Spectrum Prediction Based on Taguchi Method in Deep Learning With Long Short-Term Memory

Yu, Ling; Chen, Jin; Ding, Guoru; Tu, Ya; Yang, Jian; Sun, Jiachen

doi:10.1109/access.2018.2864222

Cited by 77 publications

(39 citation statements)

References 32 publications

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“…For instance, authors in [51] proposed a spectrum prediction algorithm using LSTM network while, authors in [52] addressed the modulation classification problem using LSTM network. In [53], the authors have used the Taguchi method for hyperparameter optimization of the LSTM network for spectrum prediction. Additionally, the authors in [54] carried out the mobile traffic prediction using the LSTM network.…”

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

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The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time.

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Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

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“…In the last subsection, we compare the prediction accuracy of several predictive algorithms on both the unrecovered spectrum data sequences and the recovered spectrum data sequences obtained via our proposed SDR-HTC algorithm. We employ five predictors: 1) simple forecasting strategy: EWMA (Exponential Weighted Moving Average) [23], 2) time series regression: KNN (K-Nearest Neighbors) [25], 3) autoregressive method: SARIMA (Seasonal Autoregressive Integrated Moving Average) [36], 4) machine learning: MLP (Multilayer Perceptron) [37] and 5) deep recurrent neural network: LSTM (Long Short-Term Memory) [38] for predicting the frequency point evolution, and compare their performance given the value of the maximum predictability. We evaluate the case where p miss = 0.5 and p anomaly = 0.2, and randomly select a frequency point within the 20MHz ∼ 1520MHz frequency region for the following evaluation.…”

Section: Comparison Of Predictive Algorithmsmentioning

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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“…More recent approaches include the application of deep learning by modeling the CQP problem as a supervised machine learning problem. For instance, [8] uses Taguchi optimization, and long short-term memory networks (LSTMs) for spectrum prediction, specifically for channel quality as well as channel occupancy. A similar approach has been proposed in [9] for 5G, where convolutional neural networks (CNNs) and LSTMs are used for making predictions.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for Channel Quality Prediction

Parera

Redondi

Cesana

et al. 2019

2019 IEEE International Symposium on Measurements &Amp; Networking (M&N)

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Spectrum Prediction Based on Taguchi Method in Deep Learning With Long Short-Term Memory

Cited by 77 publications

References 32 publications

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

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

Transfer Learning for Channel Quality Prediction

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