consisting of 1) locations where the robot stops to relay (relay positions) and 2) conditional transition probabilities that determine the sequence in which the pairs are serviced. We first pose this problem as a non-convex problem that optimizes over both relay positions and transition probabilities. To find approximate solutions, we propose a novel algorithm which alternately optimizes relay positions and transition probabilities. For the former, we find efficient convex partitions of the non-convex relay regions, then formulate a mixed-integer second-order cone problem. For the latter, we find optimal transition probabilities via sequential least squares programming. We extensively analyze the proposed approach and mathematically characterize important system properties related to the robot’s long-term energy consumption and service rate. Finally, through extensive simulation with real channel parameters, we verify the efficacy of our approach.

In this paper, we are interested in path optimization for robotic communication operations in obstacle environments. Consider a robot that needs to perform a given communication task (e.g., data uploading, broadcasting, or relaying) while navigating from a start position to a designated final position, avoiding obstacles, and minimizing its total motion and communication costs. Our goal is to develop a general path planning algorithm applicable to various robotic communication scenarios in which a robot operates in realistic channel fading environments and in the presence of obstacles. We show how we can adapt the traditional Rapidly-Exploring Random Tree Search Star (RRT*) path planning algorithm to jointly consider both communication and motion objectives in realistic channel environments that contain obstacles. We further show that our proposed approach can provide theoretical optimality guarantees while being computationally efficient. We extensively evaluate our proposed approach in realistic wireless channel environments for various transmission settings and communication tasks. The results demonstrate the efficacy of our proposed approach.