Elsherif, Fateh, authorChong, Edwin K. P., advisorJayasumana, Anura P., committee memberLuo, J. Rockey, committee memberAtadero, Rebecca, committee member2020-08-312020-08-312020https://hdl.handle.net/10217/211784Wireless networking has become an integral part of our everyday life. Certainly, wireless technologies have improved many aspects of the way people communicate, interact, and perform tasks, in addition to enabling new use cases, such as massive machine-type communications and industry verticals, among others. While convenient, these technologies impose new challenges and introduce new design problems. In this dissertation, we consider three problems in wireless networking. Specifically, we formulate optimization problems in green communication and security, and develop computationally efficient solutions to these optimization problems. First, we study the problem of base station (BS) dynamic switching for energy efficient design of fifth generation (5G) cellular networks and beyond. We formulate this problem as a Markov decision process (MDP) and use an approximation method known as policy rollout to solve it. This method employs Monte Carlo sampling to approximate the Q-value. In this work, we introduce a novel approach to design an energy-efficient algorithm based on MDP to control the ON/OFF switching of BSs; we exploit user mobility and location information in the selection of the optimal control actions. We start our formulation with the simple case of one-user one-ON. We then gradually and systematically extend this formulation to the multi-user multi-ON scenario. Simulation results show the potential of our novel approach of exploiting user mobility information within the MDP framework to achieve significant energy savings while providing quality-of-service guarantees. Second, we study the problem of jamming-aware-multi-path routing in wireless networks. Multipath routing is a technique for transmitting data from one or more source node(s) to one or more destination node(s) over multiple routing paths. We study the problem of wireless jamming-mitigation multipath routing. To address this problem, we propose a new framework for mitigating jamming risk based on semivariance optimization. Semivariance is a mathematical quantity used originally in finance and economics to measure the dispersion of a portfolio return below a risk-aversion benchmark. We map the problem of jamming-mitigation multipath routing to that of portfolio selection within the semivariance risk framework. Then we use this framework to design a new, and computationally feasible, RF-jamming mitigation algorithm. We use simulation to study the properties of our method and demonstrate its efficacy relative to a competing scheme that optimizes the jamming risk in terms of variance instead of semivariance. To the best of our knowledge, our work is the first to use semivariance as a measure of jamming risk. Directly optimizing objective functions that involve exact semivariance introduces certain computational issues. However, there are approximations to the semivariance that overcome these issues. We study semivariance problems—from the literature of finance and economics—and survey their solutions. Based on one of these solutions, we develop an efficient algorithm for solving semivariance optimization problems. Efficiency is imperative for many telecommunication applications such as tactile Internet and Internet of Things (recall that these types of applications have stringent constraints on latency and computing power). Our algorithm provides a general approach to solving semivariance optimization problems, and can be used in other applications. Last, we consider the problem of multiple--radio-access technology (multi-RAT) connectivity in heterogeneous networks (HetNets). Recently, multi-RAT connectivity has received significant attention—both from industry and academia—because of its potential as a method to increase throughput, to enhance communication reliability, and to minimize communication latency. We introduce a new approach to the problem of multi-RAT traffic allocation in HetNets. We propose a new risk-averse multi-RAT connectivity (RAM) algorithm. Our RAM algorithm allows trading off expected throughput for risk measured in throughput semivariance. Here we also adopt semivariance as a measure of throughput dispersion below a risk-aversion--throughput benchmark. We then formulate the multi-RAT connectivity problem as a semivariance-optimization problem. However, we tackle a different optimization problem in this part of the research. The objective function of the optimization problem considered here is different from the objective function of the optimization problem above that also uses semivariance to quantify risk (because the underlying standard form of portfolio selection is different). In addition, the set of constraints is different in this optimization problem: We introduce new capacity constraints to account for the stochastic capacity of the involved wireless links. We also introduce a new performance metric, the risk-adjusted throughput; risk-adjusted throughput is the ratio between the expected throughput and the throughput semideviation, where semideviation is the square root of semivariance. We evaluate the performance of our algorithm through simulation of a system with three radio-access technologies: 4G LTE, 5G NR, and WiFi. Simulation results show the potential gains of using our algorithm.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text