Prediction based channel allocation performance for cognitive radio

Barnes, Simon Daniel; Maharaj, B. T.

doi:10.1016/j.aeue.2013.09.009

Cited by 27 publications

(17 citation statements)

References 15 publications

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“…Explicit duration semi-Markov chains with generalised distribution of duty cycle time modelled primary user's inter-arrival time in [85,86], while continuous time Markov chain modelled primary user behaviour in [87,88]. Moreover, hidden Markov model received wide attention in spectrum occupancy prediction literature [9,14,83,[89][90][91][92][93]. Liu et al addressed the prediction confidence, and the error of a continuous time Markov chain model with Erlang-2 distribution model for primary user's activity [94].…”

Section: Hidden Markov Model (Hmm) Is Partially Observablementioning

confidence: 99%

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

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Section: Hidden Markov Model (Hmm) Is Partially Observablementioning

confidence: 99%

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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“…This problem needs to be taken seriously, since PU traffic prediction needs to be performed quickly and accurately for it to be useful [8].…”

Section: Traffic Modellingmentioning

confidence: 99%

“…It has also been shown in the literature that it is beneficial for SUs to be able to make proactive decisions about spectrum resource allocation [17,25,8], both in terms of accuracy in channel selection and in the potential power savings for the entire CRN [11]. To be able to make these proactive decisions, a SU will need to be able to make predictions about the future behaviour of other users of the same spectrum.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Prediction for Cognitive Radio Networks

Barnes

Maharaj

Alfa

2016

Wireless Pers Commun

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Combining spectrum sensing (SS) and primary user (PU) traffic forecasting provides a cognitive radio network (CRN) with a platform from which informed and proactive operational decisions can be made. The success of these decisions is largely dependent on prediction accuracy. Allowing secondary users (SU) to perform these predictions in a collaborative manner allows for an improvement in the accuracy of this process, since individual SUs may suffer from SS and prediction inaccuracies due to poor channel conditions. To overcome these problems a collaborative approach to forecasting PU traffic behaviour, that combines SS and forecasting through SU cooperation, has been proposed in this article. Both pre-fusion and post-fusion scenarios for cooperative prediction were investigated and a number of binary prediction methods were considered (including the authors' own simple technique). Cooperative prediction performance was investigated, under various PU traffic conditions, for a group of ten SUs experiencing different channel conditions and a sub-optimal cooperative forecasting algorithm was proposed to minimise cooperative prediction error. Simulation results indicated that the accuracy of the prediction methods was influenced by the PU traffic pattern and that cooperative prediction lead to a significant improvement in prediction accuracy under most of the traffic conditions considered. However, this came at the cost of increased computational complexity. The pre-fusion scenario was found to be the most accurate scenario (up to 25 % improvement), but was also eleven times more complex than when no fusion was employed. The cooperative forecasting algorithm was found to further improve these results.

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“…[15], [93], [128], [129], [139], [187], [220] use various linear prediction techniques, such as AR, MA, ARMA and ARIMA to perform spectrum prediction, where the output is used to improve the sensing accuracy and reduce the sensing cost. In parallel, Markov models such as HMM [14], [17], [20], [95], [111], [118], [140], [142], [144], [146], [218], [221], [222] and POMDP [153] are also widely used to perform similar tasks. These kinds of models work well under the assumption of memoryless or Markov property existing in the spectrum state evolution.…”

Section: A Spectrum Inference For Sensingmentioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

159

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Abstract-Spectrum inference, also known as spectrum prediction in the literature, is a promising technique of inferring the occupied/free state of radio spectrum from already known/measured spectrum occupancy statistics by effectively exploiting the inherent correlations among them. In the past few years, spectrum inference has gained increasing attention owing to its wide applications in cognitive radio networks (CRNs), ranging from adaptive spectrum sensing, and predictive spectrum mobility, to dynamic spectrum access and smart topology control, to name just a few. In this paper, we provide a comprehensive survey and tutorial on the recent advances in spectrum inference. Specifically, we first present the preliminaries of spectrum inference, including the sources of spectrum occupancy statistics, the models of spectrum usage, and characterize the predictability of spectrum state evolution. By introducing the taxonomy of spectrum inference from a time-frequency-space perspective, we offer an in-depth tutorial on the existing algorithms. Furthermore, we provide a comparative analysis of various spectrum inference algorithms and discuss the metrics of evaluating the efficiency of spectrum inference. We also portray the various potential applications of spectrum inference in CRNs and beyond, with an outlook to the 5th generation mobile communications (5G) and next generation high frequency (HF) communications systems. Last but not least, we highlight the critical research challenges and open issues ahead.

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Prediction based channel allocation performance for cognitive radio

Cited by 27 publications

References 15 publications

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Cooperative Prediction for Cognitive Radio Networks

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

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