Stability and Distributed Power Control in MANETs with Outages and Retransmissions

In the current work the effects of hop-by-hop packet loss and retransmissions via ARQ protocols are investigated within a Mobile Ad-hoc NET-work (MANET). Errors occur due to outages and a success probability function is related to each link, which can be controlled by power and rate allocation. We first derive the expression for the network's capacity region, where the success function plays a critical role. Properties of the latter as well as the related maximum goodput function are presented and proved. A Network Utility Maximization problem (NUM) with stability constraints is further formulated which decomposes into (a) the input rate control problem and (b) the scheduling problem. Under certain assumptions problem (b) is relaxed to a weighted sum maximization problem with number of summants equal to the number of nodes. This further allows the formulation of a non-cooperative game where each node decides independently over its transmitting power through a chosen link. Use of supermodular game theory suggests a price based algorithm that converges to a power allocation satisfying the necessary optimality conditions of (b). Implementation issues are considered so that minimum information exchange between interfering nodes is required. Simulations illustrate that the suggested algorithm brings near optimal results.Comment: 25 pages, 6 figures, 1 table, submitted to the IEEE Trans. on Communication

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

Learning for Cross-layer Resource Allocation in the Framework of Cognitive Wireless Networks

The framework of cognitive wireless networks is expected to endow wireless devices with a cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In this dissertation, we focus on the problem of developing cognitive network control mechanisms without knowing in advance an accurate network model. We study a series of cross-layer resource allocation problems in cognitive wireless networks. Based on model-free learning, optimization and game theory, we propose a framework of self-organized, adaptive strategy learning for wireless devices to (implicitly) build the understanding of the network dynamics through trial-and-error. The work of this dissertation is divided into three parts. In the first part, we investigate a distributed, single-agent decision-making problem for real-time video streaming over a time-varying wireless channel between a single pair of transmitter and receiver. By modeling the joint source-channel resource allocation process for video streaming as a constrained Markov decision process, we propose a reinforcement learning scheme to search for the optimal transmission policy without the need to know in advance the details of network dynamics. In the second part of this work, we extend our study from the single-agent to a multi-agent decision-making scenario, and study the energy-efficient power allocation problems in a two-tier, underlay heterogeneous network and in a self-sustainable green network. For the heterogeneous network, we propose a stochastic learning algorithm based on repeated games to allow individual macro- or femto-users to find a Stackelberg equilibrium without flooding the network with local action information. For the self-sustainable green network, we propose a combinatorial auction mechanism that allows mobile stations to adaptively choose the optimal base station and sub-carrier group for transmission only from local payoff and transmission strategy information. In the third part of this work, we study a cross-layer routing problem in an interweaved Cognitive Radio Network (CRN), where an accurate network model is not available and the secondary users that are distributed within the CRN only have access to local action/utility information. In order to develop a spectrum-aware routing mechanism that is robust against potential insider attackers, we model the uncoordinated interaction between CRN nodes in the dynamic wireless environment as a stochastic game. Through decomposition of the stochastic routing game, we propose two stochastic learning algorithm based on a group of repeated stage games for the secondary users to learn the best-response strategies without the need of information flooding

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

Optimization of positioning capabilities in wireless sensor networks : from power efficiency to medium access

In Wireless Sensor Networks (WSN), the ability of sensor nodes to know its position is an enabler for a wide variety of applications for monitoring, control, and automation. Often, sensor data is meaningful only if its position can be determined. Many WSN are deployed indoors or in areas where Global Navigation Satellite System (GNSS) signal coverage is not available, and thus GNSS positioning cannot be guaranteed. In these scenarios, WSN may be relied upon to achieve a satisfactory degree of positioning accuracy. Typically, batteries power sensor nodes in WSN. These batteries are costly to replace. Therefore, power consumption is an important aspect, being performance and lifetime of WSN strongly relying on the ability to reduce it. It is crucial to design effective strategies to maximize battery lifetime. Optimization of power consumption can be made at different layers. For example, at the physical layer, power control and resource optimization may play an important role, as well as at higher layers through network topology and MAC protocols. The objective of this Thesis is to study the optimization of resources in WSN that are employed for positioning purposes, with the ultimate goal being the minimization of power consumption. We focus on anchor-based positioning, where a subset of the WSN nodes know their location (anchors) and send ranging signals to nodes with unknown position (targets) to assist them in estimating it through distance-related measurements. Two well known of such measurements are received signal strength (RSS) and time of arrival (TOA), in which this Thesis focuses. In order to minimize power consumption while providing a certain quality of positioning service, in this dissertation we research on the problems of power control and node selection. Aiming at a distributed implementation of the proposed techniques, we resort to the tools of non-cooperative game theory. First, transmit power allocation is addressed for RSS based ranging. Using game theory formulation, we develop a potential game leading to an iterated best response algorithm with sure convergence. As a performance metric, we introduce the geometric dilution of precision (GDOP), which is shown to help achieving a suitable geometry of the selected anchor nodes. The proposed scheme and relative distributed algorithms provide good equilibrium performance in both static and dynamic scenarios. Moreover, we present a distributed, low complexity implementation and analyze it in terms of computational complexity. Results show that performance close to that of exhaustive search is possible. We then address the transmit power allocation problem for TOA based ranging, also resorting to a game theoretic formulation. In this setup, and also considering GDOP as performance metric, a supermodular game formulation is proposed, along with a distributed algorithm with guaranteed convergence to a unique solution, based on iterated best response. We analyze the proposed algorithm in terms of the price of anarchy (PoA), that is, compared to a centralized optimum solution, and shown to have a moderate performance loss. Finally, this dissertation addresses the effect of different MAC protocols and topologies in the positioning performance. In this direction, we study the performance of mesh and cluster-tree topologies defined in WSN standards. Different topologies place different constraints in network connectivity, having a substantial impact on the performance of positioning algorithms. While mesh topology allows high connectivity with large energy consumption, cluster-tree topologies are more energy efficient but suffer from reduced connectivity and poor positioning performance. In order to improve the performance of cluster-tree topologies, we propose a cluster formation algorithm. It significantly improves connectivity with anchor nodes, achieving vastly improved positioning performance.En les xarxes de sensors sense fils (WSN), l'habilitat dels nodes sensors per conèixer la seva posició facilita una gran varietat d'aplicacions per la monitorització, el control i l'automatització. Així, les dades que proporciona un sensor tenen sentit només si la posició pot ésser determinada. Moltes WSN són desplegades en interiors o en àrees on la senyal de sistemes globals de navegació per satèl.lit (GNSS) no té prou cobertura, i per tant, el posicionament basat en GNSS no pot ésser garantitzat. En aquests escenaris, les WSN poden proporcionar una bona precisió en posicionament. Normalment, en WSN els nodes són alimentats amb bateries. Aquestes bateries són difícils de reemplaçar. Per tant, el consum de potència és un aspecte important i és crucial dissenyar estratègies efectives per maximitzar el temps de vida de la bateria. L'optimització del consum de potència pot ser fet a diferents capes del protocol. Per exemple, en la capa física, el control de potència i l'optimització dels recursos juguen un rol important, igualment que la topologia de xarxa i els protocols MAC en les capes més altes. L'objectiu d'aquesta tesi és estudiar l¿optimització de recursos en WSN que s'utilitzen per fer posicionament, amb el propòsit de minimitzar el consum de potència. Ens focalitzem en el posicionament basat en àncora, en el qual un conjunt de nodes coneixen la seva localització (nodes àncora) i envien missatges als nodes que no saben la seva posició per ajudar-los a estimar les seves coordenades amb mesures de distància. Dues classes de mesures són la potència de la senyal rebuda (RSS) i el temps d'arribada (TOA) en les quals aquesta tesi està focalitzada. Per minimitzar el consum de potència mentre que es proporciona suficient qualitat en el posicionament, en aquesta tesi estudiem els problemes de control de potència i selecció de nodes. Tenint en compte una implementació distribuïda de les tècniques proposades, utilitzem eïnes de teoria de jocs no cooperatius. Primer, l'assignació de potència transmesa és abordada pel càlcul de la distància amb RSS. Utilitzant la teoria de jocs, desenvolupem un joc potencial que convergeix amb un algoritme iteratiu basat en millor resposta (best response). Com a mètrica d'error, introduïm la dilució de la precisió geomètrica (GDOP) que mostra quant d'apropiada és la geometria dels nodes àncora seleccionats. L'esquema proposat i els algoritmes distribuïts proporcionen una bona resolució de l'equilibri en l'escenari estàtic i dinàmic. Altrament, presentem una implementació distribuïda i analitzem la seva complexitat computacional. Els resultats obtinguts són similars als obtinguts amb un algoritme de cerca exhaustiva. El problema d'assignació de la potència transmesa en el càlcul de la distància basat en TOA, també és tractat amb teoria de jocs. En aquest cas, considerant el GDOP com a mètrica d'error, proposem un joc supermodular juntament amb un algoritme distribuït basat en millor resposta amb convergència garantida cap a una única solució. Analitzem la solució proposada amb el preu de l'anarquia (PoA), és a dir, es compara la nostra solució amb una solució òptima centralitzada mostrant que les pèrdues són moderades. Finalment, aquesta tesi tracta l'efecte que causen diferents protocols MAC i topologies en el posicionament. En aquesta direcció, estudiem les topologies de malla i arbre formant clusters (cluster-tree) que estan definides als estàndards de les WSN. La diferència entre les topologies crea diferents restriccions en la connectivitat de la xarxa, afectant els resultats de posicionament. La topologia de malla permet una elevada connectivitat entre els nodes amb gran consum d'energia, mentre que les topologies d'arbre són més energèticament eficients però amb baixa connectivitat entre els nodes i baix rendiment pel posicionament. Per millorar la qualitat del posicionament en les topologies d'arbre, proposem un algoritme de formació de clústers.Postprint (published version

Methodologies for Analyzing Equilibria in Wireless Games

Under certain assumptions in terms of information and models, equilibria correspond to possible stable outcomes in conflicting or cooperative scenarios where rational entities interact. For wireless engineers, it is of paramount importance to be able to predict and even ensure such states at which the network will effectively operate. In this article, we provide non-exhaustive methodologies for characterizing equilibria in wireless games in terms of existence, uniqueness, selection, and efficiency.Comment: To appear in IEEE Signal Processing Magazine, Sep. 200

Cores of Cooperative Games in Information Theory

Cores of cooperative games are ubiquitous in information theory, and arise most frequently in the characterization of fundamental limits in various scenarios involving multiple users. Examples include classical settings in network information theory such as Slepian-Wolf source coding and multiple access channels, classical settings in statistics such as robust hypothesis testing, and new settings at the intersection of networking and statistics such as distributed estimation problems for sensor networks. Cooperative game theory allows one to understand aspects of all of these problems from a fresh and unifying perspective that treats users as players in a game, sometimes leading to new insights. At the heart of these analyses are fundamental dualities that have been long studied in the context of cooperative games; for information theoretic purposes, these are dualities between information inequalities on the one hand and properties of rate, capacity or other resource allocation regions on the other.Comment: 12 pages, published at http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/318704 in EURASIP Journal on Wireless Communications and Networking, Special Issue on "Theory and Applications in Multiuser/Multiterminal Communications", April 200