Design of Learning Engine Based on Support Vector Machine in Cognitive Radio

Huang, Yuqing; Jiang, Hong; Hu, Hong; Yuan-cheng, Yao

doi:10.1109/cise.2009.5363127

Cited by 31 publications

(20 citation statements)

References 3 publications

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“…Require: current state s(t), old states s(x), F(t) Ensure: selection of CE algorithm that output max GP 1: Learning: given network state s(t); 2: Exploration with probability τ ; 3: Select action randomly; 4: Update A (s(t)) = {a|R(s(t), a) = 1} for s(t) ; 5: Exploitation with probability 1 − τ ; 6: Select z records A " (S (s(t), F(t))) out of F actions associated with S (s(t), F(t)); 7: Calculate R(s(t), a) according to (9) and populate A R (s(t))…”

Section: Algorithm 1 Online Learning For Ce Technique Selectionmentioning

confidence: 99%

“…Zhang and Xie proposed an improved model of learning based CE with Artificial Neural Networks (ANN) methodology in [8]. In [9], Huang et al designed a learning engine framework based on Support Vector Machine (SVM) to configure radio parameters using the estimation of bit error rate (BER) and signal-to-noise-ratio (SNR). The work in [10] proposed a primitive architecture for meta-cognition, which is used to rate the solution achieved by CBR and from this point it can decide if it is necessary to look for alternative adaptation algorithm such as GA. Y. Zhao et al [11] looked into utility function selection for streaming video with a CE testbed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supervised cognitive system: A new vision for cognitive engine design in wireless networks

AlQerm

Shihada

2018

2018 15th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Section: Algorithm 1 Online Learning For Ce Technique Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised cognitive system: A new vision for cognitive engine design in wireless networks

AlQerm

Shihada

2018

2018 15th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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“…Indeed, some machine-learning-based algorithms have been proposed for various tasks in CR parameter cognition. For example, in [7], a spectrum sensing engine based on a support vector machine (SVM) was designed, advancing sensing performance with smaller samples compared to the energy detector. In the cooperative spectrum sensing paradigm, [8] presents reinforced learning methods to reduce the sensing overhead by releasing network flow congestion.…”

Section: Multi-parameter Cognitionmentioning

confidence: 99%

Enhanced multi-parameter cognitive architecture for future wireless communications

Gao

Zhang

2015

IEEE Commun. Mag.

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“…In [52], the authors used SVM to add a learning design to the CR engine. The proposed model is based on bit error rate (BER), SNR, data rate, and modulation mode.…”

Section: Game Theorymentioning

confidence: 99%

“…For instance, game theory is used in decentralized multi-agent systems while reinforcement learning can be used in centralized and single-agent systems. Neural networks and SVM can be also used for some applications in decision-making, for instance, in [37], neural networks are used to adjust the parameters of the system so as to effectively adapt to the environment as it changes, and in [52], the SVM technique builds a model for determining the modulation scheme using the following inputs: bit error rate, SNR, and data rate. We remind the reader that the learning techniques and their applications in CR are summarized in Table 1.…”

Section: Matching Learning and Cr Tasksmentioning

confidence: 99%

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

Abbas

Nasser

Ahmad

2015

J Wireless Com Network

124

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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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Design of Learning Engine Based on Support Vector Machine in Cognitive Radio

Cited by 31 publications

References 3 publications

Supervised cognitive system: A new vision for cognitive engine design in wireless networks

Supervised cognitive system: A new vision for cognitive engine design in wireless networks

Enhanced multi-parameter cognitive architecture for future wireless communications

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

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