Channel selection for Selective Sensing using Cognitive Radio Nodes

Kaniezhil, R.; Chandrasekar, C.; Rekha, S. Nithya

doi:10.5120/4800-7019

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…Distances to the primary users were normalized to [0,10]. Figure 4 shows the comparison of call blocking of the service provider using the Fuzzy logic system and normal Spectrum Utilization (NSU) [7][8][9][10][11][12]. As the call arrival rate increases the blocking rate gets decreased.…”

Section: Simulation Resultsmentioning

confidence: 99%

An efficient spectrum utilization via cognitive radio using fuzzy logic system for heterogeneous wireless networks

Kaniezhil

Chandrasekar

2012

2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

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At present, lots of works focus on spectrum allocation of wireless networks. In this paper, we proposed a Cognitive based spectrum access by opportunistically approach of Heterogeneous Wireless networks based on Fuzzy Logic system. The Cognitive Radio is a technology where a network or a wireless system changes its environment parameters to communicate efficiently by avoiding the interference with the users. By applying FLS (Fuzzy Logic System), the available spectrum utilization is effectively utilized with the help of the three antecedents namely Spectrum utilization efficiency, Degree of mobility, Distance from primary user to the secondary users. The proposed work is compared with normal Spectrum Utilization method. Finally, Simulation results of the proposed work Fuzzy Logic System shows more efficient than the normal Spectrum utilization method.

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Section: Simulation Resultsmentioning

confidence: 99%

An efficient spectrum utilization via cognitive radio using fuzzy logic system for heterogeneous wireless networks

Kaniezhil

Chandrasekar

2012

2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

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“…Cooperative spectrum sharing was also proposed by some researchers. Cooperative spectrum sharing involves sharing of static CR nodes of various service providers [17][18].…”

Section: Related Workmentioning

confidence: 99%

“…The probability for the unnecessary handover is computed using MATLAB and the results are presented in the figure. 18. Channels, 8000 iterations and 70%, 50% and 60% of PU calls with no conversion.…”

Section: Probability Of Unnecessary Handovermentioning

confidence: 99%

Selective Channel Assignment Method in Cognitive Radio System

Gowda¹,

Vijayakumar²

2021

Preprint

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An Efficient Channel assignment method for cognitive radio system has been proposed in this paper, by considering primary and secondary calls separately in the network for the cases of with and without usage of converters. The proposed channel assignment method known as selective channel assignment method its performance is compared with the existing first fit assignment and uniformly distributed random assignment methods. Each of the models has variant for with conversion and without conversion of wavelength. The simulations are run for a network having 10, 20 channels, 12, 15 and 25 links and 8 Erlangs of load. By carrying out the simulations of the proposed and existing channel assignment methods, the blocking probabilities, throughput and channel usage frequencies are computed for each of the assignment methods. When the selective channel assignment method was used, the blocking probabilities are around 41% and 39% for 50% PU calls case and 64% and 26% for 75% PU calls case when there were no converters in the network. When converters are used, the blocking probabilities are around 30% and 36% for 50% PU calls case and 38% and 18% for 75% PU calls case. Our simulations validated the effectiveness of the proposed channel assignment scheme in terms of blocking probability, throughput and channel usage as performance parameters.

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“…An infrastructure based CR network that empowers cooperative spectrum sharing over different service providers by means of static CR nodes have been proposed in [10,11]. At the point when all the channels of a service provider are accessed or it is over-burden, it can utilize the available channels that are authorized to the service providers that are under loaded.…”

Section: Literature Surveymentioning

confidence: 99%

Call Arrival Rate Prediction and Blocking Probability Estimation for Infrastructure based Mobile Cognitive Radio Personal Area Network

Nathani¹,

Manna²

2017

Adv. sci. technol. eng. syst. j.

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The Cognitive Radio usage has been estimated as non-emergency service with low volume traffic. Present work proposes an infrastructure based Cognitive Radio network and probability of success of CR traffic in licensed band. The Cognitive Radio nodes will form cluster. The cluster nodes will communicate on Industrial, Scientific and Medical band using IPv6 over Low-Power Wireless Personal Area Network based protocol from sensor to Gateway Cluster Head. For Cognitive Radio-Media Access Control protocol for Gateway to Cognitive Radio-Base Station communication, it will use vacant channels of licensed band. Standalone secondary users of Cognitive Radio Network shall be considered as a Gateway with one user. The Gateway will handle multi-channel multi radio for communication with Base Station. Cognitive Radio Network operators shall define various traffic data accumulation counters at Base Station for storing signal strength, Carrier-to-Interference and Noise Ratio, etc. parameters and record channel occupied/vacant status. The researches has been done so far using hour as interval is too long for parameters like holding time expressed in minutes and hence channel vacant/occupied status time is only probabilistically calculated. In the present work, an infrastructure based architecture has been proposed which polls channel status each minute in contrary to hourly polling of data. The Gateways of the Cognitive Radio Network shall monitor status of each Primary User periodically inside its working range and shall inform to Cognitive Radio-Base Station for preparation of minutewise database. For simulation, the occupancy data for all primary user channels were pulled in one minute interval from a live mobile network. Hourly traffic data and minutewise holding times has been analyzed to optimize the parameters of Seasonal Auto Regressive Integrated Moving Average prediction model. The blocking probability of an incoming Cognitive Radio call has been assessed as a function of instantaneous occupancy and the blocksize of trunk servers.

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Channel selection for Selective Sensing using Cognitive Radio Nodes

Cited by 8 publications

References 17 publications

An efficient spectrum utilization via cognitive radio using fuzzy logic system for heterogeneous wireless networks

An efficient spectrum utilization via cognitive radio using fuzzy logic system for heterogeneous wireless networks

Selective Channel Assignment Method in Cognitive Radio System

Call Arrival Rate Prediction and Blocking Probability Estimation for Infrastructure based Mobile Cognitive Radio Personal Area Network

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