Artificial Intelligence Empowered UAVs Data Offloading in Mobile Edge Computing

The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs\u27 data to be offloaded to the selected MEC servers, while the existence of at least one Nash Equilibrium (NE) is proven exploiting the power of submodular games. A best response dynamics framework and two alternative reinforcement learning algorithms are introduced that converge to a NE, and their trade-offs are discussed. The overall framework performance evaluation is achieved via modeling and simulation, in terms of its efficiency and effectiveness, under different operation approaches and scenarios

Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated

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

Distributed radio resource allocation in wireless heterogeneous networks

This dissertation studies the problem of resource allocation in the radio access network of heterogeneous small-cell networks (HetSNets). A HetSNet is constructed by introducing smallcells(SCs) to a geographical area that is served by a well-structured macrocell network. These SCs reuse the frequency bands of the macro-network and operate in the interference-limited region. Thus, complex radio resource allocation schemes are required to manage interference and improve spectral efficiency. Both centralized and distributed approaches have been suggested by researchers to solve this problem. This dissertation follows the distributed approach under the self-organizing networks (SONs) paradigm. In particular, it develops game-theoretic and learning-theoretic modeling, analysis, and algorithms. Even though SONs may perform subpar to a centralized optimal controller, they are highly scalable and fault-tolerant. There are many facets to the problem of wireless resource allocation. They vary by the application, solution, methodology, and resource type. Therefore, this thesis restricts the treatment to four subproblems that were chosen due to their significant impact on network performance and suitability to our interests and expertise. Game theory and mechanism design are the main tools used since they provide a sufficiently rich environment to model the SON problem. Firstly, this thesis takes into consideration the problem of uplink orthogonal channel access in a dense cluster of SCs that is deployed in a macrocell service area. Two variations of this problem are modeled as noncooperative Bayesian games and the existence of pure-Bayesian Nash symmetric equilibria are demonstrated. Secondly, this thesis presents the generalized satisfaction equilibrium (GSE) for games in satisfaction-form. Each wireless agent has a constraint to satisfy and the GSE is a mixed-strategy profile from which no unsatisfied agent can unilaterally deviate to satisfaction. The objective of the GSE is to propose an alternative equilibrium that is designed specifically to model wireless users. The existence of the GSE, its computational complexity, and its performance compared to the Nash equilibrium are discussed. Thirdly, this thesis introduces verification mechanisms for dynamic self-organization of Wireless access networks. The main focus of verification mechanisms is to replace monetary transfers that are prevalent in current research. In the wireless environment particular private information of the wireless agents, such as block error rate and application class, can be verified at the access points. This verification capability can be used to threaten false reports with backhaul throttling. The agents then learn the truthful equilibrium over time by observing the rewards and punishments. Finally, the problem of admission control in the interfering-multiple access channel with rate constraints is addressed. In the incomplete information setting, with compact convex channel power gains, the resulting Bayesian game possesses at least one pureBayesian Nash equilibrium in on-off threshold strategies. The above-summarized results of this thesis demonstrate that the HetSNets are amenable to self-organization, albeit with adapted incentives and equilibria to fit the wireless environment. Further research problems to expand these results are identified at the end of this document

Resource Allocation for Multiple Access and Broadcast Channels under Quality of Service Requirements Based on Strategy Proof Pricing

The efficient allocation of power is a major concern in today’s wireless communications systems. Due to the high demand in data rate and the scarcity of wireless resources such as power, the multi-user communication systems like the multiple access channel (MAC) and broadcast channel (BC) have become highly competitive environments for the users as well as the system itself. Theory of microeconomics and game theory provide the good analytical manner for the selfish and social welfare conflict problems. Instead of maximizing the system sum rate, our proposed system deals with fulfilling the utility (rate) requirement of all the users with efficient power allocation. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements. We propose the framework to allocate the power to each user with universal pricing mechanisms. The prices act as the control signal and are assumed to be some virtual currency in the wireless system. They can influence the physical layer operating points to meet the desired utility requirements. Centralized and distributed power allocation frameworks are discussed separately in the thesis with different pricing schemes. In wireless systems we have users that are rational in the game theoretic sense of making decisions consistently in pursuit of their own individual objectives. Each user’s objective is to maximize the expected value of its own payoff measured on a certain utility scale. Selfishness or self-interest is an important implication of rationality. Therefore, the mobiles which share the same spectrum have incentives to misinterpret their private information in order to obtain more utility. They might behave selfishly and show also malicious behavior by creating increased interference for other mobiles. Therefore, it is important to supervise and influence the operation of the system by pricing and priority (weights) optimization. In the centralized resource allocation, we study the general MAC and BC (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The regulator ensures the QoS requirements of all users by clever pricing and prevents cheating. The simple system optimizer solves a certain system utility maximization problem to allocate the power with the given prices and weights (priorities). The linear and nonlinear pricing mechanisms are analyzed, respectively. It is shown that linear pricing is a universal pricing only if successive interference cancellation (SIC) for uplink transmission or dirty paper coding (DPC) for downlink transmission is applied at the base station (BS). For MAC without SIC, nonlinear pricing which is logarithmic in power and linear in prices is a universal pricing scheme. The prices, the resulting cost terms, the optimal power allocation to achieve the QoS requirement of each user in the feasible rate region are derived in closed form solutions for MAC with and without SIC using linear and nonlinear pricing frameworks, respectively. The users are willing to maximize their achievable rate and minimize their cost on power by falsely reporting their channel state information (CSI). By predicting the best cheating strategy of the malicious users, the regulator is able to detect the misbehavior and punish the cheaters. The infinite repeated game (RG) is proposed as a counter mechanism with the trigger strategy using the trigger price. We show that by anticipating the total payoff of the proposed RG, the users have no incentive to cheat and therefore our framework is strategy-proof. In the distributed resource allocation, each user allocates its own power by optimizing the individual utility function. The noncooperative game among the users is formulated. The individual prices are introduced to the utility function of each user to shift the Nash equilibrium (NE) power allocation to the desired point. We show that by implicit control of the proposed prices, the best response (BR) power allocation of each user converges rapidly. The Shannon rate-based QoS requirement of each user is achieved with minimum power at the unique NE point. We analyse different behavior types of the users, especially the malicious behavior of misrepresenting the user utility function. The resulting NE power allocation and achievable rates of all users are derived when malicious behavior exists. The strategy-proof mechanism is designed using the punishment prices when the types of the malicious users are detected. The algorithm of the strategy-proof noncooperative game is proposed. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations. The uplink transmission within the single cell of heterogeneous networks is exactly the same model as MAC. Therefore, the results of the pricing-based power allocation for MAC can be implemented into heterogeneous networks. Femtocells deployed in the Macrocell network provide better indoor coverage to the user equipments (UEs) with low power consumption and maintenance cost. The industrial vendors show great interest in the access mode, called the hybrid access, in which the macrocell UEs (MUEs) can be served by the nearby Femtocell Access Point (FAP). By adopting hybrid access in the femtocell, the system energy efficiency is improved due to the short distance between the FAP and MUEs while at the same time, the QoS requirements are better guaranteed. However, both the Macrocell base station (MBS) and the FAP are rational and selfish, who maximize their own utilities. The framework to successively apply the hybrid access in femtocell and fulfill the QoS requirement of each UE is important. We propose two novel compensation frameworks to motivate the hybrid access of femtocells. To save the energy consumption, the MBS is willing to motivate the FAP for hybrid access with compensation. The Stackelberg game is formulated where the MBS serves as the leader and the FAP serves as the follower. The MBS maximizes its utility by choosing the compensation prices. The FAP optimizes its utility by selecting the number of MUEs in hybrid access. By choosing the proper compensation price, the optimal number of MUEs served by the FAP to maximize the utility of the MBS coincides with that to maximize the utility of the FAP. Numerous simulation results are conducted, showing that the proposed compensation frameworks result in a win-win solution. In this thesis, based on game theory, mechanism design and pricing framework, efficient power allocation are proposed to guarantee the QoS requirements of all users in the wireless networks. The results are applicable in the multi-user systems such as heterogeneous networks. Both centralized and distributed allocation schemes are analyzed which are suitable for different communication scenarios.Aufgrund der hohen Nachfrage nach Datenrate und wegen der Knappheit an Ressourcen in Funknetzen ist die effiziente Allokation von Leistung ein wichtiges Thema in den heutigen Mehrnutzer-Kommunikationssystemen. Die Spieltheorie bietet Methoden, um egoistische und soziale Konfliktsituationen zu analysieren. Das vorgeschlagene System befasst sich mit der Erfüllung der auf Signal-zu-Rausch-und-Interferenz-Verhältnis (SINR) basierenden Quality-of-Service (QoS)-Anforderungen aller Nutzer mittels effizienter Leistungsallokation, anstatt die Übertragungsrate zu maximieren. Es wird ein Framework entworfen, um die Leistungsallokation mittels universellen Pricing-Mechanismen umzusetzen. In der Dissertation werden zentralisierte und verteilte Leistungsallokationsalgorithmen unter Verwendung verschiedener Pricing-Ansätze diskutiert. Die Nutzer in Funksystemen handeln rational im spieltheoretischen Sinne, indem sie ihre eigenen Nutzenfunktionen maximieren. Die mobilen Endgeräte, die dasselbe Spektrum nutzen, haben den Anreiz durch bewusste Fehlinterpretation ihrer privaten Informationen das eigene Ergebnis zu verbessern. Daher ist es wichtig, die Funktionalität des Systems zu überwachen und durch Optimierung des Pricings und Priorisierungsgewichte zu beeinflussen. Für den zentralisierten Ressourcenallokationsansatz werden der allgemeine Mehrfachzugriffskanal (Multiple Access Channel, MAC) und der Broadcastkanal (BC) mit linearen bzw. nichtlinearen Empfängern untersucht. Die Preise, die resultierenden Kostenterme und die optimale Leistungsallokation, mit der die QoS-Anforderungen in der zulässigen Ratenregion erfüllt werden, werden in geschlossener Form hergeleitet. Lineare und nichtlineare Pricing-Ansätze werden separat diskutiert. Das unendlich oft wiederholte Spiel wird vorgeschlagen, um Spieler vom Betrügen durch Übermittlung falscher Kanalinformationen abzuhalten. Für die verteilten Ressourcenvergabe wird das nichtkooperative Spiel in Normalform verwendet und formuliert. Die Nutzer wählen ihre Sendeleistung zur Maximierung ihrer eigenen Nutzenfunktion. Individuelle Preise werden eingeführt und so angepasst, dass die QoS-Anforderungen mit der Leistungsallokation im eindeutigen Nash-Gleichgewicht erfüllt werden. Verschiedene Arten des Nutzerverhaltens werden bezüglich der Täuschung ihrer Nutzenfunktion analysiert, und ein Strategy-Proof-Mechanismus mit Strafen wird entwickelt. Die Ergebnisse für den MAC sind anwendbar auf heterogene Netzwerke, wobei zwei neuartige Ansätze zur Kompensation bereitgestellt werden, die den hybriden Zugang zu Femtozell-Netzwerken motivieren. Mithilfe des Stackelberg-Spiels wird gezeigt, dass die vorgeschlagenen Ansätze in einer Win-Win-Situation resultieren

GAME THEORETIC APPROACH TO RADIO RESOURCE MANAGEMENT ON THE REVERSE LINK FOR MULTI-RATE CDMA WIRELESS DATA NETWORKS

This work deals with efficient power and rate assignment to mobile stations (MSs) involved in bursty data transmission in cellular CDMA networks. Power control in the current CDMA standards is based on a fixed target signal quality called signal to interference ratio (SIR). The target SIR represents a predefined frame error rate (FER). This approach is inefficient for data-MSs because a fixed target SIR can limit the MS's throughput. Power control should thus provide dynamic target SIRs instead of a fixed target SIR. In the research literature, the power control problem has been modeled using game theory. A limitation of the current literature is that in order to implement the algorithms, each MS needs to know information such as path gains and transmission rates of all other MSs. Fast rate control schemes in the evolving cellular data systems such as cdma2000-1x-EV assign transmission rates to MSs using a probabilistic approach. The limitation here is that the radio resources can be either under or over-utilized. Further, all MSs are not assigned the same rates. In the schemes proposed in the literature, only few MSs, which have the best channel conditions, obtain all radio resources. In this dissertation, we address the power control issue by moving the computation of the Nash equilibrium from each MS to the base station (BS). We also propose equal radio resource allocation for all MSs under the constraint that only the maximum allowable radio resources are used in a cell. This dissertation addresses the problem of how to efficiently assign power and rate to MSs based on dynamic target SIRs for bursty transmissions. The proposed schemes in this work maximize the throughput of each data-MS while still providing equal allocation of radio resources to all MSs and achieving full radio resource utilization in each cell. The proposed schemes result in power and rate control algorithms that however require some assistance from the BS. The performance evaluation and comparisons with cdma2000-1x-Evolution Data Only (1x-EV-DO) show that the proposed schemes can provide better effective rates (rates after error) than the existing schemes

Quality of Experience in Cyber-Physical Social Systems: A Cultural Heritage Space Use Case

In this PhD thesis, the focus is placed on the optimization of user Quality of Experience (QoE) in Cyber Physical Social Systems and speci cally in cultural heritage spaces. In order to achieve maximization of visitor perceived satisfaction, the challenges associated with visitor optimal decision making regarding touring choices and strategies in a museum or a cultural heritage space are examined and the problem of museum congestion is αddressed. Cultural heritage spaces, and museums in particular, constitute a special type of socio-physical system because, in contrast to other social systems like schools or churches, user experience is primarily controlled by the visitors themselves. Such a system also embodies both human behaviors and physical and technical constraints, a fact that makes adopting a socio-technical perspective in order to improve the visiting experience, essential. Within the above setting, quantitative models and functions are initially formulated to express the visitor experience that is gained throughout a touring process. The functions are based on several socio-physical and behavioral factors. Using this QoE modeling approach, the problem of how to optimise visitor route choices is addressed. A social recommendation and personalization framework is also presented that exploits common visitor characteristics and recommends a set of exhibits to be visited. The creation of self-organizing museum visitor communities are proposed as a means to enhance the visiting experience. They exploit visitor personal characteristics and social interactions and are based on a participatory action research (PAR) process. Recommendation Selection and Visiting Time Management (RSVTM) are combined and formulated into a two-stage distributed algorithm, based on game theory and reinforcement learning. In addition, this PhD thesis examines the problem of congestion management in cultural heritage spaces from a more pragmatic perspective, considering visitor behavioral characteristics and risk preferences. The motivation behind this approach arose from the observation that, in cultural heritage spaces, people interact with each other and consequently the decisions and behavior of one visitor influence and are influenced by others. It is, therefore, important to understand the unknown behavior tendencies of visitors especially when making decisions in order to improve their visiting experience and reduce museum congestion. The proposed mechanisms are founded on and powered by the principles of Prospect Theory and the Tragedy of the Commons. Particular attention is paid to modeling and capturing visitor behaviors and decision making under the potential risks and uncertainties which are typically encountered by visitors during their visit. According to their relative popularity and attractiveness, exhibits at a cultural heritage site are classi ed into two main categories: safe exhibits and Common Pool of Resources (CPR) exhibits. CPR exhibits are considered non-excludable and rivalrous in nature, meaning that they may experience "failure" due to over-exploitation. As a result, a visitor's decision to invest time at a CPR exhibit is regarded as risky because his/her perceived satisfaction greatly depends on the cumulative time spent at it by all visitors. A non-cooperative game among the visitors is formulated and solved in a distributed manner in order to determine the optimal investment time at exhibits for each visitor, while maximizing the visitor's perceived satisfaction. Detailed numerical results are presented, which provide useful insights into visitor behaviors and how these influence visitor perceived satisfaction, as well as museum congestion. Finally, pricing is introduced as an effective mechanism to address the problem of museum congestion. Motivated by several studies that position pricing as a mechanism to prevent overcrowding in museums, this thesis analyzes and studies the impact of different pricing policies on visitor decisions when they act as prospect-theoretic decision-makers. The theory of S-modular games is adopted to determine the time invested by each visitor at exhibits while maximizing satisfaction gained

5g new radio access and core network slicing for next-generation network services and management

In recent years, fifth-generation New Radio (5G NR) has attracted much attention owing to its potential in enhancing mobile access networks and enabling better support for heterogeneous services and applications. Network slicing has garnered substantial focus as it promises to offer a higher degree of isolation between subscribers with diverse quality-of-service requirements. Integrating 5G NR technologies, specifically the mmWave waveform and numerology schemes, with network slicing can unlock unparalleled performance so crucial to meeting the demands of high throughput and sub-millisecond latency constraints. While conceding that optimizing next-generation access network performance is extremely important, it needs to be acknowledged that doing so for the core network is equally as significant. This is majorly due to the numerous core network functions that execute control tasks to establish end-to-end user sessions and route access network traffic. Consequently, the core network has a significant impact on the quality-of-experience of the radio access network customers. Currently, the core network lacks true end-to-end slicing isolation and reliability, and thus there is a dire need to examine more stringent configurations that offer the required levels of slicing isolation for the envisioned networking landscape. Considering the factors mentioned above, a sequential approach is adopted starting with the radio access network and progressing to the core network. First, to maximize the downlink average spectral efficiency of an enhanced mobile broadband slice in a time division duplex radio access network while meeting the quality-of-service requirements, an optimization problem is formulated to determine the duplex ratio, numerology scheme, power, and bandwidth allocation. Subsequently, to minimize the uplink transmission power of an ultra-reliable low latency communications slice while satisfying the quality-of-service constraints, a second optimization problem is formulated to determine the above-mentioned parameters and allocations. Because 5G NR supports dual-band transmissions, it also facilitates the usage of different numerology schemes and duplex ratios across bands simultaneously. Both problems, being mixed-integer non-linear programming problems, are relaxed into their respective convex equivalents and subsequently solved. Next, shifting attention to aerial networks, a priority-based 5G NR unmanned aerial vehicle network (UAV) is considered where the enhanced mobile broadband and ultra-reliable low latency communications services are considered as best-effort and high-priority slices, correspondingly. Following the application of a band access policy, an optimization problem is formulated. The goal is to minimize the downlink quality-of-service gap for the best-effort service, while still meeting the quality-of-service constraints of the high-priority service. This involves the allocation of transmission power and assignment of resource blocks. Given that this problem is a mixed-integer nonlinear programming problem, a low-complexity algorithm, PREDICT, i.e., PRiority BasED Resource AllocatIon in Adaptive SliCed NeTwork, which considers the channel quality on each individual resource block over both bands, is designed to solve the problem with a more accurate accounting for high-frequency channel conditions. Transitioning to minimizing the operational latency of the core network, an integer linear programming problem is formulated to instantiate network function instances, assign them to core network servers, assign slices and users to network function instances, and allocate computational resources while maintaining virtual network function isolation and physical separation of the core network control and user planes. The actor-critic method is employed to solve this problem for three proposed core network operation configurations, each offering an added degree of reliability and isolation over the default configuration that is currently standardized by the 3GPP. Looking ahead to potential future research directions, optimizing carrier aggregation-based resource allocation across triple-band sliced access networks emerges as a promising avenue. Additionally, the integration of coordinated multi-point techniques with carrier aggregation in multi-UAV NR aerial networks is especially challenging. The introduction of added carrier frequencies and channel bandwidths, while enhancing flexibility and robustness, complicates band-slice assignments and user-UAV associations. Another layer of intriguing yet complex research involves optimizing handovers in high-mobility UAV networks, where both users and UAVs are mobile. UAV trajectory planning, which is already NP-hard even in static-user scenarios, becomes even more intricate to obtain optimal solutions in high-mobility user cases

Energy efficient offloading techniques for heterogeneous networks

Mobile data offloading has been proposed as a solution for the network congestion problem that is continuously aggravating due to the increase in mobile data demand. The concept of offloading refers to the exploitation of network heterogeneity with the objective to mitigate the load of the cellular network infrastructure. In this thesis a multicast protocol for short range networks that exploits the characteristics of physical layer network coding is presented. In the proposed protocol, named CooPNC, a novel cooperative approach is provided that allows collision resolutions with the use of an indirect inter-network cooperation scheme. Through this scheme, a reliable multicast protocol for partially overlapping short range networks with low control overhead is provided. It is shown that with CooPNC, higher throughput and energy efficiency are achieved, while it presents lower delay compared to state-of-the-art multicast protocols. A detailed description of the proposed protocol is provided, with a simple scenario of overlapping networks and also for a generalised scalable scenario. Through mathematical analysis and simulations it is proved that CooPNC presents significant performance gains compared to other state-of-the-art multicast protocols for short range networks. In order to reveal the performance bounds of Physical Layer Network Coding, the so-called Cross Network is investigated under diverse Network Coding (NC) techniques. The impact of Medium Access Control (MAC) layer fairness on the throughput performance of the network is provided, for the cases of pure relaying, digital NC with and without overhearing and physical layer NC with and without overhearing. A comparison among these techniques is presented and the throughput bounds, caused by MAC layer limitations, are discussed. Furthermore, it is shown that significant coding gains are achieved with digital and physical layer NC and the energy efficiency performance of each NC case is presented, when applied on the Cross Network.In the second part of this thesis, the uplink offloading using IP Flow Mobility (IFOM) is also investigated. IFOM allows a LTE mobile User Equipment (UE) to maintain two concurrent data streams, one through LTE and the other through WiFi access technology, that presents uplink limitations due to the inherent fairness design of IEEE 802.11 DCF. To overcome these limitations, a weighted proportionally fair bandwidth allocation algorithm is proposed, regarding the data volume that is being offloaded through WiFi, in conjunction with a pricing-based rate allocation algorithm for the rest of the data volume needs of the UEs that are transmitted through the LTE uplink. With the proposed approach, the energy efficiency of the UEs is improved, and the offloaded data volume is increased under the concurrent use of access technologies that IFOM allows. In the weighted proportionally fair WiFi bandwidth allocation, both the different upload data needs of the UEs, along with their LTE spectrum efficiency are considered, and an access mechanism is proposed that improves the use of WiFi access in uplink offloading. In the LTE part, a two-stage pricing-based rate allocation is proposed, under both linear and exponential pricing approaches, with the objective to satisfy all offloading UEs regarding their LTE uplink access. The existence of a malicious UE is also considered that aims to exploit the WiFi bandwidth against its peers in order to upload less data through the energy demanding LTE uplink and a reputation based method is proposed to combat its selfish operation. This approach is theoretically analysed and its performance is evaluated, regarding the malicious and the truthful UEs in terms of energy efficiency. It is shown that while the malicious UE presents better energy efficiency before being detected, its performance is significantly degraded with the proposed reaction method.La derivación del tráfico de datos móviles (en inglés data offloading) ha sido propuesta como una solución al problema de la congestión de la red, un problema que empeora continuamente debido al incremento de la demanda de datos móviles. El concepto de offloading se entiende como la explotación de la heterogeneidad de la red con el objetivo de mitigar la carga de la infraestructura de las redes celulares. En esta tesis se presenta un protocolo multicast para redes de corto alcance (short range networks) que explota las características de la codificación de red en la capa física (physical layer network coding). En el protocolo propuesto, llamado CooPMC, se implementa una solución cooperativa que permite la resolución de colisiones mediante la utilización de un esquema indirecto de cooperación entre redes. Gracias a este esquema, se consigue un protocolo multicast fiable i con poco overhead de control para redes de corto alcance parcialmente solapadas. Se demuestra que el protocolo CooPNC consigue una mayor tasa de transmisión neta (throughput) y una mejor eficiencia energética, a la vez que el retardo se mantiene por debajo del obtenido con los protocolos multicast del estado del arte. La tesis ofrece una descripción detallada del protocolo propuesto, tanto para un escenario simple de redes solapadas como también para un escenario general escalable. Se demuestra mediante análisis matemático y simulaciones que CooPNC ofrece mejoras significativas en comparación con los protocolos multicast para redes de corto alcance del estado del arte. Con el objetivo de encontrar los límites de la codificación de red en la capa física (physical layer network coding), se estudia el llamado Cross Network bajo distintas técnicas de Network Coding (NC). Se proporciona el impacto de la equidad (fairness) de la capa de control de acceso al medio (Medium Access Control, MAC), para los casos de repetidor puro (pure relaying), NC digital con y sin escucha del medio, y NC en la capa física con y sin escucha del medio. En la segunda parte de la tesis se investiga el offloading en el enlace ascendente mediante IP Flow Mobility (IFOM). El IFOM permite a los usuarios móviles de LTE mantener dos flujos de datos concurrentes, uno a través de LTE y el otro a través de la tecnología de acceso WiFi, que presenta limitaciones en el enlace ascendente debido a la equidad (fairness) inherente del diseño de IEEE 802.11 DCF. Para superar estas limitaciones, se propone un algoritmo proporcional ponderado de asignación de banda para el volumen de datos derivado a través de WiFi, junto con un algoritmo de asignación de tasa de transmisión basado en pricing para el volumen de datos del enlace ascendente de LTE. Con la solución propuesta, se mejora la eficiencia energética de los usuarios móviles, y se incrementa el volumen de datos que se pueden derivar gracias a la utilización concurrente de tecnologías de acceso que permite IFOM. En el algoritmo proporcional ponderado de asignación de banda de WiFi, se toman en consideración tanto las distintas necesidades de los usuarios en el enlace ascendente como su eficiencia espectral en LTE, y se propone un mecanismo de acceso que mejora el uso de WiFi para el tráfico derivado en el enlace ascendente. En cuanto a la parte de LTE, se propone un algoritmo en dos etapas de asignación de tasa de transmisión basada en pricing (con propuestas de pricing exponencial y lineal) con el objetivo de satisfacer el enlace ascendente de los usuarios en LTE. También se contempla la existencia de usuarios maliciosos, que pretenden utilizar el ancho de banda WiFi contra sus iguales para transmitir menos datos a través del enlace ascendente de LTE (menos eficiente energéticamente). Para ello se propone un método basado en la reputación que combate el funcionamiento egoísta (selfish).Postprint (published version

Enhancing cooperation in wireless networks using different concepts of game theory

PhDOptimizing radio resource within a network and across cooperating heterogeneous networks is the focus of this thesis. Cooperation in a multi-network environment is tackled by investigating network selection mechanisms. These play an important role in ensuring quality of service for users in a multi-network environment. Churning of mobile users from one service provider to another is already common when people change contracts and in a heterogeneous communication environment, where mobile users have freedom to choose the best wireless service-real time selection is expected to become common feature. This real time selection impacts both the technical and the economic aspects of wireless network operations. Next generation wireless networks will enable a dynamic environment whereby the nodes of the same or even different network operator can interact and cooperate to improve their performance. Cooperation has emerged as a novel communication paradigm that can yield tremendous performance gains from the physical layer all the way up to the application layer. Game theory and in particular coalitional game theory is a highly suited mathematical tool for modelling cooperation between wireless networks and is investigated in this thesis. In this thesis, the churning behaviour of wireless service users is modelled by using evolutionary game theory in the context of WLAN access points and WiMAX networks. This approach illustrates how to improve the user perceived QoS in heterogeneous networks using a two-layered optimization. The top layer views the problem of prediction of the network that would be chosen by a user where the criteria are offered bit rate, price, mobility support and reputation. At the second level, conditional on the strategies chosen by the users, the network provider hypothetically, reconfigures the network, subject to the network constraints of bandwidth and acceptable SNR and optimizes the network coverage to support users who would otherwise not be serviced adequately. This forms an iterative cycle until a solution that optimizes the user satisfaction subject to the adjustments that the network provider can make to mitigate the binding constraints, is found and applied to the real network. The evolutionary equilibrium, which is used to 3 compute the average number of users choosing each wireless service, is taken as the solution. This thesis also proposes a fair and practical cooperation framework in which the base stations belonging to the same network provider cooperate, to serve each other‘s customers. How this cooperation can potentially increase their aggregate payoffs through efficient utilization of resources is shown for the case of dynamic frequency allocation. This cooperation framework needs to intelligently determine the cooperating partner and provide a rational basis for sharing aggregate payoff between the cooperative partners for the stability of the coalition. The optimum cooperation strategy, which involves the allocations of the channels to mobile customers, can be obtained as solutions of linear programming optimizations