Robust Spectral-Temporal Two-Dimensional Spectrum Prediction

Ding, Guoru; Zhai, Siyu; Chen, Xiaoming; Zhang, Yuming; Liu, Chao

doi:10.1007/978-3-319-52730-7_40

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…E U/O authors of [209]- [211] proposed various joint two-dimensional spectral-temporal spectrum prediction algorithms by leveraging the low-rank nature of the spectral data. The authors of [213] developed a multi-dimensional (time, frequency, and space) spectral map construction method.…”

Section: T Imentioning

confidence: 99%

“…In [210], the authors develop an algorithm for spectral-temporal two-dimensional spectrum prediction with incomplete historical data by exploiting the approximate lowrank property of real-world spectrum measurement matrices. In [211], a robust spectral-temporal two-dimensional spectrum prediction algorithm is proposed under an assumption on the sparsity of abnormal or corrupted historical data. In [212], a matrix completion-based algorithm is developed for TV white space database construction via joint spectrum sensing in time domain and spectrum inference in spatial domain.…”

Section: Spectrum Inference In Joint Multiple Domainsmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Inference in Cognitive Radio Networks: Algorithms and Applications

Ding

Jiao

Wang

et al. 2018

IEEE Commun. Surv. Tutorials

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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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Section: T Imentioning

confidence: 99%

Section: Spectrum Inference In Joint Multiple Domainsmentioning

confidence: 99%