User centric and network centric approaches for resource and emergency alert optimization in wireless networks
Creators: name: Yousefvand, Mohammad
Publisher: name: No Publisher Supplied
Year: 2021

description: With the advent of HetNets and small cells as a viable solution to enable new 5G applications like ultra-reliable low latency communications (URLLC), the problems of user association and resource allocation in wireless HetNets have drawn a lot of attention in recent years. Due to the inherent interdependencies of these two problems, we cannot optimize one without considering its impact on the other one. Hence, we should jointly optimize them, which often results in formulating NP-hard optimization problems. To address such complexity, we have to design proper low complexity heuristic methods. In this thesis, we investigate two different approaches to solve this joint optimization problem, namely, network centric approach using a centralized optimization model based on Expected Utility Theory (EUT), and user centric approach using a distributed interactive game theoretic model based on Prospect Theory (PT).Network centric approaches often rely on solving centralized optimization problems. In this thesis, we first show that the centralized optimization of user association and resource allocation in HetNets is reducible to the well-known 0-1 Knapsack problem, and hence is NP-hard. Then, to reduce the computational complexity, we propose a machine learning aided heuristic model to efficiently solve it. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. Since the JUR optimization is NP-hard, we propose a low complexity learning based heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM) based solution to the CRO problem. Simulation results validate the efficiency of the proposed LHM method. Since network centric models are based on Expected Utility Theory (EUT), they are not capable of capturing the subjectivity of end-user decisions and its effects on the performance of wireless networks, especially under the presence of uncertainty in the network services and parameters. To explicitly address subjectivity, we use PT to study the impact of end-user decisions on service provider (SP) bidding and user/network association in a HetNet with multiple SPs while considering the uncertainty in the service guarantees offered by the SPs. Using PT to model end-user decision making that deviates from EUT, we formulate user association with SPs as a multiple leader Stackelberg game where each SP offers a bid to each user that includes a data rate with a certain probabilistic service guarantee and at a given price, while the user chooses the best offer among multiple such bids. We show that when users underweight the advertised service guarantees of the SPs (a behavior observed under uncertainty), the rejection rate of the bids increases dramatically which in turn decreases the SPs utilities and service rates. To overcome this, we design a two-stage learning-based optimized bidding framework for SPs. In the first stage, we use a support vector machine (SVM) learning algorithm to predict users' binary decisions (accept/reject bids), and then in the second stage we cast the utility-optimized bidding problem as a Markov Decision Problem (MDP) and use a reinforcement learning (RL)-based dynamic programming algorithm to efficiently solve it. Simulation results and computational complexity analysis validate the efficiency of the proposed bidding framework.We also investigate resource management for mobile networks during emergencies such as natural disasters and show that the current wireless emergency alerts (WEAs) are not efficient to increase users’ compliance with received alerts guidelines. WEAs motivate users to refrain from overloading the mobile network with non-necessary traffic during emergency situations where the capacity of the cellular network has been dramatically reduced due to damage to the communications infrastructure. In these situations, enabling people who are either trapped or in distress in isolated areas with limited network access, and allowing them to communicate with rescue and recovery teams in their neighborhoods is critical for saving them. In this thesis, we present a Cognitive Wireless Emergency Alert System (CWEAS) that introduces a cognition cycle to the current Integrated Public Alert and Warning System (IPAWS) and preserves bandwidth and protects against mobile network outage during emergencies, by using customized alerts and traffic prediction models that consider realtime monitoring information of the network, users and the environment.