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.

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.

In this paper, we are interested in high-quality imaging of still objects with only received power measurements of off-the-shelf WiFi transceivers. We show that the scattered WiFi signals off of objects carry much richer information about the edges of the objects than the surface points. Based on this observation, we then propose a completely different way of thinking about this imaging problem. More specifically, we propose Wiffract, a new foundation for imaging objects via edge tracing. Our approach uses the Geometrical Theory of Diffraction (GTD) and the corresponding Keller cones to image edges of the object. We extensively validate our approach with 37 experiments in three different areas, including through-wall scenarios. We take developing a WiFi Reader as one example application to showcase the capabilities of our proposed pipeline. More specifically, we show how our approach can successfully image several alphabet-shaped objects. We further show that our approach enables WiFi to read, i.e., correctly classify the letters, with an accuracy of 86.7%. Finally, we show how our approach enables WiFi to image and read through walls, by imaging the details and further reading the letters of the word "BELIEVE" through walls. Overall, our proposed approach can open up new directions for RF imaging.