Long-Term Spectrum State Prediction: An Image Inference Perspective

Sun, Jiachen; Wang, Jinlong; Ding, Guoru; Shen, Liang; Yang, Jian; Wu, Qihui; Yu, Ling

doi:10.1109/access.2018.2861798

Cited by 34 publications

(22 citation statements)

References 21 publications

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“…Recently, tensor models are also introduced to jointly capture dependencies in time and frequency domains as well as the daily periodicity of the spectrum data. Then, based on it, tensor completion algorithms [24], [25] are developed for long-term spectrum prediction in the presence of missing data and anomalies. Despite their good performance on recovering missing values and separating anomalies, the computational complexity of the batch algorithms can easily become large.…”

Section: B Challengesmentioning

confidence: 99%

“…It provides valuable intuitions for the optimization part in this paper. For the 3-D baseline methods, we select the most recent work in literature, i.e., HaLRTC (high accuracy low rank tensor completion) algorithm in [24]. Notably, the proposed SDR-HTC framework is quite different from HaLRTC at least in two aspects: i) SDR-HTC is robust to anomalies, while HaLRTC is designed without considering the impact of anomalies; ii) SDR-HTC works in an online manner, while HaLRTC is a batch algorithm.…”

Section: A Comparison With the State-of-the-art Spectrum Data Recovementioning

confidence: 99%

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Recovering Missing Values From Corrupted Historical Observations: Approaching the Limit of Predictability in Spectrum Prediction Tasks

Chen

et al. 2020

IEEE Access

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Spectrum prediction is a key enabler of a range of emerging applications, from adaptive spectrum sensing to agile and proactive decision making, for dynamic spectrum access. Due to the fact that spectrum sensors easily experience an issue of missing readings and anomaly pollution, spectrum prediction with incomplete and corrupted historical observations has caused extensive concern. In this paper, we aim to tackle the challenging problem on how to accurately and efficiently recover the missing values from corrupted historical spectrum observations for approaching the limit of predictability in the spectrum prediction tasks. Specially, we first formulate a hankelized time-structured spectrum tensor that can naturally preserve both spectral and temporal dependencies among the historical spectrum observations. We then model the spectrum data recovery problem as tensor completion with its latent low-rank structure and sparse anomaly property. To efficiently solve the optimization problem, we design a robust online spectrum data recovery algorithm based on the alternating direction method. In addition, we also introduce the concept of maximum predictability to reveal the harmful effects of missing data and anomalies, as well as to evaluate the effectiveness of our proposed algorithm from an information theory perspective. Extensive experimental evaluations on the real-world spectrum observation dataset show that the proposed algorithm achieves significantly better spectrum data recovery performance in terms of both recovery accuracy and computation efficiency. It also indicates that not only the predictability of the frequency bands but also the prediction accuracy of any predictive algorithm can be improved by the proposed algorithm.

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Section: B Challengesmentioning

confidence: 99%

Section: A Comparison With the State-of-the-art Spectrum Data Recovementioning

confidence: 99%

Recovering Missing Values From Corrupted Historical Observations: Approaching the Limit of Predictability in Spectrum Prediction Tasks

Chen

et al. 2020

IEEE Access

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“…Furthermore, paper [ 16 ] realizes the reconstruction by building a Continuous to Finite (CTF) module. Similar to the tensor completion problem [ 34 , 35 ], the observation matrix in the CTF module also has a low-rank property.…”

Section: System Model and Problem Statementmentioning

confidence: 99%

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

Bai

Huang

et al. 2018

Sensors

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The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.

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“…However, a lot of calculations are required for determining the frequent pattern which may vary in different environments. Sun et al [19] converted the spectrum data to two-dimensional image space for time-frequency spectrum prediction. This method is based on historical multi-day data to predict the use of spectrum in the next day, with limited prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

Wang

Yuan-cheng

et al. 2019

Information

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Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance.

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Long-Term Spectrum State Prediction: An Image Inference Perspective

Cited by 34 publications

References 21 publications

Recovering Missing Values From Corrupted Historical Observations: Approaching the Limit of Predictability in Spectrum Prediction Tasks

Recovering Missing Values From Corrupted Historical Observations: Approaching the Limit of Predictability in Spectrum Prediction Tasks

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

Wideband Spectrum Sensing Method Based on Channels Clustering and Hidden Markov Model Prediction

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