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Algorithm Design and Analysis in Wireless Networks Lin Chen Algorithm design and analysis in wireless networks Lin Chen To cite this version: Lin Chen. Algorithm design and analysis in wireless networks. Data Structures and Algorithms [cs.DS]. Paris-Sud XI, 2017. tel-01688436 HAL Id: tel-01688436 https://hal.archives-ouvertes.fr/tel-01688436 Submitted on 24 Jan 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Algorithm Design and Analysis in Wireless Networks Habilitation Thesis Lin CHEN Laboratoire de Recherche en Informatique (UMR 8623) Université Paris-Sud Defended on July 11, 2017 Committee M. Tamer BASAR Reviewer M. Pierre FRAIGNIAUD Reviewer M. Bruno GAUJAL Reviewer M. Eitan ALTMAN Examiner M. Joffroy BEAUQUIER Examiner Mme. Johanne COHEN Examiner M. Mérouane DEBBAH Examiner M. Fabio MARTIGNON Examiner Abstract Algorithms are perhaps the most fundamental and fascinating elements in computer science as a whole. Networks and networked systems are no exception. This habilitation thesis summarizes my research during the last eight years on some algorithmic problems of both fundamental and practical importance in modern networks and networked systems, more specifically, wireless net- works. Generically, wireless networks have a number of common features which form a common ground on which algorithms for wireless networks are designed. These features include the lack of network-wide coordination, large number of nodes, limited energy and computation resource, and the unreliable wireless links. These constraints and considerations make the algorithmic study for wireless networks an emerging research field requiring new tools and methodologies, some of which cannot be drawn from existing state-of-the-art research in either algorithm or networking community. Motivated by this observation, we aim at making a tiny while systematic step forwards in the design and analysis of algorithms that can scale elegantly, act efficiently in terms of computation and communication, while keeping operations as local and distributed as possible. Specifically, we expose our works on a number of algorithmic problems in emerging wireless networks that are simple to state and intuitively understandable, while of both fundamental and practical importance, and require non-trivial efforts to solve. These problems include (1) channel rendezvous and neigh- bor discovery, (2) opportunistic channel access, (3) distributed learning, (4) path optimization and scheduling, (5) algorithm design and analysis in radio-frequency identification systems. Methodologically, most of our analysis is systematically articulated as follows. • Theoretical performance bound. After formulating the target problem, we analytically char- acterize the performance of the optimal solution as well as some natural and intuitive al- gorithms in some cases. These results usually give us pertinent insights on the structural properties of the problem including the theoretical limit and the performance gap between the limit and any algorithm that is not carefully devised. • Optimum or approximation algorithm design. Guided by the theoretical results estab- lished in the first step, we then direct our efforts to the design and analysis of efficient algorithms for the target problem. By efficient we mean that our algorithms produce either the optimum solution, or, in case where the problem is NP-hard, constant-factor or logarithmic approximations in polynomial or quasi-polynomial time. • Further extension and generalization. Once we have established a complete framework solving or approximately solving the problem, we further analyze the lessons that can be learnt from the analysis process and demonstrate how our framework can be extended or adapted to address a generic class of problems in a wider range of applications presenting similar structural properties. 1 Contents Contents Abstract 1 1 Introduction 6 1.1 Background and Motivation ............................... 6 1.2 Thesis Methodology ................................... 7 1.3 Overview of Major Results and Thesis Organization .................. 8 2 Channel Rendezvous and Neighbor Discovery 12 2.1 Introduction ........................................ 12 2.1.1 Channel Rendezvous ............................... 12 2.1.2 Neighbor Discovery ................................ 13 2.1.3 Chapter Organization .............................. 14 2.2 The Generalized Telephone Problem .......................... 14 2.2.1 Introduction .................................... 14 2.2.2 Related Work ................................... 16 2.2.3 Problem Formulation ............................... 17 2.2.4 Theoretic Performance Bound .......................... 19 2.2.5 An Order-optimum Deterministic Algorithm . 20 2.2.6 Theoretic Performance Bound for Probabilistic Strategies . 22 2.2.7 Variants and Extensions ............................. 26 2.3 Neighbor Discovery .................................... 27 2.3.1 Context and Motivation ............................. 27 2.3.2 Related Work ................................... 28 2.3.3 Multi-channel Neighbor Discovery ....................... 29 2.3.4 Algorithm-independent Discovery Delay Bound . 31 2.3.5 MCD: Single-channel Case ............................ 32 2.3.6 MCD: Multi-channel Case ............................ 34 2.4 Summary of Other Contributions ............................ 36 2.4.1 From Single- to Multi-radio Channel Rendezvous . 36 2.4.2 Cooperative Channel Rendezvous ........................ 37 2.4.3 Oblivious Neighbor Discovery with Directional Antennas . 38 2.5 Conclusion and Perspectives ............................... 38 3 Opportunistic Channel Access: A Restless Multi-Armed Bandit Perspective 40 3.1 Introduction ........................................ 40 3.1.1 Optimality of Myopic Sensing Policy ...................... 40 3.1.2 Beyond Myopic Sensing: a Heuristic ν-step Lookahead Policy . 41 2 Contents 3.1.3 Chapter Organization .............................. 42 3.2 RMAB and its Application ................................ 42 3.2.1 MAB and Gittins Index .............................. 42 3.2.2 RMAB and Whittle Index ............................. 43 3.2.3 Application of RMAB ............................... 44 3.2.4 Non-Bayesian MAB ................................ 44 3.3 Optimality of Myopic Sensing Policy .......................... 45 3.3.1 Problem Formulation ............................... 45 3.3.2 Axioms ...................................... 48 3.3.3 Optimality of Myopic Sensing Policy ...................... 49 3.3.4 Discussion ..................................... 51 3.4 Beyond Myopic Policy: ν-step Lookahead Policy .................... 52 3.4.1 Problem Formulation ............................... 52 3.4.2 When Stop Sensing: the ν-step Lookahead Policy . 55 3.4.3 One-step Lookahead Policy ........................... 57 3.5 Summary of Other Contributions ............................ 59 3.5.1 Myopic Policy under Multi-state Markov Channel Model . 59 3.5.2 Myopic Channel Probing Policy in Underlay Cognitive Radio Systems . 59 3.5.3 RMAB with Multiples Users and Channel Switching Cost . 60 3.6 Conclusion and Perspectives ............................... 60 4 Distributed Learning in Wireless Networks: A Game-theoretical Perspective 62 4.1 Introduction ........................................ 62 4.1.1 Imitation-based Spectrum Access ........................ 63 4.1.2 Retrospective Spectrum Access ......................... 64 4.1.3 Chapter Organization .............................. 65 4.2 Imitation-based Spectrum Access ............................ 65 4.2.1 System Model and Spectrum Access Game Formulation . 65 4.2.2 Imitation-based Spectrum Access ........................ 66 4.2.3 Imitation on the Same Channel ......................... 69 4.3 Retrospective Spectrum Access ............................. 72 4.3.1 System Model and Game Formulation ..................... 73 4.3.2 Retrospective Spectrum Access ......................... 74 4.3.3 Convergence Analysis .............................. 75 4.4 Summary of Other Contributions ............................ 79 4.4.1 Joint Operator Pricing and Network Selection in CRNs . 79 4.4.2 Distributed Spectrum Management in TV White Space . 80 4.4.3 Load Balancing in Smart Grids ......................... 80 4.5 Conclusion and Perspectives ............................... 81 5 Data Harvesting and Charging Path Optimization 82 5.1 Introduction ........................................ 82 5.1.1 Time-constrained Data Harvesting in Wireless Sensor Networks . 82 5.1.2 Charging Path Optimization and Scheduling in Mobile Networks . 83 5.1.3 Chapter Organization .............................. 84 5.2 Time-constrained Data Harvesting in Wireless Sensor Networks . 84 5.2.1 Background and Related Work ......................... 84 3 Contents 5.2.2 Time-constrained Data Harvesting Problem . 85 5.2.3 Theoretical Performance Bound: Optimal and Random Algorithms . 87 5.2.4 Approximation Algorithm Design ........................ 88 5.3 Charging Path Optimization and Scheduling in Mobile Networks . 93 5.3.1 Background and Related Work ........................
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