Proportional and double imitation rules for spectrum access in cognitive radio networks

Iellamo, Stefano; Chen, Lin; Coupechoux, Marceau

doi:10.1016/j.comnet.2013.03.008

Cited by 10 publications

(11 citation statements)

References 42 publications

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“…The version of Trial and Error presented in [15], for instance, is able to converge to the PNE maximizing the social welfare. Nevertheless, complexity is high and convergence speed is very slow, as it has been shown in [8]. The retrospective learning protocol that we propose has a similar architecture to the learning procedures developed in [12] and [17].…”

Section: Related Workmentioning

confidence: 93%

“…The analysis relies however on a random backoff mechanism, which requires the modification of the SUs packet structure for channel contention. In [8] the authors propose imitation rules that are used by a large population of SUs to converge to a Pure NE (PNE). In this paper, it is assumed that the SUs are able to capture packets transmitted by any other SU in the network.…”

Section: Related Workmentioning

confidence: 99%

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Retrospective spectrum access protocol: A payoff-based learning algorithm for cognitive radio networks

Iellamo

Chen

Coupechoux

2014

2014 IEEE International Conference on Communications (ICC)

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Decentralized cognitive radio networks (CRN) require efficient channel access protocols to enable cognitive secondary users (SUs) to access the primary channels in an opportunistic way without any coordination. In this paper, we develop a distributed retrospective spectrum access protocol that can orient the network towards a socially efficient and fair equilibrium state. With the developed protocol, each SU j chooses a channel to select based on the experienced payoff in past Hj periods. Each SU is thus supposed to be equipped with bounded memory and should make its decision based on only local observations. In that sense, the SUs behavioral rules are said to be payoffbased. The protocol also models a natural human decision making behavior of striking a balance between exploring a new choice and retrospectively exploiting past successful choices. With both analytical demonstration and numerical evaluation, we illustrate the two noteworthy features of our solution: (1) the entirely distributed implementation requiring only local observations and (2) the guaranteed statistical convergence to the equilibrium state within a bounded delay.

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Section: Related Workmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Retrospective spectrum access protocol: A payoff-based learning algorithm for cognitive radio networks

Iellamo

Chen

Coupechoux

2014

2014 IEEE International Conference on Communications (ICC)

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“…In [10], population game theory has been applied to model the spectrum access problem and develop distributed spectrum access policies based on imitation, a behavior rule widely applied in human societies consisting of imitating successful behaviors. In [11], the authors study the spectrum access problem in cognitive radio networks from a game-theoretical perspective.…”

Section: Introductionmentioning

confidence: 99%

A Physical Model for Predicting throughput of Wireless LANs

Pakparvar

David

Martens

et al. 2014

Lecture Notes in Electrical Engineering

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This paper presents an experimentally verified heuristic physical model to predict the throughput of a Wireless LAN link in presence of homogeneous interference of neighboring networks. The model predicts achievable throughput based upon two interference characteristics: transmission rate of the interface and the channel occupancy degree of the interference which is a measure of user activity defined in the paper.

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“…A channel selection problem in multiple collision domains was discussed in [191]. Iellamo et al [78] used numerically evaluated successful access probabilities depending on the number of stations in CSMA/CA as payoff functions. Here, we discuss channel selection problems in interference graph (I, E), where each node i ∈ I attempts to adjust its channel c i to maximize its successful access probability or throughput.…”

Section: Channel Assignment To Enhance Throughput In Collision Channelsmentioning

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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Proportional and double imitation rules for spectrum access in cognitive radio networks

Cited by 10 publications

References 42 publications

Retrospective spectrum access protocol: A payoff-based learning algorithm for cognitive radio networks

Retrospective spectrum access protocol: A payoff-based learning algorithm for cognitive radio networks

A Physical Model for Predicting throughput of Wireless LANs

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

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