This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative game theory. In particular, we first transform the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game in which each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the scheduling game. We prove the existence of NE and provide sufficient conditions guaranteeing the uniqueness of NE by utilizing the equivalence between the non-cooperative game and the Variational Inequality (VI) problem. A corresponding parallel updating algorithm for finding the NE is proposed which is proved to globally Part of this work was presented in the IEEE Asilomar conference 2020,
We present LLOCUS, a novel learning-based system that uses mobile crowdsourced RF sensing to estimate the location and power of unknown mobile transmitters in real time, while allowing unrestricted mobility of the crowdsourcing participants. We carefully identify and tackle several challenges in learning and localizing, based on RSS, in such a dynamic environment. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the power of a transmitter at an unknown location, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using three experiments in different indoor and outdoor environments. We find that LLOCUS reduces the localization error by 17-68% compared to several non-learning methods. CCS CONCEPTS • Networks → Location based services; Mobile ad hoc networks; Mobile and wireless security.
This article exploits the interaction dynamics of the elastic oceanic crust with the underlying mush complexes (MC) to constrain the axial topography of mid-ocean ridges (MORs). The effective viscosity (μeff) of MC beneath MORs is recognized as the crucial factor in modulating their axial high vs flat topography. Based on a two-step viscosity calculation (suspension and solid-melt mixture rheology), we provide a theoretical estimate of μeff as a function of melt suspension characteristics (crystal content, polymodality, polydispersity, and strain rate) and its volume fraction in the MC region. We then develop a numerical model to show the control of μeff on the axial topography. Using an enthalpy-porosity-based fluid formulation of uppermost mantle, the model implements a one-way fluid–structure interaction that transmits viscous forces of the MC region to the overlying upper crust. The limiting non-rifted topographic elevations (−0.06–1.27 km) of model MORs are found to occur in the viscosity range of μeff = 1012–1014 Pa s. The higher end (1013–1014) Pa s of this spectrum produces axial highs, which are replaced by flat or slightly negative topography as μeff≤5×1012 Pa s. We discuss a number of major natural MORs to validate the model findings.
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