Radio Environment Prediction for Cognitive Radio

Takeuchi, Kazunori; Kasahara, Shoji; Nomoto, Satoshi

doi:10.1109/crowncom.2008.4562502

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…By doing so, the cognitive radio can predict the availability of wireless media for transmission capacity estimation. In [8], a prediction of radio environment for cognitive radio is proposed. With the radio environment observation being done in various locations for the unlicensed band of 2.4 GHz, the result indicates that the radio channel occupation time presents long-range dependency with the time scale longer than 0.1 second.…”

Section: Learning In Cognitive Radiomentioning

confidence: 99%

Cognitive Wireless Sensor Networks: Intelligent Channel Assignment

Chantaraskul

Tanwongvarl

2017

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Abstract. One of the major problems in Wireless Sensor Networks (WSNs) is the traffic congestion caused by increasing number of devices sharing the limited spectrum of the ISM (Industrial, Scientific, and Medical radio) band. As a result, a new concept of Cognitive Wireless Sensor Networks (CWSNs) has been proposed in order to achieve reliable and efficient communication via spectrum awareness and smart adaption. Based on such concept, this paper proposes the intelligent channel assignment technique for channel management in CWSNs. The proposed method is based on the learning and prediction technique so called Policy Gradient together with our proposed virtual channel environment classification. Simulation model is used for the system performance evaluation. The simulation results show that our proposed intelligent channel assignment provides substantially higher performance in terms of system throughput and average packet end-to-end delay than the traditional IEEE 802.15.4 based system. It also outperforms the systems integrated with Episodic Reinforcement and GPOMDP learning technique.

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Section: Learning In Cognitive Radiomentioning

confidence: 99%

Cognitive Wireless Sensor Networks: Intelligent Channel Assignment

Chantaraskul

Tanwongvarl

2017

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“…Learning Threats CR has the ability to learn, it can be under the past experience and the current environment to predict the future, and select the best set of parameters [17]. CR can be seen as the human mind [18] extensions.…”

Section: Artificial Intelligence Behavior Threatsmentioning

confidence: 99%

The Research of Network Security Based on Cognitive Radio

Mu¹,

Li²

2016

IJSIA

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Firstly, we are given the security problems faced by a detailed analysis of the cognitive radio networks, and introduces the related basic cognitive radio network problems. Then, based on the difference between the cognitive radio network and the existing wireless network, which analyzes security and artificial intelligence, dynamic spectrum access and discussion. It concluded for the safety of cross-layer design.

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“…Therefore, any predictive models used to forecast opportunities based on primary user past behavior, e.g. [7] should be carefully reconsidered when mobility of secondary users is take into account.…”

Section: Opportunites In Gsm900 Downlink Bandsmentioning

confidence: 99%

Coexistence Analysis and Cognitive Opportunities Selection in GSM Bands

Marques

Ribeiro

et al. 2009

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference

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This paper presents a characterization of spectrum opportunities in GSM900 downlink bands, for fixed and mobile scenarios. A testbed to analyze coexistence between the licensed GSM900 downlink system and an OFDM based secondary system that opportunistically shares the same spectrum is presented. Coexistence analysis is based on metrics such as spectrum collisions rate and the secondary system throughput. Using this testbed a cognitive mechanism to classify and select stable opportunities is evaluated in realistic scenarios.

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Radio Environment Prediction for Cognitive Radio

Cited by 9 publications

References 3 publications

Cognitive Wireless Sensor Networks: Intelligent Channel Assignment

Cognitive Wireless Sensor Networks: Intelligent Channel Assignment

The Research of Network Security Based on Cognitive Radio

Coexistence Analysis and Cognitive Opportunities Selection in GSM Bands

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