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

Basics of coalitional games with applications to communications and networking

Game theory is the study of decision making in an interactive environment. Coalitional games fulfill the promise of group efficient solutions to problems involving strategic actions. Formulation of optimal player behavior is a fundamental element in this theory. This paper comprises a self-instructive didactic means to study basics of coalitional games indicating how coalitional game theory tools can provide a framework to tackle different problems in communications and networking. We show that coalitional game approaches achieve an improved performance compare to non-cooperative game theoretical solutions

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

Resource allocation in networks via coalitional games

The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.  Software Engineering (Software)Algorithms and the Foundations of Software technolog

A Comprehensive Survey on Networking over TV White Spaces

The 2008 Federal Communication Commission (FCC) ruling in the United States opened up new opportunities for unlicensed operation in the TV white space spectrum. Networking protocols over the TV white spaces promise to subdue the shortcomings of existing short-range multi-hop wireless architectures and protocols by offering more availability, wider bandwidth, and longer-range communication. The TV white space protocols are the enabling technologies for sensing and monitoring, Internet-of-Things (IoT), wireless broadband access, real-time, smart and connected community, and smart utility applications. In this paper, we perform a retrospective review of the protocols that have been built over the last decade and also the new challenges and the directions for future work. To the best of our knowledge, this is the first comprehensive survey to present and compare existing networking protocols over the TV white spaces.Comment: 19 page

Game Theoretical Approaches for Wireless Networks

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김성철.In this dissertation, I introduce three algorithms, which are connectivity reconstruction game (CRG), adaptive sector coloring game (ASCG), and asymmetric transmission game (ATG), by mainly using supermodular game and exact potential game with considerations of various objectives (e.g., energy consumption and interference management) in wireless sensor and cellular networks. My main contributions are threefold: 1) connectivity relaxation (energy saving) in wireless localization2) intercell interference coordination in wireless cellular networks3) interference minimization in wireless ad-hoc relay networks. The corresponding explanations are as follows. 1) In geographically dense and energy limited wireless sensor networks, connectivity based localization with full power transmission can be inefficient in terms of energy consumption. In this work, I propose a distributed power control based connectivity reconstruction game, which takes into considerations of both energy efficiency and the quality of localization. The proposed scheme results in a better performance with an improved 61.9% reduction in energy consumption while maintaining the performance of localization at a level similar to the conventional algorithm with full power transmission. 2) Inter-cell interference coordination (ICIC) is a promising technique to improve the performance of frequency-domain packet scheduling (FDPS) in downlink LTE/LTEA networks. However, it is difficult to maximize the performance of FDPS using static ICIC schemes because of insufficient consideration of signal-to-interference-and-noise ratio (SINR) distribution and user fairness. On the other hand, dynamic ICIC schemes based on channel state information (CSI) also have difficulty presented in the excessive signaling overhead and X2 interface latency. In order to overcome these drawbacks, I introduce a new concept of ICIC problem based on geometric network information (GNI) and propose an ASCG as a decentralized solution of the GNI based ICIC problem. Furthermore, I develop an ASCG with a dominant strategy space noted as ASCGD to secure a stable solution through proving the existence of Nash equilibrium (NE). The proposed scheme provides better performance in terms of system throughput gain of up to about 44.1%, and especially of up to about 221% for the worst 10% users than static ICIC schemes. Moreover, the performance of the CSI based ICIC, which require too much computational load and signaling overhead, is only 13.0% and 5.6% higher than that of ASCG-D regarding the total user throughput and the worst 10% user throughput, respectively. The most interesting outcome is that the signaling overhead of ASCG-D is 1/144 of dynamic ICIC schemes one. 3) In this work, I introduce the new concept of temporal diversity utilization based on asymmetric transmission to minimize network interference in wireless ad-hoc networks with a two-hop half-duplex relaying (HDR) protocol. Asymmetric transmission is an interference-aware backoff technique, in which each communication session (source-relay-destination link) adaptively chooses a certain subset of spectrallyorthogonal data streaming which should be delayed by the duration of one time-slot (i.e., half of one subframe). I design the problem in the HDR scenario by applying the concept of asymmetric transmission, and evaluate the game-theoretical algorithm, called ATG, to derive the suboptimal solution. I show that ATG is an exact potential game, and derive its convergence and optimality properties. Furthermore, I develop an approximated version of ATG (termed A-ATG) in order to reduce signaling and computational complexity. Numerical results verify that two algorithms proposed showsignificant synergistic effects when collaborating with the conventional methods in terms of interference coordination. Ultimately, the energy consumption to satisfy the rate requirement is reduced by up to 17:4% compared to the conventional schemes alone.1 INTRODUCTION 1 1.1 Application of Supermodular Game for Connectivity Relaxation in Wireless Localization 2 1.2 Application of Exact Potential Game for Effective Inter-Cell Interference Coordination in Wireless Cellular Networks 3 1.3 Application of Exact Potential Game for Interference Minimization in Wireless Ad-hoc Relay Networks 7 1.4 Dissertation Outline 11 2 APPLICATION OF SUPERMODULAR GAME: Distributed Power Control based Connectivity Reconstruction Game inWireless Localization 13 2.1 Brief Introduction 13 2.2 System Model 13 2.3 Proposed Power Control Algorithm 14 2.3.1 Reliability Function 14 2.3.2 Game Formulation 15 2.3.3 Convergence Properties of CRG 17 2.4 Simulation Results 20 3 APPLICATION OF EXACT POTENTIAL GAME: Adaptive Sector Coloring Game for Geometric Network Information based Inter-Cell Interference Coordination in Wireless Cellular Networks 24 3.1 Brief Introduction 24 3.2 Network Model 26 3.2.1 System Preliminaries 26 3.2.2 Determination of Time Policy 27 3.2.3 Two-Stage Framework of RB Allocation 27 3.3 PROBLEM FORMULATION: Geometric Network Information based ICIC 28 3.3.1 Outline 28 3.3.2 What Is the GNI 28 3.3.3 Temporal Perspective: Why GNI 29 3.3.4 Spatial Perspective: How do I Design a Suitable Utility Function 29 3.3.5 GNI based ICIC Problem 33 3.4 ADAPTIVE SECTOR COLORING GAME 33 3.4.1 Design of ASCG 33 3.4.2 ASCG with a Dominant Strategy Space 35 3.4.3 Summary of System Operation 40 3.5 PERFORMANCE EVALUATION 41 3.5.1 Simulation Settings and Baselines for Comparison 41 3.5.2 SINR Distribution and Average User Throughput 43 3.5.3 Signaling Overhead for ICIC and FDPS 47 3.5.4 Reduction of Feasible ASCG Strategy Space 49 4 APPLICATION OF EXACT POTENTIAL GAME: Asymmetric Transmission Game for Interference Coordination in Wireless Ad-hoc Relay Networks 51 4.1 Brief Introduction 51 4.2 Problem Formulation 52 4.2.1 System Preliminaries 52 4.2.2 The Concept of Asymmetric Transmission for Interference Coordination: A Simple Example 53 4.2.3 Optimization Problem 54 4.3 Asymmetric Transmission Game 55 4.3.1 Game Formulation 55 4.3.2 Convergence and Optimality Properties of Asymmetric Transmission Game 55 4.3.3 Approximated Version of Asymmetric Transmission Game . . 58 4.4 Simulation Results 61 4.4.1 Parameters Settings 61 4.4.2 Network Interference in One-shot Game 62 4.4.3 Individual Power Consumption in One-shot Game 66 4.4.4 Total Energy Consumption in 1000-shot Games 70 4.4.5 Complexity Analysis for Varying K and M 71 5 CONCLUSION 74 Appendix A Derivation of number of partitions for extracting the dominant feasible strategy set 76 Appendix B Derivation of the cardinal number of the dominant feasible strategy set 78 Appendix C Existence of NE in ASCG-D 79 Appendix D The Required Signaling overhead of ASCG-D 82 Bibliography 83 Abstract (In Korean) 93Docto

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

D13.1 Fundamental issues on energy- and bandwidth-efficient communications and networking

Deliverable D13.1 del projecte europeu NEWCOM#The report presents the current status in the research area of energy- and bandwidth-efficient communications and networking and highlights the fundamental issues still open for further investigation. Furthermore, the report presents the Joint Research Activities (JRAs) which will be performed within WP1.3. For each activity there is the description, the identification of the adherence with the identified fundamental open issues, a presentation of the initial results, and a roadmap for the planned joint research work in each topic.Preprin