Multi-Channel Sensing and Access Game: Bayesian Social Learning with Negative Network Externality

Jiang, Chunxiao; Chen, Yan; Liu, K. J. Ray

doi:10.1109/twc.2014.022014.131209

Cited by 39 publications

(14 citation statements)

References 29 publications

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“…In [76], the authors addressed multi-channel sensing and access in distributed networks, with and without constraint on the number of channels that SUs are able to sense and access. They proposed a cooperative approach for estimating the channel state and used Bayesian learning to solve multi-channel sensing problem.…”

Section: Game Theorymentioning

confidence: 99%

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Abbas

Nasser

Ahmad

2015

J Wireless Com Network

122

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Cognitive radios are expected to play a major role towards meeting the exploding traffic demand over wireless systems. A cognitive radio node senses the environment, analyzes the outdoor parameters, and then makes decisions for dynamic time-frequency-space resource allocation and management to improve the utilization of the radio spectrum. For efficient real-time process, the cognitive radio is usually combined with artificial intelligence and machine-learning techniques so that an adaptive and intelligent allocation is achieved. This paper firstly presents the cognitive radio networks, resources, objectives, constraints, and challenges. Then, it introduces artificial intelligence and machine-learning techniques and emphasizes the role of learning in cognitive radios. Then, a survey on the state-of-the-art of machine-learning techniques in cognitive radios is presented. The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. This paper also discusses the cognitive radio implementation and the learning challenges foreseen in cognitive radio applications. IntroductionAccording to Cisco Visual Networking Index, the global IP traffic will reach 168 exabytes per month by 2019 [1], and the number of devices will be three times the global population. In addition, the resources in terms of power and bandwidth are scarce. Therefore, novel solutions are needed to minimize energy consumption and optimize resource allocation. Cognitive radio (CR) was introduced by Joseph Mitola III and Gerald Q. Maguire in 1999 for a flexible spectrum access [2]. Basically, they defined cognitive radio as the integration of model-based reasoning with software radio technologies [3]. In 2005, Simon Haykin had given a review of the cognitive radio concept and had treated it as brain-empowered wireless communications [4]. Cognitive radio is a radio or system that senses the environment, analyzes its transmission parameters, *Correspondence: nfa23@aub.edu.lb Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon and then makes decisions for dynamic time-frequencyspace resource allocation and management to improve the utilization of the radio electromagnetic spectrum.Generally, radio resource management aims at optimizing the utilization of various radio resources such that the performance of the radio system is improved. For instance, the authors in [5] proposed an optimal resource (power and bandwidth) allocation in cognitive radio networks (CRNs), specifically in the scenario of spectrum underlay, while taking into consideration the limitations of interference temperature limits. The optimization formulations provide optimal solutions for resources allocation at, sometimes, the detriment of global convergence, computation time, and...

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Section: Game Theorymentioning

confidence: 99%

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Abbas

Nasser

Ahmad

2015

J Wireless Com Network

122

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“…The research work developed in [25] proposes a game theoretic framework for joint spectrum sensing and spectrum access by considering the mutual influence between sensing and access for unlicensed users or Secondary Users (SUs) in two different scenarios: a synchronous scenario where the primary network is slotted, and an asynchronous scenario characterized by a non-slotted network. In [26], a cooperative channel state learning method based on a Bayesian learning rule for multi-channel sensing in different scenarios of the SUs is proposed. In [27], the authors focus their attention on a hierarchical DSA model to open the licensed spectrum to SUs while limiting the interference perceived by licensed ones or Primary Users (PUs).…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Implementation of Cognitive Radio Networks to evaluate spectrum management strategies in real-time

Raschellà

Umbert

2016

Computer Communications

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a b s t r a c tThis paper illustrates a Universal Software Radio Peripheral (USRP)-based real-time testbed that is able to evaluate different spectrum management solutions that exploit the Cognitive Radio (CR) paradigm. The main objective of this testbed is to provide an accurate and realistic platform by which the performance of innovative spectrum management solutions for a wide set of scenarios and use cases in the context of Opportunistic Networks (ONs) and Cognitive Radio Networks (CRNs) can be entirely validated and assessed before their implementation in real systems. Real-time platforms are essential to carry out significant studies and to accurately assess the performance of innovative solutions before their implementation in the real world. This work provides a comprehensive description of the testbed, highlighting many interesting implementation details and illustrating its applicability for different studies that rely on the CR paradigm. Then, a particular application in a realistic Digital Home (DH) scenario is also illustrated, which allows demonstrating the effectiveness of the real-time testbed and assessing its practicality in terms of user-perceived end-to-end Quality of Experience (QoE) in a realistic environment.

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“…Moreover, the negative externality is not considered in this static model, which means users' throughput are independent of each other [26], [27]. According to the Hotelling model in economics [28], which is usually used to model the distribution of customers within one area, we assume that cognitive users are linearly uniformly located within the coverage of each femtocell base station.…”

Section: Static Pricing Model Without Negative Externalitymentioning

confidence: 99%

“…In this model, the network access prices (p m , p f ) depends on the number of users in each network, and each user's utility depends on others' since the more users access one network, the less utility each user can obtain, i.e., negative network externality [26], [27]. Let us define the network state as S = (i m , i f ), where i m means the number of users in macrocell network and i f means that in femtocell network.…”

Section: Dynamic Pricing Model With Negative Externalitymentioning

confidence: 99%

Optimal Pricing Strategy for Operators in Cognitive Femtocell Networks

Jiang

Chen

Liu

et al. 2014

IEEE Trans. Wireless Commun.

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Abstract-Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of cognitive femtocell network have been studied, e.g., spectrum sharing, interference mitigation, etc., the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in the cognitive femtocell network and propose a two-tier pricing game theoretic framework with two models: static and dynamic pricing models. In the static pricing model, we derive the closed-form expressions for pricing and demand functions, as well as the Nash equilibrium pricing strategies for both macrocell and femtocell operators. In the dynamic pricing model, we first model the cognitive users' network access behavior as a two-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm in the dynamic pricing model can quickly converge to the Nash equilibrium prices.Index Terms-Cognitive femtocell, two-tier pricing, Nash equilibrium price, Markov decision process.

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Multi-Channel Sensing and Access Game: Bayesian Social Learning with Negative Network Externality

Cited by 39 publications

References 29 publications

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Recent advances on artificial intelligence and learning techniques in cognitive radio networks

Implementation of Cognitive Radio Networks to evaluate spectrum management strategies in real-time

Optimal Pricing Strategy for Operators in Cognitive Femtocell Networks

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