Interference Reducing Networks

Neel, J.; Menon, Rekha; MacKenzie, Allen B.; Reed, Jeffrey H.; Gilles, Robert P.

doi:10.1109/crowncom.2007.4549780

Cited by 14 publications

(14 citation statements)

References 17 publications

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“…From the defined utility function of the users in (3), the potential function of the proposed potential game can be obtained using [11]:…”

Section: A the Proposed Game Modelmentioning

confidence: 99%

“…the negative of the total received interference of the whole network), each unilateral action of a player in the way of maximizing her utility function will indirectly lead to improvement in the utility of the total network. As a result, when the game converges to the equilibrium point, the revenue of the total network will be maximized as well [11].…”

Section: Introductionmentioning

confidence: 99%

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A game-theoretic approach for joint channel and power allocation in downlink of cellular cognitive radio networks

Omidvar

Khalaj

2012

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

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In this paper, we consider a cellular cognitive radio network that uses opportunistic spectrum access for its transmission in the downlink direction from base stations toward cognitive radio users. Since the spectrum is licensed to a primary network, proper resource management should be employed in the secondary network to protect primary users against excessive interference while providing acceptable utilization for secondary users. We use game theory to propose a distributed method for joint frequency and power allocation in the downlink of cognitive radio networks. Earlier works in this area were based on centralized optimization-based approaches, whereas our proposed method solves the problem of joint resource allocation in a distributed way at lower complexity and less message passing. Comparing the proposed decentralized method with a centralized method shows that our proposed joint resource allocation scheme results in significantly less power consumption while supporting more secondary users.

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“…From the defined utility function of the users in (3), the potential function of the proposed potential game can be obtained using [11]:…”

Section: A the Proposed Game Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A game-theoretic approach for joint channel and power allocation in downlink of cellular cognitive radio networks

Omidvar

Khalaj

2012

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

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“…1(b) are discussed. Neel et al [135], [136] considered a different channel allocation game typically applied to channel allocation for APs in the wireless local area networks (WLANs) shown in Fig. 1(c), where each TX i ∈ I selects a channel c i ∈ C i to minimize the interference from other TXs, i.e.,…”

Section: Identical Transmission Power Levelsmentioning

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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“…MODIFIED DYNAMIC FREQUENCY SELECTION ALGORITHM Concepts such as Interference reduction networks and dynamic frequency selection algorithms have been addressed in the literature ( [10] and [11] . As mentioned earlier, in this paper, necessary enhancements in order to guarantee continuous operation requirements of cognitive radio networks shall be presented.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Complexity Dynamic Frequency Selection Algorithm for Cognitive Radios

Bahramian

Khalaj

2007

2007 4th International Symposium on Wireless Communication Systems

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In this paper, a non-cooperative distributed frequency selection algorithm is proposed for cognitive radio networks in which in addition to interference minimization, the minimization of implementation complexity and probability of interruption of secondary users are also addressed. The convergence and interference reduction property of the algorithm is also demonstrated using game theoretic concepts. In order to evaluate the performance of the algorithm, the associated optimization problem is formulated into a variant of graph colouring problem, and a centralized algorithm is also proposed as an approximation of the optimal solution. Finally, simulation results demonstrate that the proposed distributed algorithm achieves acceptable performance in comparison with earlier schemes.

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Interference Reducing Networks

Cited by 14 publications

References 17 publications

A game-theoretic approach for joint channel and power allocation in downlink of cellular cognitive radio networks

A game-theoretic approach for joint channel and power allocation in downlink of cellular cognitive radio networks

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

A Novel Low-Complexity Dynamic Frequency Selection Algorithm for Cognitive Radios

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