Network Selection and Resource Allocation Games for Wireless Access Networks

Malanchini, Ilaria; Cesana, Matteo; Gatti, Nicola

doi:10.1109/tmc.2012.207

Cited by 72 publications

(41 citation statements)

References 45 publications

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“…Other problems that have been formulated in terms of potential games are found in routing [9], [97], [156], [157], [186], [203], BS/AP selection [98], [99], [112], [161], [172], cooperative transmissions [4], [139], secrecy rate maximization [11], code design for radar [146], broadcasting [35], spectrum market [87], network coding [115], [147], [148], data cashing [94], social networks [40], computation offloading [42], localization [79], and demand-side management in smart grids [77], [183]. …”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Yamamoto

2015

IEICE Trans. Commun.

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SUMMARY Potential games form a class of non-cooperative games where the convergent of unilateral improvement dynamics is guaranteed in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed. key words: potential game, game theory, radio resource management, channel assignment, transmission power control IntroductionThe broadcast nature of wireless transmissions causes cochannel interference and channel contention, which can be viewed as interactions among transceivers. Interactions among multiple decision makers can be formulated and analyzed using a branch of applied mathematics called game theory [61], [131]. Game-theoretic approaches have been applied to a wide range of wireless communication technologies, including transmission power control for code division multiple access (CDMA) cellular systems [153] and cognitive radios [132]. For a summary of game-theoretic approaches to wireless networks, we refer the interested reader to [68] [189].In this paper, we focus on potential games [126], which form a class of strategic form games with the following desirable properties:• The existence of a Nash equilibrium in potential games is guaranteed in many practical situations [126] (Theorems 1 and 2 in this paper), but is not guaranteed for general strategic form games. Example 2 in Sect. 2. We provide an overview of problems in wireless networks that can be formulated in terms of potential games. We also clarify the relations among games, and provide simpler proofs of some known results. Problem-specific learning algorithms [92], [168] are beyond the scope of this paper.The remainder of this paper is organized as follows: In Sect. 2, 3, and 4, we introduce strategic form games, potential games, and learning algorithms, respectively. We then discuss various potential games in Sects. 5 to 18, as shown in Table 1. Finally, we provide a few concluding remarks in Sect. 19.The notation used here is shown in Table 2. Unless the context indicates otherwise, sets of strategies are denoted by calligraphic uppercase letters, e.g., A i , strategies are denoted by lowercase letters, e.g., a i ∈ A i , and tuples of strategies are denoted by boldface lowercase letters, e.g., a. Note that a i is a scalar variable when A i is a set of scalars or indices, a i is a vector variable when A i is a set of vectors, and a i is a set variable when A i is a collection of sets.We use R to denote the set of real numbers, R + to denote the set of nonnegative real numbers, R ++ to denote the set of positive real numbers, and C to denote the set of complex numbers. The cardinality of set A is denoted by |A|. The power set of A is denoted by 2 A . Finally, ½condition is the indicator function, which is one when condition is true and is zero otherwise.We treat many sy...

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Yamamoto

2015

IEICE Trans. Commun.

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“…Interesting overviews on the potentials of game theory in the field of network selection are provided in [23] and [24]. In [25], a noncooperative game is used to model the association process to Wi-Fi-based APs.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Dynamic Network Architecture and Topology Optimization

Shafigh

Lorenzo

Glisic

et al. 2016

IEEE/ACM Trans. Networking

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Abstract-A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

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“…Network selection has been studied using game theory via several models including noncooperative game [19][20][21] and evolutionary game [22][23][24][25][26]. In [19] the authors propose a International Journal of Distributed Sensor Networks 3 study to capture the dynamics among end users and network operators in the processes of network selection and resource allocation. The authors resort to noncooperative game theory to model the competition among multiple end users in accessing shared wireless networks.…”

Section: Related Workmentioning

confidence: 99%

Gateway Selection Game in Cyber-Physical Systems

Wang

Gao

2016

International Journal of Distributed Sensor Networks

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Cyber-Physical Systems (CPSs) obtain the information of the physical world and impact the environment through many different kinds of devices. Usually, devices with different wireless technologies communicate with each other and the external networks through gateways placed in the working area. Different kinds of devices in a CPS may not operate with each other for their own benefit, and the competition will be more intense between different kinds of devices. They will contend for bandwidth of gateways to increase their throughput and avoid transmission delay. In this paper, we formulate this gateway selection situation as a noncooperative game. We investigate the actions of devices when they change their gateway and the result of devices' competition. We first give a bandwidth allocation model of gateways and propose a distributed algorithm for clients of gateway selection in order to increase the total bandwidth of their own kind. Then we investigate the migration trends of clients, and three theorems about the condition when clients stop migrating are given. We propose examples of gateway selection game with and without Nash Equilibrium. At last section, we give simulation results of gateway selection game.

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Network Selection and Resource Allocation Games for Wireless Access Networks

Cited by 72 publications

References 45 publications

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

A Framework for Dynamic Network Architecture and Topology Optimization

Gateway Selection Game in Cyber-Physical Systems

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