Prediction-Based Spectrum Access Optimization in Cognitive Radio Networks

Zuo, Peiliang; Wang, Xing; Linghu, Wangdan; Sun, Rong; Peng, Tao; Wang, Wenbo

doi:10.1109/pimrc.2018.8580726

Cited by 25 publications

(9 citation statements)

References 15 publications

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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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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 has been extensively studied in the literature by employing various techniques [7], such as Hidden Markov Model (HMM) [18], [22], ANN [14], [20], Long Short-Term Memory (LSTM) [17], Autoregressive (AR) model [19], etc. In [14] the authors tried to predict the future occupancy states of a channel by designing a Multi-Layer Perceptron (MLP) with backpropagation (BP).…”

Section: Related Workmentioning

confidence: 99%

“…The Bayesian regularization [32] is employed as a training algorithm, since it is one of the strong BP training methods, preventing the network from over-fitting. More particularly, the Bayesian regularization introduces an extra parameter to the cost function in (17):…”

Section: ) Artificial Neural Network (Ann)mentioning

confidence: 99%

Novel QoS-Aware Proactive Spectrum Access Techniques for Cognitive Radio Using Machine Learning

et al. 2019

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Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q-learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q-learning assisted second strategy enhanced the full-satisfaction by up to 95.7%. INDEX TERMS Cognitive radio networks, dynamic spectrum access, machine learning, self-organizing networks, Q-learning.

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“…The secondary users (SU) are able to distinguish the availability of the shared spectrum through spectrum sensing. The SUs are able to foresee active status of the primary users (PU) and schedule their access in advance with spectrum prediction [1]. The spectrum prediction step generates an initial guess on the availability of an idle channel by a training process based on historical spectrum sensing data.…”

Section: Introductionmentioning

confidence: 99%

Concept Drift Detection Methods for Deep Learning Cognitive Radios: A Hardware Perspective

Shahabuddin

Khan

Juntti

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Deep learning models usually assume that training dataset and target data have the same distribution. If this is not the case, model mismatch causes performance degradation when the model is used with the real data. With radio frequency (RF) measurements from real data traffic, the exact distribution of the measurements is unknown in many cases and model mismatch is unavoidable. This is known as concept drift, or model misspecification in deep learning, which we are interested in for cognitive radio dynamic spectrum access predictions. In this paper, we present three concept drift detection methods and their corresponding very large scale integration (VLSI) circuits. The circuits are mapped on a Xilinx Virtex-7 field-programmable gate array (FPGA) and the resource utilization results are provided.

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Prediction-Based Spectrum Access Optimization in Cognitive Radio Networks

Cited by 25 publications

References 15 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

Novel QoS-Aware Proactive Spectrum Access Techniques for Cognitive Radio Using Machine Learning

Concept Drift Detection Methods for Deep Learning Cognitive Radios: A Hardware Perspective

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