A Deep Learning Approach to Radio Signal Denoising

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy.

[1]  Branka Vucetic,et al.  Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach , 2018, IEEE Transactions on Signal Processing.

[2]  Timothy J. O'Shea,et al.  Deep Learning Based MIMO Communications , 2017, ArXiv.

[3]  Ljiljana Trajkovic,et al.  Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Classification Algorithms , 2018 .

[4]  Donghoon Lee,et al.  Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography , 2018 .

[5]  T. Charles Clancy,et al.  Convolutional Radio Modulation Recognition Networks , 2016, EANN.

[6]  Timothy J. O'Shea,et al.  Spectral detection and localization of radio events with learned convolutional neural features , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[7]  Sourangsu Banerji,et al.  On IEEE 802.11: Wireless LAN Technology , 2013, ArXiv.

[8]  Jun Won Choi,et al.  Deep neural network-based automatic modulation classification technique , 2016, 2016 International Conference on Information and Communication Technology Convergence (ICTC).

[9]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[10]  Alfred Mertins,et al.  Signal Analysis: Wavelets, Filter Banks, Time-Frequency Transforms and Applications , 1999 .

[11]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[12]  T. Charles Clancy,et al.  Over-the-Air Deep Learning Based Radio Signal Classification , 2017, IEEE Journal of Selected Topics in Signal Processing.

[13]  Benoît Champagne,et al.  An EM Approach for Cooperative Spectrum Sensing in Multiantenna CR Networks , 2016, IEEE Transactions on Vehicular Technology.

[14]  Jeroen Wigard,et al.  Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[15]  Dong In Kim,et al.  DCCC-MAC: A Dynamic Common-Control-Channel-Based MAC Protocol for Cellular Cognitive Radio Networks , 2016, IEEE Transactions on Vehicular Technology.

[16]  Vishnu Raj,et al.  Spectrum Access In Cognitive Radio Using a Two-Stage Reinforcement Learning Approach , 2017, IEEE Journal of Selected Topics in Signal Processing.

[17]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[18]  Yu-Dong Yao,et al.  Modulation Classification Based on Signal Constellation Diagrams and Deep Learning , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[20]  Shih Yu Chang,et al.  Determination of Wireless Networks Parameters through Parallel Hierarchical Support Vector Machines , 2012, IEEE Transactions on Parallel and Distributed Systems.

[21]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[22]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[23]  Janne J. Lehtomäki,et al.  Simple Primary User Signal Area Estimation for Spectrum Measurement , 2016, IEICE Trans. Commun..

[24]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[25]  Ketan Kotecha,et al.  Performance of Vehicle-to-Vehicle Communication using IEEE 802.11p in Vehicular Ad-hoc Network Environment , 2013, ArXiv.

[26]  A. Maćkiewicz,et al.  Principal Components Analysis (PCA) , 1993 .

[27]  Rodney W. Johnson,et al.  Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy , 1980, IEEE Trans. Inf. Theory.

[28]  Roland Memisevic,et al.  On autoencoder scoring , 2013, ICML.

[29]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[30]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[31]  Stephan ten Brink,et al.  Deep Learning Based Communication Over the Air , 2017, IEEE Journal of Selected Topics in Signal Processing.

[32]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[33]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[34]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .