An autoregressive approach for spectrum occupancy modeling and prediction based on synchronous measurements

Görçin, Ali; Celebi, Hasari; Qaraqe, Khalid; Arslan, Hüseyin

doi:10.1109/pimrc.2011.6140056

Cited by 27 publications

(20 citation statements)

References 32 publications

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“…In [64] and [65], the duty cycle was predicted by using the AR model with logit transformations of the linearly combined binary spectrum occupancy rates to account for the fact that the duty cycle is between 0 and 1. Four locations for the GSM band were examined and a model order of p = 3 was shown to have acceptable performances.…”

Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

208

105

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Section: Spectrum Occupancy Predictionmentioning

confidence: 99%

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Chen

2016

IEEE Commun. Surv. Tutorials

208

105

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“…In order to prepare a solid basis for our discussions in the next section, we will briefly present the most common approaches of modelling spectrum usage across the three orthogonal dimensions of time, frequency and space, as illustrated in Fig. 2 [19], [22], [89], [91]- [93]. Explicitly, in Fig.…”

Section: B Models Of Spectrum Usagementioning

confidence: 99%

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

154

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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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“…This could contribute to spectral efficiency improvement efforts [3][4][5]. The prediction information of the channel status can be used by secondary users (SUs) to decide the sensing periods and channel occupancy duration for a single channel sensing scenario [6].…”

Section: Introductionmentioning

confidence: 99%

“…Different initiatives for radioelectric spectrum channel modeling, mainly using deterministic and stochastic models, have been proposed [3,[10][11][12][13][14][15][16]. Differences among those proposals and the present work lie in that in the first initiatives, the duty cycle (the average percentage of the channel utilization) time series is modeled for different types of channels, whereas the proposal presented here models the time series for received power in three GSM channels with different occupancy levels.…”

Section: Introductionmentioning

confidence: 99%

Linear Algorithms for Radioelectric Spectrum Forecast

et al. 2016

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This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs) in a cognitive radio (CR) network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs) and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.

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An autoregressive approach for spectrum occupancy modeling and prediction based on synchronous measurements

Cited by 27 publications

References 32 publications

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

A Survey of Measurement-Based Spectrum Occupancy Modeling for Cognitive Radios

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Linear Algorithms for Radioelectric Spectrum Forecast

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