Distributed Resource Allocation Based on Game Theory in Multi-cell OFDMA Systems

Qiu, Jing; Zhou, Zheng

doi:10.1007/s10776-009-0088-y

Cited by 25 publications

(16 citation statements)

References 10 publications

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“…Such power allocation is aimed to the up-link communication toward the CR local base station. A game theoretic solution for distributed uplink resource allocation in multi-cell OFDMA systems is presented in [12] where convergence and Nash equilibrium was proved using potential game. The potential games are introduced because they can guarantee the existence of at least one NE.…”

Section: Related Workmentioning

confidence: 99%

Bargaining Solutions for Energy Efficient and Fair Power Allocation in Cognitive D2D Communications

Fourati¹,

Hamouda²,

Maharaj³

2016

IJCA

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Device-to-Device (D2D) technology underlying the cellular network is an attractive solution for future generation network to increase cellular traffic offloading. In order to reduce the interference caused to cellular network, D2D users can opportunistically access the cellular spectrum using cognitive radio capabilities. They can either avoid interference by transmitting on the spectrum wholes or merely control their power while transmitting simultaneously with the cellular users. In this paper, cognitive D2D users, referred to as Secondary Users (SUs), communicate at the same time as uplink cellular users. A new power control approach is proposed based on bargaining game theoretic solutions to better control SUs' transmit power and thus reduce the interference level at the cellular base station referred to as the Primary User (PU). First, the SUs utility functions are defined and take into account the achieved data rate, the power consumption and the impact of the interference. Then, the optimal power allocation is analyzed through the application of the Nash bargaining, Kalai-Smorodinsky and other bargaining solutions. A comparison between the performance of these solutions in terms of energy efficiency and fairness is derived. Simulation results show that a tradeoff between fairness and energy efficiency should be taken into account. The performance of cooperative solutions is also compared with non-cooperative games both analytically and through simulations. General TermsCognitive radio, Game Theory, D2D Communications, 5G.

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Section: Related Workmentioning

confidence: 99%

Bargaining Solutions for Energy Efficient and Fair Power Allocation in Cognitive D2D Communications

Fourati¹,

Hamouda²,

Maharaj³

2016

IJCA

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“…A transmission power control game G32 with multiple channels was discussed in [73], [81], [144]. Let the set of channels be denoted by C. Each TX i ∈ I transmits through a subset of C to maximize the aggregated capacity…”

Section: Multi-channel Systemsmentioning

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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“…Game and supermarket game theory have been recently adopted to address the problem of resource allocation in wireless networks (see, for example, [59], [63], [64], [66], [70] and [71]). Table 8 summarizes the main features of a number of selected studies conducted on the resource allocation problem in multicarrier wireless networks using both game theory and supermarket game theory.…”

Section: Game and Supermarket Game In Non-cognitive MC Scenariosmentioning

confidence: 99%

Resource Allocation in Multiuser Multi-Carrier Cognitive Radio Network via Game and Supermarket Game Theory: Survey, Tutorial, and Open Research Directions

2014

KSII TIIS

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In this tutorial, we integrate the concept of cognitive radio technology into game theory and supermarket game theory to address the problem of resource allocation in multiuser multicarrier cognitive radio networks. In addition, multiuser multicarrier transmission technique is chosen as a candidate to study the resource allocation problem via game and supermarket game theory. This tutorial also includes various definitions, scenarios and examples related to (i) game theory (including both non-cooperative and cooperative games), (ii) supermarket game theory (including pricing, auction theory and oligopoly markets), and (iii) resource allocation in multicarrier techniques. Thus, interested readers can better understand the main tools that allow them to model the resource allocation problem in multicarrier networks via game and supermarket game theory. In this tutorial article, we first review the most fundamental concepts and architectures of CRNs and subsequently introduce the concepts of game theory, supermarket game theory and common solution to game models such as the Nash equilibrium and the Nash bargaining solution. Finally, a list of related studies is highlighted and compared in this tutorial. R(1) Interaction among independent users: In CRNs, there are two types of users (i.e., PUs and CRs), also called decision makers, with conflicts of interest, interacting with each other independently, trying to access the same spectrum band. This interaction adds certain obstacles in analyzing the problem of resource allocation in MMC-CRNs. Game theory, in contrast, appears to be one of the most attractive tools for removing these obstacles because it is mainly used to model scenarios where the action of one player impacts/conflicts with that of other players in the network [4]. Moreover, the availability of a common solution in game theory such as the Nash equilibrium and Nash bargaining add another advantage in modeling the problem of resource allocation via game theory.(2) Spectrum supermarket and real supermarket: The behavior of PUs and CRs in allocating their resources in MMC-CRNs is quite similar to the interactions among people in actual markets. Both include the following features: (i) Pricing, where the owner of the spectrum can gain benefits by renting the available spectrum holes to the tenants in the network (i.e., CR nodes). Thus, mutual benefits exist whereby the PUs improves their revenue and CRs enjoy access to the band for their needs. (ii) Auction, where CRs compete with each other in an auction scenario to obtain access to certain bands. The similarities between the concept of CRs and the interaction among people in a real supermarket make the adoption of economics concepts in analyzing the problem of resource allocation in MMC-CRNs another attractive tool.

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Distributed Resource Allocation Based on Game Theory in Multi-cell OFDMA Systems

Cited by 25 publications

References 10 publications

Bargaining Solutions for Energy Efficient and Fair Power Allocation in Cognitive D2D Communications

Bargaining Solutions for Energy Efficient and Fair Power Allocation in Cognitive D2D Communications

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Resource Allocation in Multiuser Multi-Carrier Cognitive Radio Network via Game and Supermarket Game Theory: Survey, Tutorial, and Open Research Directions

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