Optimising energy efficiency and spectral efficiency in multi-tier heterogeneous networks:performance and tradeoffs

The exponential growth in the number of cellular users along with their increasing demand of higher transmission rate and lower power consumption is a dilemma for the design of future generation networks. The spectral efficiency (SE) can be improved by better utilisation of the network resources at the cost of reduction in the energy efficiency (EE) due to the enormous increase in the network power expenditure arising from the densification of the network. One of the possible solutions is to deploy Heterogeneous Networks (HetNets) consisting of several tiers of small cell BSs overlaid within the coverage area of the macrocells. The HetNets can provide better coverage and data rate to the cell edge users in comparison to the macrocells only deployment. One of the key requirements for the next generation networks is to maintain acceptable levels of both EE and SE. In order to tackle these challenges, this thesis focuses on the analysis of the EE, SE and their tradeoff for different scenarios of HetNets. First, a joint network and user adaptive selection mechanism in two-tier HetNets is proposed to improve the SE using game theory to dynamically re-configure the network while satisfying the user's quality-of-service (QoS) requirements. In this work, the proposed scheme tries to offload the traffic from the heavily loaded small cells to the macrocell. The user can only be admitted to a network which satisfies the call admission control procedures for both the uplink and downlink transmission scheme. Second, an energy efficient resource allocation scheme is designed for a two-tier HetNets. The proposed scheme uses a low-complexity user association and power allocation algorithm to improve the uplink system EE performance in comparison to the traditional cellular systems. In addition, an opportunistic joint user association and power allocation algorithm is proposed in an uplink transmission scheme of device to device (D2D) enabled HetNets. In this scheme, each user tries to maximise its own Area Spectral Efficiency (ASE) subject to the required Area Energy Efficiency (AEE) requirements. Further, a near-optimal joint user association and power allocation approach is proposed to investigate the tradeoff between the two conflicting objectives such as achievable throughput and minimising the power consumption in two-tier HetNets for the downlink transmission scheme. Finally, a multi-objective optimization problem is formulated that jointly maximizes the EE and SE in two-tier HetNets. In this context, a joint user association and power allocation algorithm is proposed to analyse the tradeoff between the achievable EE and SE in two-tier HetNets. The formulated problem is solved using convex optimisation methods to obtain the Pareto-optimal solution for the various network parameters

Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management

Data-Driven and Game-Theoretic Approaches for Privacy

abstract: In the past few decades, there has been a remarkable shift in the boundary between public and private information. The application of information technology and electronic communications allow service providers (businesses) to collect a large amount of data. However, this ``data collection" process can put the privacy of users at risk and also lead to user reluctance in accepting services or sharing data. This dissertation first investigates privacy sensitive consumer-retailers/service providers interactions under different scenarios, and then focuses on a unified framework for various information-theoretic privacy and privacy mechanisms that can be learned directly from data. Existing approaches such as differential privacy or information-theoretic privacy try to quantify privacy risk but do not capture the subjective experience and heterogeneous expression of privacy-sensitivity. The first part of this dissertation introduces models to study consumer-retailer interaction problems and to better understand how retailers/service providers can balance their revenue objectives while being sensitive to user privacy concerns. This dissertation considers the following three scenarios: (i) the consumer-retailer interaction via personalized advertisements; (ii) incentive mechanisms that electrical utility providers need to offer for privacy sensitive consumers with alternative energy sources; (iii) the market viability of offering privacy guaranteed free online services. We use game-theoretic models to capture the behaviors of both consumers and retailers, and provide insights for retailers to maximize their profits when interacting with privacy sensitive consumers. Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. In the second part, a novel context-aware privacy framework called generative adversarial privacy (GAP) is introduced. Inspired by recent advancements in generative adversarial networks, GAP allows the data holder to learn the privatization mechanism directly from the data. Under GAP, finding the optimal privacy mechanism is formulated as a constrained minimax game between a privatizer and an adversary. For appropriately chosen adversarial loss functions, GAP provides privacy guarantees against strong information-theoretic adversaries. Both synthetic and real-world datasets are used to show that GAP can greatly reduce the adversary's capability of inferring private information at a small cost of distorting the data.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Radio Resource Management for Wireless Mesh Networks Supporting Heterogeneous Traffic

Wireless mesh networking has emerged as a promising technology for future broadband wireless access, providing a viable and economical solution for both peer-to-peer applications and Internet access. The success of wireless mesh networks (WMNs) is highly contingent on effective radio resource management. In conventional wireless networks, system throughput is usually a common performance metric. However, next-generation broadband wireless access networks including WMNs are anticipated to support multimedia traffic (e.g., voice, video, and data traffic). With heterogeneous traffic, quality-of-service (QoS) provisioning and fairness support are also imperative. Recently, wireless mesh networking for suburban/rural residential areas has been attracting a plethora of attentions from industry and academia. With austere suburban and rural networking environments, multi-hop communications with decentralized resource allocation are preferred. In WMNs without powerful centralized control, simple yet effective resource allocation approaches are desired for the sake of system performance melioration. In this dissertation, we conduct a comprehensive research study on the topic of radio resource management for WMNs supporting multimedia traffic. In specific, this dissertation is intended to shed light on how to effectively and efficiently manage a WMN for suburban/rural residential areas, provide users with high-speed wireless access, support the QoS of multimedia applications, and improve spectrum utilization by means of novel radio resource allocation. As such, five important resource allocation problems for WMNs are addressed, and our research accomplishments are briefly outlined as follows: Firstly, we propose a novel node clustering algorithm with effective subcarrier allocation for WMNs. The proposed node clustering algorithm is QoS-aware, and the subcarrier allocation is optimality-driven and can be performed in a decentralized manner. Simulation results show that, compared to a conventional conflict-graph approach, our proposed approach effectively fosters frequency reuse, thereby improving system performance; Secondly, we propose three approaches for joint power-frequency-time resource allocation. Simulation results show that all of the proposed approaches are effective in provisioning packet-level QoS over their conventional resource allocation counterparts. Our proposed approaches are of low complexity, leading to preferred candidates for practical implementation; Thirdly, to further enhance system performance, we propose two low-complexity node cooperative resource allocation approaches for WMNs with partner selection/allocation. Simulation results show that, with beneficial node cooperation, both proposed approaches are promising in supporting QoS and elevating system throughput over their non-cooperative counterparts; Fourthly, to further utilize the temporarily available radio spectrum, we propose a simple channel sensing order for unlicensed secondary users. By sensing the channels according to the descending order of their achievable rates, we prove that a secondary user should stop at the first sensed free channel for the sake of optimality; and Lastly, we derive a unified optimization framework to effectively attain different degrees of performance tradeoff between throughput and fairness with QoS support. By introducing a bargaining floor, the optimal tradeoff curve between system throughput and fairness can be obtained by solving the proposed optimization problem iteratively

Resource Allocation and Service Management in Next Generation 5G Wireless Networks

The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other. In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems. First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework. Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs). Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined. Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond

Design issues in quality of service routing

The range of applications and services which can be successfully deployed in packet-switched networks such as the Internet is limited when the network does nor provide Quality of Service (QoS). This is the typical situation in today's Internet. A key aspect in providing QoS support is the requirement for an optimised and intelligent mapping of customer traffic flows onto a physical network topology. The problem of selecting such paths is the task of QoS routing QoS routing algorithms are intrinsically complex and need careful study before being implemented in real networks. Our aim is to address some of the challenges present m the deployment of QoS routing methods. This thesis considers a number of practical limitations of existing QoS routing algorithms and presents solutions to the problems identified. Many QoS routing algorithms are inherently unstable and induce traffic fluctuations in the network. We describe two new routing algorithms which address this problem The first method - ALCFRA (Adaptive Link Cost Function Routing Algorithm) - can be used in networks with sparse connectivity, while the second algorithm - CAR (Connectivity Aware Routing) - is designed to work well in other network topologies. We also describe how to ensure co-operative interaction of the routing algorithms in multiple domains when hierarchial routing is used and also present a solution to the problems of how to provide QoS support m a network where not all nodes are QoS-aware. Our solutions are supported by extensive simulations over a wide range of network topologies and their performance is compared to existing algorithms. It is shown that our solutions advance the state of the art in QoS routing and facilitate the deployment of QoS support in tomorrow's Internet