Modeling of Learning Inference and Decision-Making Engine in Cognitive Radio

Huang, Yuqing; Wang, Jiao; Jiang, Hong

doi:10.1109/nswctc.2010.195

Cited by 16 publications

(6 citation statements)

References 10 publications

(10 reference statements)

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“…Yuqing Huang et al in [75] proposed a CR learning interference and decision-making engine based on Bayesian networks. The authors made use of the junction tree algorithm to model interference using probabilistic models obtained from the BNs.…”

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

123

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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

123

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“…During the learning process, each SU sees the channel and other secondary users as its environment, updates its Q-values and takes the best action based on the prevalent situation. Authors in [87] used an artificial intelligence technique in developing a decision-making tool for allocating resource in CRN. In the developed model, cognitive radio learning inference and decision-making engine based on Bayesian network was proposed to obtain the optimum configuration rules to adapt to the variation of the environment with the learning and inference algorithm of Bayesian network.…”

Section: Solutions Through Soft Computingmentioning

confidence: 99%

Solving resource allocation problems in cognitive radio networks: a survey

Awoyemi

Maharaj

Alfa

2016

J Wireless Com Network

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Cognitive radio networks (CRN), in their quest to become the preferred next-generation wireless communication paradigm, will depend heavily on their ability to efficiently manage the limited resources at their disposal in meeting the demands of their numerous users and driving their operations. As a result, a considerable amount of research work has been recently dedicated to investigating and developing resource allocation (RA) models that capture the essentials of CRN. The various ideas put forward by researchers to address RA problems in CRN have been somewhat diverse, and somehow, there seem to be no links that bring cohesion and clarity of purpose and ideas. To address this problem and bridge the gap, in this paper, a comprehensive study on the prevalent techniques developed for addressing RA problems in CRN is carried out, with an intent to put some structure, relevance and meaning to the various solution approaches. The solution models are therefore grouped and/or classified based on certain outstanding criteria, and their strengths and weaknesses highlighted. Open-ended problems are identified, and suggestions for improving solution models are given. The study therefore gives good directions for further investigations on developing RA solutions in CRN.

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“…CE models based on AI and machine learning techniques take radio parameters, environment parameters, and given objectives as inputs and present a solution that satisfy given objectives and Quality of service (QoS) [31]. In addition to learning the wireless communication environment, the CE can adapt the system parameters in response to the variation of the environment [32]. Along with taking into account the input parameters, the CE may also utilize users context and take into account the network conditions.…”

Section: Cognitive Enginementioning

confidence: 99%

Algorithms for Opportunistic Load Balancing Cognitive Engine

Mäkeläinen¹,

Khan²,

Hänninen³

et al. 2014

Proceedings of the 9th International Conference on Cognitive Radio Oriented Wireless Networks

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Due to the increasing use of more and more powerful smart devices demands on the scarce radio spectrum are becoming more intense. One way to cope with increasing demands on radio spectrum is to apply innovative and flexible authorization schemes like spectrum sharing. Under the spectrum sharing paradigm, multiple users and/or systems are authorized to utilize the same spectrum band in a defined sharing agreement. A technology that is generally recommended for the implementation of spectrum sharing is called cognitive radio (CR).In this thesis, we design and implement a cognitive engine (CE) that intelligently and dynamically allocates spectrum resources to users. We first consider a scenario where a network has an exclusive access to a spectrum band and the CE accepts or rejects the arrival user requests based on an algorithm that takes into account a user's priority and its bandwidth demand. We then consider a spectrum sharing scenario where along with the exclusive utilization to its own spectrum band a network also can opportunistically utilize another network's spectrum band. For this scenario, we design and implement a CE that performs two main tasks: 1) Accepts or rejects arrival user requests based on a priority based algorithm; and 2) it intelligently load balances the user traffic between the two available network resources, while taking into account the primary user activity in the shared spectrum band. We provide a load balancing algorithm and evaluate its performance under different primary and secondary user traffic scenarios. We show that the proposed load balancing algorithm increases average throughput of the network and it also reduces the average number of users rejected by the network. Keywords: cognitive radio, simulator, medium access, radio resource management Mäkeläinen M. (2013) Opportunistiset kuormituksentasausalgoritmit kognitiivisessa koneessa. Oulun yliopisto, tietotekniikan osasto. Diplomityö, 64 s. TIIVISTELMÄYhä tehokkaampien älykkäiden langattomien päätelaitteiden nopea lisääntymi-nen johtaa niukan radiospektrin yhä kiihtyvään käyttöön. Eräs menetelmä radiospektrin lisääntyvän kysynnän tyydyttämiseen on hyödyntää innovatiivista ja joustavaa resurssin käytönjakoa kuten spektrin jakamista. Spektrinjakamismalli mahdollistaa useiden käyttäjien ja/tai järjestelmien yhtäaikaisen käytön samalla taajuuskaistalla hyödyntämällä sovittua käytäntöä resurssien jakamisesta. Radiospektrin jakaminen on tänä päivänä yleisesti suositeltu toteuttamaan hyödyn-tämällä kognitiivista radioteknologiaa.Tässä työssä suunnittellaan ja toteutetaan kognitiivinen päätöksentekokone, joka jakaa radiospektriresursseja käyttäjille älykkäästi ja dynaamisesti. Kognitiivista päätöksentekokonetta radioresurssien jakamisessa hyödynnetään kahdessa skenaariossa. Ensimmäisessä skenaariossa radioverkolla on yksinomainen pää-sy taajuuskaistalle, jonka käyttöä kognitiivinen päätöksentekokone säätelee joko hyväksymällä tai hylkäämällä verkkoon liittyviä käyttäjiä. Kognitiivinen päätök-sentekokoneen päätökset perustuu...

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Modeling of Learning Inference and Decision-Making Engine in Cognitive Radio

Cited by 16 publications

References 10 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

Solving resource allocation problems in cognitive radio networks: a survey

Algorithms for Opportunistic Load Balancing Cognitive Engine

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