Two state Markov chain based predictive model for cognitive Radio spectrum availability: A conceptual approach

Oluwaranti, Adeniran Ishola; Okegbile, Samuel D.

doi:10.1109/ftc.2016.7821608

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…As an update on our previous work presented in [22] Kolmogorov Smirnov test was also performed so as to determine whether there is significant difference between the empirical distributions and the predicted distributions. The approach discussed in [25] [26] [27] [28] has been adopted for it ease of representation.…”

Section: Resultsmentioning

confidence: 99%

“…The experimental design approach adopted in this work involves using the present behavior of the primary users to predict their future behavior. This adopted approach has been used in several existing works as found in [7] [22] etc. The traffic patterns obtained from the behavior of the PUs were carefully studied and the prediction was done using the transition probabilities obtained from the spectrum usage.…”

Section: Methodsmentioning

confidence: 99%

“…Looking at the pattern, a model developed for such a pattern will be used for Figure 3. The proposed model design [22].…”

Section: Model Designmentioning

confidence: 99%

“…The considered deterministic patterns were then used to predict the future states of the spectrum. The details of the discrete-time two-state Markov chain with appropriate Duty Cycle (DC) models adopted in this work has been presented in our previous work [22]. Here, we focused on the modified bell shaped exponential equation formulated and presented the evaluation results.…”

Section: Model Designmentioning

confidence: 99%

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On Evaluating the Efficacy of Predictive Models for Cognitive Radio Spectrum Availability in Nigeria

Okegbile¹,

Oluwaranti²,

Aina³

2018

WET

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In order to avoid the undesired interference with the activities of the primary users in cognitive radio networks, the secondary users are required to be able to predict the behavior of the primary users so as to leave the channel before the arrival of such licensed owner of the spectrum. While a number of existing literatures on cognitive radio spectrum prediction employ the use of propagation curves for predicting the spectrum holes otherwise known as TV white space, these models are built based on measurements conducted in regions that are different from Nigeria, suitability in terms of usage may therefore vary due to environmental factors and terrain profile. This work evaluates the efficacy of the developed model in predicting the cognitive spectrum availability in Nigeria. Models capable of predicting spectrum occupancy in the time domain using discrete-time two-state Markov chain with an appropriate Duty Cycle (DC) model and also a modified m-bell shaped exponential equation were formulated. The result obtained in all cases considered shows that the formulated models are appropriate to be used in any environment if the parameters were carefully extracted from the data. This work has also demonstrated that the accuracy of Markov chain models depends on the level of usage of a spectrum under consideration and may therefore not give desirable results when employed in some other spectrum.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Looking at the pattern, a model developed for such a pattern will be used for Figure 3. The proposed model design [22].…”

Section: Model Designmentioning

confidence: 99%

Section: Model Designmentioning

confidence: 99%

See 2 more Smart Citations

On Evaluating the Efficacy of Predictive Models for Cognitive Radio Spectrum Availability in Nigeria

Okegbile¹,

Oluwaranti²,

Aina³

2018

WET

Self Cite

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“…The status of the channel occupied by the PU can be divided into busy and idle [17,18,40,41]; thus, the state transition diagram of the PU can be drawn-1 means busy, 0 means idle-as Figure 4 shows. priority factor p, which characterizes the probability that SUq2 will refuse to stop using channels.…”

Section: Impact Of Pumentioning

confidence: 99%

A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

Zhang

Yang

Zhang³

et al. 2017

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With the rapid development of maritime digital communication, the demand for spectrum resources is increasing, and building a maritime cognitive radio communication system is an effective solution. In this paper, the problem of how to effectively allocate the spectrum for secondary users (SUs) with different priorities in a maritime cognitive radio communication system is studied. According to the characteristics of a maritime cognitive radio and existing research about cognitive radio systems, this paper establishes a centralized maritime cognitive radio communication model and creates a simplified queuing model with two queues for the communication model. In the view of the behaviors of SUs and primary users (PUs), we propose a dynamic spectrum allocation (DSA) algorithm based on the system status, and analyze it with a two-dimensional Markov chain. Simulation results show that, when different types of SUs have similar arrival rates, the algorithm can vary the priority factor according to the change of users' status in the system, so as to adjust the channel allocation, decreasing system congestion. The improvement of the algorithm is about 7%-26%, and the specific improvement is negatively correlated with the SU arrival rate.

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Review of Markov Chain and Its Applications in Telecommunication Systems

Omer

Woldegebreal

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Two state Markov chain based predictive model for cognitive Radio spectrum availability: A conceptual approach

Cited by 8 publications

References 6 publications

On Evaluating the Efficacy of Predictive Models for Cognitive Radio Spectrum Availability in Nigeria

On Evaluating the Efficacy of Predictive Models for Cognitive Radio Spectrum Availability in Nigeria

A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model

Review of Markov Chain and Its Applications in Telecommunication Systems

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