Prediction-Based Spectrum Management in Cognitive Radio Networks

Zhao, Yanxiao; Zhiming, Hong; Luo, Yu; Wang, Guodong; Pu, Liang

doi:10.1109/jsyst.2017.2741448

Cited by 39 publications

(21 citation statements)

References 32 publications

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“…Recently, with notable progress in the spectrum data mining and the artificial intelligence (AI), many advanced spectrum management models were proposed. A prediction-based spectrum management scheme was proposed in [26], which adopted the spectrum prediction and users' mobility prediction to improve multiple system performance metrics. In [27], a smart spectrum management model for vehicle-to-everything communication was proposed, which combined spectrum measurement, modeling, and database to assist vehicles with spectrum information.…”

Section: B Dsm and The Transitions To Ssmmentioning

confidence: 99%

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

et al. 2020

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Spectrum is the indispensable resource for 5G wireless systems and beyond. The dynamic spectrum management (DSM) framework with spectrum sharing among different kinds of users is essential to satisfy the sustainable rapid increase of the bandwidth requirement. Based on the analysis of the DSM as well as the latent applications of the machine learning and artificial intelligence (AI) in the spectrum access, this paper will address the problem of incorporating intelligence into the spectrum management. Firstly, the spectrum opportunity (SOP) related information is layered into the spectrum data, the spectrum information and the spectrum knowledge based on the processing and abstractive level. The spectrum knowledge is formally defined as the extendible and scalable information to reason and predict the SOP usability as well as the outcome of the SOP occupancy. Then the smart spectrum model (SSM) is proposed with the spectrum knowledge as the key enabler and the coupled SOP exploration and exploitation as the core feature. Under the SSM framework, the spectrum knowledge and real-time observing (SKRO) enabled SOP exploration scheme is designed, which can make use of both the historical information and the real-time sensing information for smart SOP exploration. Extensive simulations are provided which demonstrate that the SKRO enabled SSM can achieve much better SOP utilization while satisfying the required legacy assurance. Specifically, in the low signal to noise environment, at least 16.55% gains on the SOP utilization ratio can be obtained compared with the DSM.

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Section: B Dsm and The Transitions To Ssmmentioning

confidence: 99%

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

et al. 2020

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“…Briefly speaking, spectrum sensing detects the current spectrum state through signal detection, whereas spectrum prediction infers future spectrum state evolution trajectory by fully exploiting the inherent statistical dependencies and regularities among historical observations. With the ability to foresee the state evolution of radio spectrum, spectrum prediction has attracted much attention during the past few years [1], [2], and has been widely developed for, such as, adaptive sensing scheduling [3], agile decision making [4], and proactive channel switching [5], in CR networks. So far, a diverse group of spectrum prediction methods have been proposed, e.g.…”

Section: A Backgroundmentioning

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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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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“…In HCRNs, when the channel performance deteriorates, or the SUs' interference with PU exceeds the PU's tolerance threshold, SUs have to vacate and switch to a new target channel to continue data transmission. According to the decision timing for selecting target channels, spectrum handoff methods can be classified into the reactive decision based and the proactive (or predictive) decision-based handoffs [11][12][13]. Predictive decision can select a series of prospective backup vacant target channels before spectrum handoff occurs, which can save substantial sensing time.…”

Section: Introductionmentioning

confidence: 99%

“…Predictive decision can select a series of prospective backup vacant target channels before spectrum handoff occurs, which can save substantial sensing time. Therefore, the predictive decision-based spectrum handoff schemes have become a research focus of CRNs [11,13]. In [14], Hoque et al established an analytical mode for the probability of spectrum handoff and further derived an analytical expression for average spectrum handoff number for a SU based on the residual time distributions of spectrum holes, and investigated the effect of spectrum handoff delay on the performance of spectrum mobility in CRNs.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

Cao

Qian

2020

Sensors

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Spectrum handoff is one of the key techniques in a cognitive radio system. In order to improve the agility and the reliability of spectrum handoffs as well as the system throughput in hybrid cognitive radio networks (HCRNs) combing interweave mode with underlay mode, a predictive (or proactive) spectrum handoff scheme based on a deep Q-network (DQN) for HCRNs is proposed in this paper. In the proposed spectrum handoff approach, spectrum handoff success rate is introduced into an optimal spectrum resource allocation model to ensure the reliability of spectrum handoff, and the closed-form expression for the spectrum handoff success rate is obtained based on the Poisson distribution. Furthermore, we exploit the transfer learning strategy to further improve the DQN learning process and finally achieve a priority sequence of target available channels for spectrum handoffs, which can maximize the overall HCRNs throughput while satisfying constraints on secondary users’ interference with primary user, limits on the spectrum handoff success rate, and the secondary users’ performance requirements. Simulation results show that the proposed spectrum handoff scheme outperforms the state-of-the-art spectrum handoff algorithms based on predictive decision in terms of the convergence rate, the handoff success rate and the system throughput.

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Prediction-Based Spectrum Management in Cognitive Radio Networks

Cited by 39 publications

References 32 publications

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

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

Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks

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