Search

This paper presents a new Wireless Power Transfer (WPT) approach by aligning the phases of a group of spatially distributed Radio Frequency (RF) transmitters (TX) at the target receiver (RX) device. Our approach can transfer energy over tens of meters and even to targets blocked by obstacles. Compared to popular beamforming based WPTs, our approach leads to a drastically different energy density distribution: the energy density at the target receiver is much higher than the energy density at other locations. Due to this unique energy distribution pattern, our approach offers a safer WPT solution, which can be potentially scaled up to ship a higher level of energy over longer distances. Specifically, we model the energy density distribution and prove that our proposed system can create a high energy peak exactly at the target receiver. Then we conduct detailed simulation studies to investigate how the actual energy distribution is impacted by various important system parameters, including number/topology of transmitters, transmitter antenna directionality, the distance between receiver and transmitters, and environmental multipath. Finally, we build an actual prototype with 17 N210 and 4 B210 Universal Software Radio Peripheral (USRP) nodes, through which we validate the salient features and performance promises of the proposed system.

We consider the communication between a source (user) and a receiver in the presence of multiple jammers, and study resource assignment in a non-cooperative game theory framework using communication latency as the user's utility. Equilibrium strategies are found in closed form, and their uniqueness is proven. This allows one to identify jammers whose equilibrium strategies have an on/off structure, or they are sensitive to small variations in the network parameters.

We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework. A player (the user or the jammer) has incomplete information about its rival’s identity in the form of uncertainty; the player only knows the probabilities that its rival is a player implementing a behavioral strategy as a follower in a Stackelberg game (smart-type), or selects a feasible strategy as in a Nash game (regular-type). We model the problem as two Bayesian games. In the first game, the user has incomplete information about the jammer, and in the second game, the jammer has incomplete information about the user. The user’s utility is throughput. We prove that a unique equilibrium exists and derive it in closed form as a function of the known probabilities. We show that the Nash and Stackelberg equilibria are boundary cases of the obtained equilibrium. Thus, our approach allows one to incorporate the Nash and Stackelberg equilibria into a unified scale of equilibria. Monotonicity properties of the equilibrium strategies and the corresponding payoffs with respect to the network parameters are proven, and also supported by simulations.

The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic’s trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant.

The paper considers a multi-access channel scenario, where several users communicate with a base station, and investigates power allocation is a game-theoretic framework. The communication metric is the inverse signal-to-interference-plus-noise ratio (SINR) at the base station, which, for low SINR reflects communication delay. Each user faces a trade-off between the latency of the signal received by the base station, and the price that the user pays for using a specific amount of power that causes interference in the system. The equilibrium is derived in closed form and its uniqueness is proven. It is shown that the resulting strategy allows each user to maintain uninterrupted communication. For comparison purposes, we construct a specific three user network scenario, and study the SINR and throughput metrics. In that setting, we show that, unlike the latency metric, SINR and throughput may give rise to multiple equilibria, which may cause destabilization of communication.

Drug resistance in cancer treatments is a frequent problem that, when it arises, leads to failure in therapeutic efforts. Tumor heterogeneity is the primary reason for resistance emergence and a precise treatment design that takes heterogeneity into account is required to postpone the rise of resistant subpopulations in the tumor environment. In this paper, we present a mathematical framework involving clonal evolution modeling of drug‐sensitive and drug‐resistant clones. Using our framework, we examine delaying the rise of resistance in heterogeneous tumors during control phase of therapy in a containment treatment approach. We apply pharmacokinetic/pharmacodynamic (PKPD) modeling and show that dosage strategies can be designed to control the resistant subpopulation. Our results show that the drug dosage and schedule determine the relative dynamics of sensitive and resistant clones. We present an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence for a given period of containment treatment., We present a mathematical framework involving clonal evolution modeling of drug‐sensitive and drug‐resistant clones to examine delaying the rise of resistance in heterogeneous tumors during the control phase of therapy in a containment treatment approach. We propose an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence, in the tumor environment.

A dual purpose communication and radar system is considered, operating in the presence of a jammer. The system transmits communication signals and uses their reflections off targets to support target tracking. A game theoretic framework is used to design the system, i.e., determine power allocations, so that the performance of both communication and radar components is optimized. Two metrics are considered and compared for system design: (a) a weighted combination of signal-to-interference-plus-noise ratio (SINR) of the communication and radar components, reflecting the performance of both components; and (b) a weighted combination of the inverse SINRs of the corresponding components, reflecting the latency in the performance of the components. The comparison suggests that the latency metric enables a design that requires less a priori information on fading gains, and further, as the decision rule can be derived in closed form, the design is easier implementable in practice. The interaction between the system and the jammer is modeled by non-zero sum games. A Bayesian extension of the games allows us to investigate the impact that incomplete information about the underlying network parameters has upon the strategies that should be employed.

We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework using communication latency as the user’s utility. The user switches between two different modes, i.e., the (a) regular transmission mode, according to which both players follow a Nash equilibrium; and the (b) smart transmission mode, according to which the user always implements the best response strategy. First, we consider the case in which the switching between transmission modes occurs with a given frequency. For this case we find the optimal transmission power of the user by formulating and solving a Bayesian game problem. We show that an increase in the frequency of smart transmissions leads to a decrease in communication latency and to an increase in the total transmission cost. We determine the switching frequency that optimizes the latency-cost trade off using α-fairness criteria. We also discuss the implications of the proposed latency metric on the player strategies as compared to the previously well studied signal-to-interference-plus-noise ratio (SINR) metric.