Cross-Layer Resource Allocation and Scheduling in Wireless Multicarrier Networks

The current dominate layered networking architecture, in which each layer is designed and operated independently, results in inefficient and inflexible resource use in wireless networks due to the nature of the wireless medium, such as time-varying channel fading, mutual interference, and topology variations. In this thesis, we focus on resource allocation and scheduling in wireless orthogonal frequency division multiplexing (OFDM) networks based on joint physical and medium access control (MAC) layer optimization. To achieve orders of magnitude gains in system performance, we use two major mechanisms in resource management: exploiting the time variance and frequency selectivity of wireless channels through adaptive modulation, coding, as well as packet scheduling and regulating resource allocation through network economics. With the help of utility functions that capture the satisfaction level of users for a given resource assignment, we establish a utility optimization framework for resource allocation in OFDM networks, in which the network utility at the level of applications is maximized subject to the current channel conditions and the modulation and coding techniques employed in the network. Although the nonlinear and combinatorial nature of the cross-layer optimization challenges algorithm development, we propose novel efficient dynamic subcarrier assignment (DSA) and adaptive power allocation (APA) algorithms that are proven to achieve the optimal or near-optimal performance with very low complexity. Based on a holistic design principle, we design max-delay-utility (MDU) scheduling, which senses both channel and queue information. The MDU scheduling can simultaneously improve the spectral efficiency and provide right incentives to ensure that all applications can receive their different required quality of service (QoS). To facilitate the cross-layer design, we also deeply investigate the mechanisms of channel-aware scheduling, such as efficiency, fairness, and stability. First, using extreme value theory, we analyze the impact of multiuser diversity on throughput and packet delay. Second, we reveal a generic relationship between a specific convex utility function and a type of fairness. Third, with rigorous proofs, we provide a method to design cross-layer scheduling algorithms that allow the queueing stability region at the network layer to approach the ergodic capacity region at the physical layer.Ph.D.Committee Chair: Ye (Geoffrey) Li; Committee Member: Ian F. Akyildiz; Committee Member: James McClellan; Committee Member: John R. Barry; Committee Member: Xingxing Y

Distributed control architecture for multiservice networks

The research focuses in devising decentralised and distributed control system architecture for the management of internetworking systems to provide improved service delivery and network control. The theoretical basis, results of simulation and implementation in a real-network are presented. It is demonstrated that better performance, utilisation and fairness can be achieved for network customers as well as network/service operators with a value based control system. A decentralised control system framework for analysing networked and shared resources is developed and demonstrated. This fits in with the fundamental principles of the Internet. It is demonstrated that distributed, multiple control loops can be run on shared resources and achieve proportional fairness in their allocation, without a central control. Some of the specific characteristic behaviours of the service and network layers are identified. The network and service layers are isolated such that each layer can evolve independently to fulfil their functions better. A common architecture pattern is devised to serve the different layers independently. The decision processes require no co-ordination between peers and hence improves scalability of the solution. The proposed architecture can readily fit into a clearinghouse mechanism for integration with business logic. This architecture can provide improved QoS and better revenue from both reservation-less and reservation-based networks. The limits on resource usage for different types of flows are analysed. A method that can sense and modify user utilities and support dynamic price offers is devised. An optimal control system (within the given conditions), automated provisioning, a packet scheduler to enforce the control and a measurement system etc are developed. The model can be extended to enhance the autonomicity of the computer communication networks in both client-server and P2P networks and can be introduced on the Internet in an incremental fashion. The ideas presented in the model built with the model-view-controller and electronic enterprise architecture frameworks are now independently developed elsewhere into common service delivery platforms for converged networks. Four US/EU patents were granted based on the work carried out for this thesis, for the cross-layer architecture, multi-layer scheme, measurement system and scheduler. Four conference papers were published and presented

A real-time demand response pricing model for the smart grid

Submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)This thesis contributes to a novel model for Real-Time Price Suggestions (RTPS) of the Smart Grid (SG), which is the next generation modern bi-directional grid, particularly with respect to the pricing model. The research employs an experiment-based methodology which includes the use of a simulation technique. The research developed a Demand Response (DR) pricing model. Energy users are keen to reduce their bills, and Energy Providers (EP) is also keen on reducing their industrial costs. The DR model would benefit them both. The model has been tested with the UK-based traditional price value using real-time usage data. Energy users significantly reduced their bill and EP reduced their industrial cost due to load shifting. The Price Control Unit (PCU) and Price Suggestion Unit (PSU) utilise a set of embedded algorithms to vary price based upon demand. This model makes suggestions based on an energy threshold and makes use of Simultaneous Perturbation Stochastic Approximation Methods to produce prices. The results show that bill and peak load reductions benefit both the energy provider and users. The tests on a daily basis and monthly basis both benefit energy users and energy provider. The model has been validated by building a hardware prototype. This model also addresses users’ preferences; if users are non-responsive, they can still reduce their bills. The model contributes significantly to the existing models, and the novel contribution is the PSU which uniquely benefits energy users and provider. Therefore, there is a number of fundamental aspect of contributions to the model RTPS constitutes the final thesis of the PhD. The Real-Time Pricing is a better pricing system, algorithm developed on a daily basis and monthly basis and finally building a hardware prototype

Data-driven optimization of bus schedules under uncertainties

Plusieurs sous-problèmes d’optimisation se posent lors de la planification des transports publics. Le problème d’itinéraires de véhicule (PIV) est l’un d’entre eux et consiste à minimiser les coûts opérationnels tout en assignant exactement un autobus par trajet planifié de sorte que le nombre d’autobus entreposé par dépôt ne dépasse pas la capacité maximale disponible. Bien que les transports publics soient sujets à plusieurs sources d’incertitude (à la fois endogènes et exogènes) pouvant engendrer des variations des temps de trajet et de la consommation d’énergie, le PIV et ses variantes sont la plupart du temps résolus de façon déterministe pour des raisons de résolubilité. Toutefois, cette hypothèse peut compromettre le respect de l’horaire établi lorsque les temps des trajets considérés sont fixes (c.-à-d. déterministes) et peut produire des solutions impliquant des politiques de gestion des batteries inadéquates lorsque la consommation d’énergie est aussi considérée comme fixe. Dans cette thèse, nous proposons une méthodologie pour mesurer la fiabilité (ou le respect de l’horaire établi) d’un service de transport public ainsi que des modèles mathématiques stochastiques et orientés données et des algorithmes de branch-and-price pour deux variantes de ce problème, à savoir le problème d’itinéraires de véhicule avec dépôts multiples (PIVDM) et le problème d’itinéraires de véhicule électrique (PIV-E). Afin d’évaluer la fiabilité, c.-à-d. la tolérance aux délais, de certains itinéraires de véhicule, nous prédisons d’abord la distribution des temps de trajet des autobus. Pour ce faire, nous comparons plusieurs modèles probabilistes selon leur capacité à prédire correctement la fonction de densité des temps de trajet des autobus sur le long terme. Ensuite, nous estimons à l'aide d'une simulation de Monte-Carlo la fiabilité des horaires d’autobus en générant des temps de trajet aléatoires à chaque itération. Nous intégrons alors le modèle probabiliste le plus approprié, celui qui est capable de prédire avec précision à la fois la véritable fonction de densité conditionnelle des temps de trajet et les retards secondaires espérés, dans nos modèles d'optimisation basés sur les données. Deuxièmement, nous introduisons un modèle pour PIVDM fiable avec des temps de trajet stochastiques. Ce problème d’optimisation bi-objectif vise à minimiser les coûts opérationnels et les pénalités associées aux retards. Un algorithme heuristique basé sur la génération de colonnes avec des sous-problèmes stochastiques est proposé pour résoudre ce problème. Cet algorithme calcule de manière dynamique les retards secondaires espérés à mesure que de nouvelles colonnes sont générées. Troisièmement, nous proposons un nouveau programme stochastique à deux étapes avec recours pour le PIVDM électrique avec des temps de trajet et des consommations d’énergie stochastiques. La politique de recours est conçue pour rétablir la faisabilité énergétique lorsque les itinéraires de véhicule produits a priori se révèlent non réalisables. Toutefois, cette flexibilité vient au prix de potentiels retards induits. Une adaptation d’un algorithme de branch-and-price est développé pour évaluer la pertinence de cette approche pour deux types d'autobus électriques à batterie disponibles sur le marché. Enfin, nous présentons un premier modèle stochastique pour le PIV-E avec dégradation de la batterie. Le modèle sous contrainte en probabilité proposé tient compte de l’incertitude de la consommation d’énergie, permettant ainsi un contrôle efficace de la dégradation de la batterie grâce au contrôle effectif de l’état de charge (EdC) moyen et l’écart de EdC. Ce modèle, combiné à l’algorithme de branch-and-price, sert d’outil pour balancer les coûts opérationnels et la dégradation de la batterie.The vehicle scheduling problem (VSP) is one of the sub-problems of public transport planning. It aims to minimize operational costs while assigning exactly one bus per timetabled trip and respecting the capacity of each depot. Even thought public transport planning is subject to various endogenous and exogenous causes of uncertainty, notably affecting travel time and energy consumption, the VSP and its variants are usually solved deterministically to address tractability issues. However, considering deterministic travel time in the VSP can compromise schedule adherence, whereas considering deterministic energy consumption in the electric VSP (E-VSP) may result in solutions with inadequate battery management. In this thesis, we propose a methodology for measuring the reliability (or schedule adherence) of public transport, along with stochastic and data-driven mathematical models and branch-and-price algorithms for two variations of this problem, namely the multi-depot vehicle scheduling problem (MDVSP) and the E-VSP. To assess the reliability of vehicle schedules in terms of their tolerance to delays, we first predict the distribution of bus travel times. We compare numerous probabilistic models for the long-term prediction of bus travel time density. Using a Monte Carlo simulation, we then estimate the reliability of bus schedules by generating random travel times at each iteration. Subsequently, we integrate the most suitable probabilistic model, capable of accurately predicting both the true conditional density function of the travel time and the expected secondary delays, into the data-driven optimization models. Second, we introduce a model for the reliable MDVSP with stochastic travel time minimizing both the operational costs and penalties associated with delays. To effectively tackle this problem, we propose a heuristic column generation-based algorithm, which incorporates stochastic pricing problems. This algorithm dynamically computes the expected secondary delays as new columns are generated. Third, we propose a new two-stage stochastic program with recourse for the electric MDVSP with stochastic travel time and energy consumption. The recourse policy aims to restore energy feasibility when a priori vehicle schedules are unfeasible, which may lead to delays. An adapted algorithm based on column generation is developed to assess the relevance of this approach for two types of commercially available battery electric buses. Finally, we present the first stochastic model for the E-VSP with battery degradation. The proposed chance-constraint model incorporates energy consumption uncertainty, allowing for effective control of battery degradation by regulating the average state-of-charge (SOC) and SoC deviation in each discharging and charging cycle. This model, in combination with a tailored branch-and-price algorithm, serves as a tool to strike a balance between operational costs and battery degradation

Admission control and resource allocation for LTE uplink systems

Long Term Evolution (LTE) radio technologies aim not only to increase the capacity of mobile telephone networks, but also to provide high throughput, low latency, an improved end-to-end Quality of Service (QoS) and a simple architecture. The Third Generation Partnership Project (3GPP) has defined Single Carrier FDMA (SC-FDMA) as the access technique for the uplink and Orthogonal Frequency Division Multiple Access (OFDMA) for the downlink. It is well known that scheduling and admission control play an important role for QoS provisioning, and that they are strongly related. Knowing that we can take full advantage of this property we can design an admission control mechanism that uses the design criterion of the scheduling scheme. In this thesis, we developed two new algorithms for handling single-class resource allocation and two algorithms for handling multi-class resource allocation, as well as a new admission control scheme for handling multi-class Grade of Service (GoS) and QoS in uplink LTE systems. We also present a combined solution that uses the resource allocation and the admission control properties to satisfy the GoS and QoS requirements. System performance is evaluated using simulations. Numerical results show that the proposed scheduling algorithms can handle multi-class QoS in LTE uplink systems with a little increase in complexity, and can be used in conjunction with admission control to meet the LTE requirements. In addition, the proposed admission control algorithm gain for the most sensitive traffic can be increased without sacrificing the overall system capacity. At the same time, guaranteeing GoS and maintaining the basic QoS requirements for all the admitted requests

An Optimization Theoretical Framework for Resource Allocation over Wireless Networks

With the advancement of wireless technologies, wireless networking has become ubiquitous owing to the great demand of pervasive mobile applications. Some fundamental challenges exist for the next generation wireless network design such as time varying nature of wireless channels, co-channel interferences, provisioning of heterogeneous type of services, etc. So how to overcome these difficulties and improve the system performance have become an important research topic. Dynamic resource allocation is a general strategy to control the interferences and enhance the performance of wireless networks. The basic idea behind dynamic resource allocation is to utilize the channel more efficiently by sharing the spectrum and reducing interference through optimizing parameters such as the transmitting power, symbol transmission rate, modulation scheme, coding scheme, bandwidth, etc. Moreover, the network performance can be further improved by introducing diversity, such as multiuser, time, frequency, and space diversity. In addition, cross layer approach for resource allocation can provide advantages such as low overhead, more efficiency, and direct end-to-end QoS provision. The designers for next generation wireless networks face the common problem of how to optimize the system objective under the user Quality of Service (QoS) constraint. There is a need of unified but general optimization framework for resource allocation to allow taking into account a diverse set of objective functions with various QoS requirements, while considering all kinds of diversity and cross layer approach. We propose an optimization theoretical framework for resource allocation and apply these ideas to different network situations such as: 1.Centralized resource allocation with fairness constraint 2.Distributed resource allocation using game theory 3.OFDMA resource allocation 4.Cross layer approach On the whole, we develop a universal view of the whole wireless networks from multiple dimensions: time, frequency, space, user, and layers. We develop some schemes to fully utilize the resources. The success of the proposed research will significantly improve the way how to design and analyze resource allocation over wireless networks. In addition, the cross-layer optimization nature of the problem provides an innovative insight into vertical integration of wireless networks