Enhancing the performance of energy harvesting wireless communications using optimization and machine learning

The motivation behind this thesis is to provide efficient solutions for energy harvesting communications. Firstly, an energy harvesting underlay cognitive radio relaying network is investigated. In this context, the secondary network is an energy harvesting network. Closed-form expressions are derived for transmission power of secondary source and relay that maximizes the secondary network throughput. Secondly, a practical scenario in terms of information availability about the environment is investigated. We consider a communications system with a source capable of harvesting solar energy. Two cases are considered based on the knowledge availability about the underlying processes. When this knowledge is available, an algorithm using this knowledge is designed to maximize the expected throughput, while reducing the complexity of traditional methods. For the second case, when the knowledge about the underlying processes is unavailable, reinforcement learning is used. Thirdly, a number of learning architectures for reinforcement learning are introduced. They are called selector-actor-critic, tuner-actor-critic, and estimator-selector-actor-critic. The goal of the selector-actor-critic architecture is to increase the speed and the efficiency of learning an optimal policy by approximating the most promising action at the current state. The tuner-actor-critic aims at improving the learning process by providing the actor with a more accurate estimation about the value function. Estimator-selector-actor-critic is introduced to support intelligent agents. This architecture mimics rational humans in the way of analyzing available information, and making decisions. Then, a harvesting communications system working in an unknown environment is evaluated when it is supported by the proposed architectures. Fourthly, a realistic energy harvesting communications system is investigated. The state and action spaces of the underlying Markov decision process are continuous. Actor-critic is used to optimize the system performance. The critic uses a neural network to approximate the action-value function. The actor uses policy gradient to optimize the policy\u27s parameters to maximize the throughput

Improving relay based cellular networks performance in highly user congested and emergency situations

PhDRelay based cellular networks (RBCNs) are the technologies that incorporate multi-hop communication into traditional cellular networks. A RBCN can potentially support higher data rates, more stable radio coverage and more dynamic services. In reality, RBCNs still suffer from performance degradation in terms of high user congestion, base station failure and overloading in emergency situations. The focus of this thesis is to explore the potential to improve IEEE802.16j supported RBCN performance in user congestion and emergency situations using adjustments to the RF layer (by antenna adjustments or extensions using multi-hop) and cooperative adjustment algorithms, e.g. based on controlling frequency allocation centrally and using distributed approaches. The first part of this thesis designs and validates network reconfiguration algorithms for RBCN, including a cooperative antenna power control algorithm and a heuristic antenna tilting algorithm. The second part of this thesis investigates centralized and distributed dynamic frequency allocation for higher RBCN frequency efficiency, network resilience, and computation simplicity. It is demonstrated that these benefits mitigate user congestion and base station failure problems significantly. Additionally, interweaving coordinated dynamic frequency allocation and antenna tilting is investigated in order to obtain the benefits of both actions. The third part of this thesis incorporates Delay Tolerate Networking (DTN) technology into RBCN to let users self-organize to connect to functional base station through multi-hops supported by other users. Through the use of DTN, RBCN coverage and performance are improved. This thesis explores the augmentation of DTN routing protocols to let more un-covered users connect to base stations and improve network load balancin

2014 Projects Day Booklet

https://scholarworks.seattleu.edu/projects-day/1029/thumbnail.jp

Recent Advances in Cellular D2D Communications

Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years. It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other. Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots. However, there are many challenges to realizing the full benefits of D2D. For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue. With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated. New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed. This edited book presents a collection of ten chapters, including one review and nine original research works on addressing many of the aforementioned challenges and beyond

White Paper for Research Beyond 5G

The documents considers both research in the scope of evolutions of the 5G systems (for the period around 2025) and some alternative/longer term views (with later outcomes, or leading to substantial different design choices). This document reflects on four main system areas: fundamental theory and technology, radio and spectrum management; system design; and alternative concepts. The result of this exercise can be broken in two different strands: one focused in the evolution of technologies that are already ongoing development for 5G systems, but that will remain research areas in the future (with “more challenging” requirements and specifications); the other, highlighting technologies that are not really considered for deployment today, or that will be essential for addressing problems that are currently non-existing, but will become apparent when 5G systems begin their widespread deployment

Docitive Networks. A Step Beyond Cognition

Projecte fet en col.laboració amb Centre Tecnològic de Telecomunicacions de CatalunyaCatalà: En les Xarxes Docents es por ta més enllà la idea d'elaborar decisions intel ligents. Per mitjà de compartir informació entre els nodes, amb l'objectiu primordial de reduir la complexitat i millorar el rendiment de les Xarxes Cognitives. Per a això es revisen alguns conceptes importants de les bases de l'Aprenentatge Automàtic, prestant especial atenció a l'aprenentatge per reforç. També es fa una visió de la Teoria de Jocs Evolutius i de la dinàmica de rèpliques. Finalment, simulacions ,basades en el projecte TIC-BUNGEE, es mostren per validar els conceptes introduïts.Castellano: Las Redes Docentes llevan más alla la idea de elaborar decisiones inteligentes, por medio de compartir información entre los nodos, con el objetivo primordial de reducir la complejidad y mejorar el rendimiento de las Redes Cognitiva. Para ello se revisan algunos conceptos importantes de las bases del Aprendizaje Automático, prestando especial atencion al aprendizaje por refuerzo, también damos una visón de la Teoría de Juegos Evolutivos y de la replicación de dinamicas. Por último, las simulaciones basadas en el proyecto TIC-BUNGEE se muestran para validar los conceptos introducidos.English: The Docitive Networks further use the idea of drawing intelligent decisions by means of sharing information between nodes with the prime aim of reduce complexity and enhance performance of Congnitive Networks. To this end we review some important concepts form Machine Learning, paying special atention to Reinforcement Learning, we also go insight Evolutionary Game Theory and Replicator Dynamics. Finally, simulations Based on ICT-BUNGEE project are shown to validate the introduced concepts

Teoria de jogos para utilização efetiva dos recursos em aplicações para 5G

Doutoramento em Engenharia Eletrotécnica - TelecomunicaçõesEsta tese tem como objetivo fornecer afirmações conclusivas em relação a utilização eficiente de recursos para redes e aplicações de 5G (5a geração) com recurso a teoria dos jogos. Neste contexto, investigamos dois cenários principais, um relativo a comunicações móveis e um outro relativo a redes inteligentes. Uma métrica importante para o desenho das redes móveis emergentes é a eficiência energética, com particular ênfase no lado do dispositivo móvel, onde as tecnologias das baterias são ainda limitadas. Alguns trabalhos de investigação relacionados têm demonstrado que a cooperação pode ser um paradigma útil no sentido de resolver o problema do défice energético. Contudo, pretendemos ir mais além, ao definir a cooperação e os utilizadores móveis como um grupo de jogadores racionais, que podem atuar sobre estratégias e utilidades, por forma a escolher a retransmissão mais apropriada para poupança de energia. Esta interpretação presta-se à aplicação da teoria dos jogos, e recorremos assim aos jogos coalicionais para solucionar conflitos de interesse entre dispositivos cooperantes, empregando Programação Linear (LP) para resolver o problema da selecção da retransmissão e derivar a principal solução do jogo. Os resultados mostram que a escolha do jogo de retransmissão coalicional proposto pode potencialmente duplicar a duração da bateria, numa era em que a próxima geração de dispositivos móveis necessitará de cada vez mais energia para suportar serviços e aplicações cada vez mais sofisticados. O segundo cenário investiga a resposta da procura em aplicações smart grid, que está a ganhar interesse sob a égide do 5G e que é considerada uma abordagem promissora, incentivando os utilizadores a consumir electricidade de forma mais uniforme em horas de vazio. Recorremos novamente à teoria dos jogos, imaginando as interacções estratégicas entre a empresa fornecedora de energia eléctrica e os potenciais utilizadores finais como um jogo de forma extensiva. São abordados dois programas em tempo real de resposta à procura: Day-Ahead Pricing (DAP) e Convex Pricing Tariffs. A resposta dos consumidores residenciais conscientes dos preços destas tarifas, é formulada como um problema de Mixed Integer Linear Programming (MILP) ou Quadratic Programming (QP), nos quais as soluções potenciais são o agendamento dos seus electrodomésticos inteligentes de modo a minimizar os seus gastos diários de electricidade, satisfazendo as suas necessidades diárias de energia e níveis de conforto. Os resultados demonstram que implementar o programa DAP pode reduzir a razão Peak-to-Average (PAR) at e 71% e as faturas de consumo das casas inteligentes at e 32%. Para além disso, a aplicação de tarifas convexas em tempo real pode melhorar ainda mais estas métricas de desempenho, alcançando uma redução de 80% do PAR e uma economia de mais de 50% na faturação da energia residencial.This research thesis aims to provide conclusive statements towards effective resource utilization for 5G (5th Generation) mobile networks and applications using game theory. In this context, we investigate two key scenarios pertaining to mobile communications and smart grids. A pivotal design driver for the upcoming era of mobile communications is energy efficiency, with particular emphasis on the mobile side where battery technology is still limited. Related works have shown that cooperation can be a useful engineering paradigm to take a step towards solving the energy deficit. However, we go beyond by envisaging cooperation and mobile users as a game of rational players, that can act on strategies and utilities in order to choose the most appropriate relay for energy saving. This interpretation lends itself to the application of game theory, and we look at coalitional games to settle conflicts of interest among cooperating user equipments, and employ Linear Programming (LP) to solve the relay selection problem and to derive the core solution of the game. The results reveal that adopting the proposed coalitional relaying game can potentially double battery lifetime, in an era where the next wave of next generation handsets will be more energy demanding supporting sophisticated services and applications. The second scenario investigates demand response in smart grid applications, which is also gaining momentum under the umbrella of 5G, which is a promising approach urging end-users to consume electricity more evenly during nonpeak hours of the day. Again, we resort to game theory and picture the strategic interactions between the electric utility company and the potential end-users as an extensive form game. Two real-time demand response programmes are addressed, namely Day-Ahead Pricing (DAP) and convex pricing tariffs. The response of price-aware residential consumers to these programmes is formulated as Mixed Integer Linear Programming (MILP) or Quadratic Programming (QP) problem, which optimally schedule their smart home appliances so as to minimise their daily electricity expenses while satisfying their daily energy needs and comfort levels. The results demonstrate that implementing the DAP programme can reduce the Peakto- Average Ratio (PAR) of demand by up to 71% and cut smart households bill by 32%. Moreover, applying real-time convex pricing tariffs can push these performance metrics even further, achieving 80% PAR reduction and more than 50% saving on the household electricity bill