Online Spectrum Prediction with Adaptive Threshold Quantization
In this paper, we explore the spectrum inference to achieve the spectrum occupancy in advance through analyzing the historical spectrum. We have conceived an offline-online cooperative framework. Specifically, the hyperparameters can be achieved on an offline way, which will be used for online prediction. Moreover, based on the accuracy of online spectrum inference, the hyperparameters can be further optimized relying on specifically designed grid search and K-fold cross-validation combined
... od in an iterative manner. We present a long short-term memory (LSTM) aided spectrum occupancy prediction method, relying on adaptive threshold quantization aided data preprocessing (ATQ-DP). To be specific, first, the captured spectrum data may be quantized by the adaptive thresholds in order to lesson the influence of noise imposed on them, where the thresholds are obtained by kernel density estimation (KDE) method. Then, LSTM will be activated to perform spectrum prediction based on the quantized data, thus, future spectrum occupancy can be inferred in advance. Additionally, performance evaluations show that the accuracy of spectrum inference is always better than that of the LSTM aided spectrum inference relying on the traditional fixed threshold quantization aided data preprocessing (FTQ-DP), where the FTQ-DP is used for comparison purposes. INDEX TERMS Adaptive threshold quantization, spectrum prediction, long short-term memory (LSTM). VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ HAOYU LI received the B.S. degree in communication engineering from the Huaihai Institute of Technology, Lianyungang, China, in 2017. He is currently pursuing the M.S. degree in satellite communication with the Satellite and Mobile Communication Section, Nanjing University of Posts and Telecommunications. His research interests include cooperative communications, spectrum inference, and deep learning.