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When interacting with mobile apps, users need to take decisions and make certain choices out of a set of alternative ones offered by the app. We introduce optimization problems through which we engineer the choices presented to users so that they are nudged towards decisions that lead to better outcomes for them and for the app platform. User decision-making rules are modeled by using principles from behavioral science and machine learning.

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Communication at mmWave frequencies is one of the major innovations of the fifth generation of cellular networks, because of the potential multi-gigabit data rate given by the large amounts of available bandwidth. The mmWave channel, however, makes reliable communications particularly challenging, given the harsh propagation environment and the sensitivity to blockage. Therefore, proper modeling of the mmWave channel is fundamental for accurate results in system simulations of mmWave cellular networks.

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In this paper, we design a self-powering dual mode simultaneous wireless information and power transfer (SWIPT) system in which a sensor node adaptively controls single tone or multi-tone communication mode. To this end, we introduce duty cycle operation for the dual mode SWIPT with self-powering, which considers nonlinear energy harvesting (EH) model for both single tone and multi-tone waveforms. We formulate an adaptive mode switching (MS) problem which maximizes the achievable rate under the energy causality condition.

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This paper studies a multi-source “age of information”
problem in multi-hop wireless networks with packetized
status updates and explicit channel contention. Specifically, the
scenario considered in this paper assumes that each node in the
network is a both a source and a monitor of information. Nodes
take turns broadcasting their information to other nodes in the
network while also maintaining tables of status updates for the
information received from all other nodes in the network. Lower

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Age of Information (AoI), measures the time elapsed since the last received information packet was generated at the source. We consider the problem of AoI minimization for single-hop flows in a wireless network, under pairwise interference constraints and time varying channel. We consider simple, yet broad, class of distributed scheduling policies, in which a transmission is attempted over each link with a certain attempt probability. We obtain an interesting relation between the optimal attempt probability and the optimal AoI of the link, and its neighboring links.

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Public Safety Networks (PSN) are expected to provide resilient communication paradigms under disaster recovery scenarios. Towards providing an energy efficient solution an UAV-supported multi-level architecture is employed where user equipments (UEs) are grouped together in clusters.

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Wireless-controlled robots, cars and other critical applications are in need of technologies that offer high reliability and low latency. Massive MIMO, Multiple-Input Multiple-Output, is a key technology for the upcoming 5G systems and is one part of the solution to increase the reliability of wireless systems. More specifically, when increasing the number of base station antennas in a massive MIMO systems the channel variations decrease and the so-called channel hardening effect appears. This means that the variations of the channel gain in

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