Game Theory and Femtocell Communications: Making Network Deployment Feasible

9781466600928Femtocell is currently the most promising technology for supporting the increasing demand of data traffic in wireless networks. Femtocells provide an opportunity for enabling innovative mobile applications and services in home and office environments. Femtocell Communications and Technologies: Business Opportunities and Deployment Challenges is an extensive and thoroughly revised version of a collection of review and research based chapters on femtocell technology. This work focuses on mobility and security in femtocell, cognitive femtocell, and standardization and deployment scenarios. Several crucial topics addressed in this book are interference mitigation techniques, network integration option, cognitive optimization, and economic incentives to install femtocells that may have a larger impact on their ultimate success. The book is optimized for use by graduate researchers who are familiar with the fundamentals of wireless communication and cellular concepts

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 Management of D2D Communications in Cellular Networks

To improve the system capacity, spectral performance, and energy efficiency, stringent requirements for increasing reliability, and decreasing delays have been intended for next-generation wireless networks. Device-to-device (D2D) communication is a promising technique in the fifth-generation (5G) wireless communications to enhance spectral efficiency, reduce latency and energy efficiency. In D2D communication, two wireless devices in close proximity can communicate with each other directly without pass through the Base Station (BS) or Core Network (CN). In this proposal, we identify compromises and challenges of integrating D2D communications into cellular networks and propose potential solutions. To maximize gains from such integration, resource management, and interference avoidance are key factors. Thus, it is important to properly allocate resources to guarantee reliability, data rate, and increase the capacity in cellular networks. In this thesis, we address the problem of resource allocation in D2D communication underlaying cellular networks. We provide a detailed review of the resource allocation problem of D2D communications. My Ph.D research will tackle several issues in order to alleviate the interference caused by a D2D user-equipment (DUE) and cellular-userequipment (CUE) in uplink multi-cell networks, the intra-cell and inter-cell interference are considered in this work to improve performance for D2D communication underlaying cellular networks. The thesis consists of four main results. First, the preliminary research proposes a resource allocation scheme to formulate the resource allocation problem through optimization of the utility function, which eventually reflects the system performance concerning network throughput. The formulated optimization problem of maximizing network throughput while guaranteeing predefined service levels to cellular users is non-convex and hence intractable. Thus, the original problem is broken down into two stages. The first stage is the admission control of D2D users while the second one is the power control for each admissible D2D pair and its reuse partner. Second, we proposed a spectrum allocation framework based on Reinforcement Learning (RL) for joint mode selection, channel assignment, and power control in D2D communication. The objective is to maximize the overall throughput of the network while ensuring the quality of transmission and guaranteeing low latency requirements of D2D communications. The proposed algorithm uses reinforcement learning (RL) based on Markov Decision Process (MDP) with a proposed new reward function to learn the policy by interacting with the D2D environment. An Actor-Critic Reinforcement Learning (AC-RL) approach is then used to solve the resource management problem. The simulation results show that our learning method performs well, can greatly improve the sum rate of D2D links, and converges quickly, compared with the algorithms in the literature. Third, a joint channel assignment, power allocation and resource allocation algorithm is proposed. The algorithm designed to allow multiple DUEs to reuse the same CUE channel for D2D communications underlaying multi-cell cellular networks with the consideration of the inter-cell and intra-cell interferences. Obviously, under satisfying the QoS requirements of both DUEs and CUEs, the more the number of the allowed accessing DUEs on a single CUE channel is, the higher the spectrum efficiency is, and the higher the network throughput can be achieved. Meanwhile, implementing resource allocation strategies at D2D communications allows to effectively mitigate the interference caused by the D2D communications at both cellular and D2D users. In this part, the formulated optimization problem of maximizing network throughput while guaranteeing predefined service levels to cellular users. Therefore, we propose an algorithm that solves this nonlinear mixed-integer problem in three steps wherein the first step, subchannel assignment is carried out, the second one is the power allocation, while the third step of the proposed algorithm is the resource allocation for multiple D2D pairs based on genetic algorithm. The simulation results verify the effectiveness of our proposed algorithm. Fourth, integrating D2D communications and Femtocells in Heterogeneous Networks (HetNets) is a promising technology for future cellular networks. Which have attracted a lot of attention since it can significantly improve the capacity, energy efficiency and spectral performance of next-generation wireless networks (5G). D2D communication and femtocell are introduced as underlays to the cellular systems by reusing the cellular channels to maximize the overall throughput in the network. In this part, the problem is formulated to maximize the network throughput under the QoS constraints for CUEs, DUEs and FUEs. This problem is a mixed-integer non-linear problem that is difficult to be solved directly. To solve this problem, we propose a joint channel selection, power control, and resource allocation scheme to maximize the sum rate of the cellular network system. The simulation results show that the proposed scheme can effectively reduce the computational complexity and improve the overall system throughput compared with existing well-known methods

Leveraging Cognitive Radio Networks Using Heterogeneous Wireless Channels

The popularity of ubiquitous Internet services has spurred the fast growth of wireless communications by launching data hungry multimedia applications to mobile devices. Powered by spectrum agile cognitive radios, the newly emerged cognitive radio networks (CRN) are proposed to provision the efficient spectrum reuse to improve spectrum utilization. Unlicensed users in CRN, or secondary users (SUs), access the temporarily idle channels in a secondary and opportunistic fashion while preventing harmful interference to licensed primary users (PUs). To effectively detect and exploit the spectrum access opportunities released from a wide spectrum, the heterogeneous wireless channel characteristics and the underlying prioritized spectrum reuse features need to be considered in the protocol design and resource management schemes in CRN, which plays a critical role in unlicensed spectrum sharing among multiple users. The purpose of this dissertation is to address the challenges of utilizing heterogeneous wireless channels in CRN by its intrinsic dynamic and diverse natures, and build the efficient, scalable and, more importantly, practical dynamic spectrum access mechanisms to enable the cost-effective transmissions for unlicensed users. Note that the spectrum access opportunities exhibit the diversity in the time/frequency/space domain, secondary transmission schemes typically follow three design principles including 1) utilizing local free channels within short transmission range, 2) cooperative and opportunistic transmissions, and 3) effectively coordinating transmissions in varying bandwidth. The entire research work in this dissertation casts a systematic view to address these principles in the design of the routing protocols, medium access control (MAC) protocols and radio resource management schemes in CRN. Specifically, as spectrum access opportunities usually have small spatial footprints, SUs only communicate with the nearby nodes in a small area. Thus, multi-hop transmissions in CRN are considered in this dissertation to enable the connections between any unlicensed users in the network. CRN typically consist of intermittent links of varying bandwidth so that the decision of routing is closely related with the spectrum sensing and sharing operations in the lower layers. An efficient opportunistic cognitive routing (OCR) scheme is proposed in which the forwarding decision at each hop is made by jointly considering physical characteristics of spectrum bands and diverse activities of PUs in each single band. Such discussion on spectrum aware routing continues coupled with the sensing selection and contention among multiple relay candidates in a multi-channel multi-hop scenario. An SU selects the next hop relay and the working channel based upon location information and channel usage statistics with instant link quality feedbacks. By evaluating the performance of the routing protocol and the joint channel and route selection algorithm with extensive simulations, we determine the optimal channel and relay combination with reduced searching complexity and improved spectrum utilization. Besides, we investigate the medium access control (MAC) protocol design in support of multimedia applications in CRN. To satisfy the quality of service (QoS) requirements of heterogeneous applications for SUs, such as voice, video, and data, channels are selected to probe for appropriate spectrum opportunities based on the characteristics and QoS demands of the traffic along with the statistics of channel usage patterns. We propose a QoS-aware MAC protocol for multi-channel single hop scenario where each single SU distributedly determines a set of channels for sensing and data transmission to satisfy QoS requirements. By analytical model and simulations, we determine the service differentiation parameters to provision multiple levels of QoS. We further extend our discussion of dynamic resource management to a more practical deployment case. We apply the experiences and skills learnt from cognitive radio study to cellular communications. In heterogeneous cellular networks, small cells are deployed in macrocells to enhance link quality, extend network coverage and offload traffic. As different cells focus on their own operation utilities, the optimization of the total system performance can be analogue to the game between PUs and SUs in CRN. However, there are unique challenges and operation features in such case. We first present challenging issues including interference management, network coordination, and interworking between cells in a tiered cellular infrastructure. We then propose an adaptive resource management framework to improve spectrum utilization and mitigate the co-channel interference between macrocells and small cells. A game-theory-based approach is introduced to handle power control issues under constrained control bandwidth and limited end user capability. The inter-cell interference is mitigated based upon orthogonal transmissions and strict protection for macrocell users. The research results in the dissertation can provide insightful lights on flexible network deployment and dynamic spectrum access for prioritized spectrum reuse in modern wireless systems. The protocols and algorithms developed in each topic, respectively, have shown practical and efficient solutions to build and optimize CRN

Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version

Mobility and resource management for 5G heterogeneous networks

The conventional topology of current cellular networks is a star structure, where central control points usually serve as base stations (BSs). This provides the advantage of simplicity while still providing quality of service (QoS). For next-generation networks, however, this topology is disadvantageous and difficult to use due to the insufficient availability of network access. The hybrid topology radio network will thus naturally be the future mobile access network that can help to overcome current and future challenges efficiently. Therefore, relay technology can play an important role in a hybrid cellular network topology. Today, with the recent long-term evolution-advanced (LTE-A) standards, the 3rd Generation Partnership Project (3GPP) supports a single-hop relay technology in which the radio access link between the BS and users is relayed by only one relay station (RS). With the help of multi-hop relay, however, the radio link between the BS and users can be extended to more than two hops to improve the coverage and network capacity. Multiple hops to transmit data to and from the corresponding BS results in the reduction of path loss. However, using a multi-hop relay system requires more radio resources to transmit data through different hops. More interference is also created due to a greater number of simultaneous transmissions in the network. New mobility and resource management schemes are thus important for achieving a high QoS while increasing the whole network capacity. In the first part, the problem of relay selection and radio resource allocation is studied, and choosing how the bandwidth should be shared between direct, backhaul, and access links in multi-hop relay networks is discussed. In such a network, resource allocation plays a critical role because it manages channel access in both time and frequency domains and determines how resources are allocated for different links. The proposed solution includes a nonlinear programming technique and a heuristic method. First, the problem formulation of resource allocation and relay selection is presented to provide an integrated framework for multi-hop relay networks. Second, an analytical solution to the problem is presented using a nonlinear programming technique. Finally, an iterative two-stage algorithm is presented to address the joint resource allocation and relay selection problem in multi-hop relay networks Under backhaul and capacity limitation constraints. In particular, the first stage proposed a fast approximation analytical solution for a resource allocation algorithm that takes into account the trade-off between the optimality and the complexity of the multi-hop relay architecture; the second stage presented a heuristic relay selection strategy that considers the RS load and helps to keep the relay from being overloaded is proposed. In the second part, the mobility problem in downlink multi-hop relay networks is addressed. In addition to the resource allocation issue, the relay selection problem is studied from a network layer perspective. Therefore, this part includes the issue of radio path selection. As an alternative to the heuristic algorithm developed in the previous part, the presented work describes the development and evaluation of a relay-selection scheme based on a Markov decision process (MDP) that considers the RS load and the existing radio-link path to improve handoff performance. First, the problem formulation of resource allocation and relay selection is presented. Second, an MDP mathematical model is developed to solve the relay selection problem in a decentralized way and to make the selection process simple. This relay selection scheme has the objective of maintaining the throughput and ensuring seamless mobility and service continuity to all mobile terminals while reducing the handoff frequency and improving handoff performance. In the third part, the admission and power control problem of a general heterogeneous network (HetNet) consisting of several small cells (SCs) is solved. Compared to the first two parts of this work, the system is expanded from a multi-hop RS to a general SC context. This part therefore focuses only on the access link problem, assuming the capacity of the SC backhaul links are large enough not to be bottlenecks. This part mainly deals with the problem of how to maximize the number of admitted users in an overloaded system while minimizing the transmit power given a certain QoS level. First, the problem is formulated to address concerns about QoS requirements in a better way. Second, a Voronoi-based user association scheme for maximizing the number of admitted users in the system under QoS and capacity limitation constraints is proposed to find near-optimal solutions. Finally, a twostage algorithm is presented to address the joint admission and power control problem in a downlink heterogeneous SC network. In particular, the first stage proposes a dynamic call admission control policy that considers the SC load and call-level QoS while also helping to keep the system from being overloaded. The second stage presents an adaptive power allocation strategy that considers both user distribution and the density of SCs in HetNets. Finally, the proposed solutions are evaluated using extensive numerical simulations, and the numerical results are presented to provide a comparison with related works found in the literature

Resources Optimization For Distributed Mobile Platforms In Smart Cities

This thesis is focused on the study and design of techniques able to optimize resources in distributed mobile platforms. It is related to a smart city environment, in order to enhance quality, performance and interactivity of urban services. The subject is the computation offloading, intended as the delegation of certain computing tasks to an external platform, such as a cloud or a cluster of devices. Offloading the computation tasks can effectively expand the usability of mobile devices beyond their physical limits and may be necessary due to limitations of a system handling a particular task on its own. The computation offloading within an ecosystem as a urban community, where a large amount of users are connected towards even multiple devices, is a challenging subject. In a very close future, smart cities will be peculiar sources of intensive computing tasks, since they are conceived as systems where e-governance will be not only transparent and fast, but also oriented to energy and water conservation, efficient waste disposal, city automation, seamless facilities to travel and affordable access to health management systems. Also traffic will need to be monitored intelligently, emergencies foreseen and resolved quickly, homes and citizens provided with a wide series of control and security devices. All these ambitious aspirations will require the deployment of infrastructures and systems where devices will generate massive data and should be orchestrated in a collective way. In this context, the computation offloading is an operation dealing with the optimization of urban services, in order to reduce costs and consumption of resources and to improve the connection between citizens and government. This dissertation is organized in three main parts, dealing with the optimization of the resources in a smart city from different points of view

Research on efficiency and privacy issues in wireless communication

Wireless spectrum is a limited resource that must be used efficiently. It is also a broadcast medium, hence, additional procedures are required to maintain communication over the wireless spectrum private. In this thesis, we investigate three key issues related to efficient use and privacy of wireless spectrum use. First, we propose GAVEL, a truthful short-term auction mechanism that enables efficient use of the wireless spectrum through the licensed shared access model. Second, we propose CPRecycle, an improved Orthogonal Frequency Division Multiplexing (OFDM) receiver that retrieves useful information from the cyclic prefix for interference mitigation thus improving spectral efficiency. Third and finally, we propose WiFi Glass, an attack vector on home WiFi networks to infer private information about home occupants. First we consider, spectrum auctions. Existing short-term spectrum auctions do not satisfy all the features required for a heterogeneous spectrum market. We discover that this is due to the underlying auction format, the sealed bid auction. We propose GAVEL, a truthful auction mechanism, that is based on the ascending bid auction format, that avoids the pitfalls of existing auction mechanisms that are based on the sealed bid auction format. Using extensive simulations we observe that GAVEL can achieve better performance than existing mechanisms. Second, we study the use of cyclic prefix in Orthogonal Frequency Division Multiplexing. The cyclic prefix does contain useful information in the presence of interference. We discover that while the signal of interest is redundant in the cyclic prefix, the interference component varies significantly. We use this insight to design CPRecycle, an improved OFDM receiver that is capable of using the information in the cyclic prefix to mitigate various types of interference. It improves spectral efficiency by decoding packets in the presence of interference. CPRecycle require changes to the OFDM receiver and can be deployed in most networks today. Finally, home WiFi networks are considered private when encryption is enabled using WPA2. However, experiments conducted in real homes, show that the wireless activity on the home network can be used to infer occupancy and activity states such as sleeping and watching television. With this insight, we propose WiFi Glass, an attack vector that can be used to infer occupancy and activity states (limited to three activity classes), using only the passively sniffed WiFi signal from the home environment. Evaluation with real data shows that in most of the cases, only about 15 minutes of sniffed WiFi signal is required to infer private information, highlighting the need for countermeasures