Distributed Learning-Based Spectrum Allocation with Noisy Observations in Cognitive Radio Networks

Derakhshani, Mahsa; Le‐Ngoc, Tho

doi:10.1109/tvt.2014.2309120

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…where EE is the energy of the system in state s t , and a constant kτ is the product of Boltzmann's constant k and thermodynamic temperature τ . In this regard, if kτ −→ ∞ there will be a uniform distribution over the strategy set of player b, and if kτ −→ 0, it causes to select the strategy which is mostly reported by the users [48].…”

Section: Game Theory Based Framework For Power Allocationmentioning

confidence: 99%

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Banitalebi

Azmi

Mokari

et al. 2022

IEEE Open J. Commun. Soc.

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In this paper, we investigate a resource allocation problem for a Cellular Vehicle to Everything (C-V2X) network to improve energy efficiency of the system. To address this problem, self-organizing mechanisms are proposed for joint and disjoint subcarrier and power allocation procedures which are performed in a fully distributed manner. A multi-agent Q-learning algorithm is proposed for the joint power and subcarrier allocation. In addition, for the sake of simplicity, it is decoupled into two sub-problems: a subcarrier allocation sub-problem and a power allocation sub-problem. First, to allocate the subcarrier among users, a distributed Q-learning method is proposed. Then, given the optimal subcarriers, a dynamic power allocation mechanism is proposed where the problem is modeled as a non-cooperative game. To solve the problem, a no-regret learning algorithm is utilized. To evaluate the performance of the proposed approaches, other learning mechanisms are used which are presented in fig. 8. Simulation results show the multi-agent joint Q-learning algorithm yields significant performance gains of up to about 11% and 18% in terms of energy efficiency compared to proposed disjoint mechanism and the third disjoint Q-learning mechanism for allocating the power and subcarrier to each user; however, the multi-agent joint Q-learning algorithm uses more memory than disjoint methods.

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Section: Game Theory Based Framework For Power Allocationmentioning

confidence: 99%

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Banitalebi

Azmi

Mokari

et al. 2022

IEEE Open J. Commun. Soc.

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“…They also discussed G30 on graphs [46]. For this kind of transmission probability adjustment to satisfy x i < 1, the cost function cost i (x) = ½ i x i >1 needs to be used [50]. Because this cost function is a coordination function, a game with this cost function is still an EPG.…”

Section: Transmission Probability Adjustment For the Multiple-access mentioning

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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“…Moreover, spectrum allocation as a key part of cognitive radio technology, has received a great deal of attention from experts and scholars. Cognitive radio spectrum allocation can be divided into two categories according to the network structure [1,6]: centralized spectrum allocation [7][8][9] and distributed spectrum allocation [10][11][12]. The two methods of allocation are applied in different networks due to their respective characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

Zhang

Yang

Zhang³

et al. 2017

Information

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With the rapid development of maritime digital communication, the demand for spectrum resources is increasing, and building a maritime cognitive radio communication system is an effective solution. In this paper, the problem of how to effectively allocate the spectrum for secondary users (SUs) with different priorities in a maritime cognitive radio communication system is studied. According to the characteristics of a maritime cognitive radio and existing research about cognitive radio systems, this paper establishes a centralized maritime cognitive radio communication model and creates a simplified queuing model with two queues for the communication model. In the view of the behaviors of SUs and primary users (PUs), we propose a dynamic spectrum allocation (DSA) algorithm based on the system status, and analyze it with a two-dimensional Markov chain. Simulation results show that, when different types of SUs have similar arrival rates, the algorithm can vary the priority factor according to the change of users' status in the system, so as to adjust the channel allocation, decreasing system congestion. The improvement of the algorithm is about 7%-26%, and the specific improvement is negatively correlated with the SU arrival rate.

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Distributed Learning-Based Spectrum Allocation with Noisy Observations in Cognitive Radio Networks

Cited by 6 publications

References 33 publications

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

Distributed Learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

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