Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Devanarayana, Chamara; Alfa, Attahiru Sule

doi:10.1109/glocom.2011.6133706

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Introducing one more state named ''fuzzy'' (unknown availability), a 3-state higher-order HMM channel state predictor is introduced in [17]. Instead of using a fixed length of observations, a variable order Markov model is introduced in [18]. Recently, a frequent pattern mining (FPM) based predictor [5] demonstrates that it provides better channel state prediction performance over the first-order HMM-based predictor.…”

Section: Related Workmentioning

confidence: 99%

Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

Huang

Liu

Yang

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. When unlicensed users opportunistically utilize spectrum holes, prediction models that infer the availability of spectrum holes can help to improve the spectrum extraction rate and reduce the collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims at identifying frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users are able to utilize spectrum holes aggressively without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real-world Wi-Fi and personal communication service (PCS) activities, we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved. Further, we extend the three-state PPPM to an N-state PPPM to predict the duration of high/low utilization in a channel. The frequent patterns of channel utilization duration are critical in optimizing channel switch strategies. The high prediction accuracy is validated with data collected in the paging bands.

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Section: Related Workmentioning

confidence: 99%

Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

Huang

Liu

Yang

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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“…The continuous demand for wireless communication technologies and systems Wireless Engineering and Technology has reached a peak where existing capacity cannot meet increasing demand without means of improving the efficiency of spectrum utilization. The Owned spectrum allocation policy also known as the fixed spectrum allocation policy has led to Spectrum Scarcity [1] [2] [3]. However, research by Federal Communications Commission (FCC) Spectrum Policy Task Force showed that a large portion of the assigned spectrum was used sporadically, leading to under-utilization of a significant amount of spectrum.…”

Section: Introductionmentioning

confidence: 99%

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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“…In the literature, there are mainly three categories of PSA schemes as follows: analytic formulations ( [9][10][11]), heuristic schemes based on assumed channel usage ( [12][13][14][15]), and heuristic schemes based on channel usage learning ( [4,[16][17][18]). Most of the learning methods like neural networks (e.g., [16]) and binary time series models (e.g., [18]) in the literature fixed the value of the memory to be used in prediction.…”

Section: Existing Schemes On Psamentioning

confidence: 99%

“…In Sections 1.3 and 1.2, we discuss some available solutions for the problems 2 and 5, respectively. This paper is an extension to the work in [4]. In that paper, we only discussed the usage of probabilistic suffix tree (PST) as a prediction mechanism, where there was only a single SU trying to access the channel.…”

Section: Introductionmentioning

confidence: 99%

Proactive channel access in cognitive radio networks using statistical radio environment maps

Devanarayana

Alfa

2015

J Wireless Com Network

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Inclusion of statistical knowledge of the primary user (PU) channel usage had shown to be beneficial in dynamic spectrum access. Motivated by this fact, this paper investigated the importance of collecting and using statistics on neighboring secondary users (SUs) in selecting channels in addition to the knowledge of PU channel usage. The paper assumed that PU traffic characteristics of the channels are included in the radio environment map in the form of probabilistic suffix trees, which is a sequence predictor based on Markov property. In the proposed method, an intelligent sequence hopping-based common control channel and a carrier sense multiple access (CSMA)/collision avoidance (CA)-based medium access control (MAC) protocol were introduced. As shown in the paper, selecting channels using statistics of both the neighboring SUs and PUs reduced the number of packet collisions compared to a scheme which only uses PU statistics. Furthermore, the simulation results showed that the scheme proposed had better throughput performance with respect to both the random channel selection scheme and the scheme which only uses PU statistics while having less training complexity.

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Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Cited by 10 publications

References 36 publications

Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

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

Proactive channel access in cognitive radio networks using statistical radio environment maps

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