What can Machine Learning do for Radio Spectrum Management?

The opening of the unlicensed radio spectrum creates new opportunities and new challenges for communication technology that can be faced by Machine Learning techniques. In this work, we discuss the potential benefits and the challenges with reference to the recent research developments in this area. Applications go from channel estimation to Signal quality control, and from signal classification to action control. We survey Machine learning and Deep Learning algorithms with possible radio applications and highlight the corresponding challenges.

[1]  Stephan ten Brink,et al.  On deep learning-based channel decoding , 2017, 2017 51st Annual Conference on Information Sciences and Systems (CISS).

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

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

[4]  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.

[5]  Sudeep Pasricha,et al.  Context-Aware Energy Enhancements for Smart Mobile Devices , 2014, IEEE Transactions on Mobile Computing.

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

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

[8]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[9]  Stephan ten Brink,et al.  Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[10]  Andrea J. Goldsmith,et al.  Detection Algorithms for Communication Systems Using Deep Learning , 2017, ArXiv.

[11]  Eduardo F. Morales,et al.  An Introduction to Reinforcement Learning , 2011 .

[12]  Liang Jiang,et al.  Deep Neural Network Probabilistic Decoder for Stabilizer Codes , 2017, Scientific Reports.

[13]  Shauna Revay,et al.  Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks , 2018, IEEE Journal of Selected Topics in Signal Processing.

[14]  Srinivasan Seshan,et al.  RFDump: an architecture for monitoring the wireless ether , 2009, CoNEXT '09.

[15]  David Burshtein,et al.  Deep Learning Methods for Improved Decoding of Linear Codes , 2017, IEEE Journal of Selected Topics in Signal Processing.

[16]  Marco Gruteser,et al.  Wireless device identification with radiometric signatures , 2008, MobiCom '08.

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

[18]  Tim Schenk,et al.  RF Imperfections in High-rate Wireless Systems , 2008 .

[19]  Qixun Zhang,et al.  Proactive Radio Resource Optimization With Margin Prediction: A Data Mining Approach , 2017, IEEE Transactions on Vehicular Technology.

[20]  Tim Schenk,et al.  RF Imperfections in High-rate Wireless Systems: Impact and Digital Compensation , 2008 .

[21]  Joel Emer,et al.  Eyeriss: an Energy-efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks Accessed Terms of Use , 2022 .

[22]  Frank L. Lewis,et al.  Hybrid control for a class of underactuated mechanical systems , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[23]  Kwang-Cheng Chen,et al.  Cognitive Radio Network Tomography , 2010, IEEE Transactions on Vehicular Technology.

[24]  Sachin Katti,et al.  DOF: a local wireless information plane , 2011, SIGCOMM.

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

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

[27]  Timothy J. O'Shea,et al.  Unsupervised representation learning of structured radio communication signals , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).

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

[29]  Andrea Goldsmith.  Joint source/channel coding for wireless channels , 1995, 1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century.

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

[31]  Bariscan Yonel,et al.  Deep Learning for Passive Synthetic Aperture Radar , 2017, IEEE Journal of Selected Topics in Signal Processing.

[32]  Ingrid Moerman,et al.  End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications , 2017, IEEE Access.

[33]  Nikos D. Sidiropoulos,et al.  Learning to optimize: Training deep neural networks for wireless resource management , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

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

[35]  Klaus Moessner,et al.  Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE-Based Macro/Femtocell Deployments , 2015, IEEE Transactions on Wireless Communications.

[36]  Sofie Pollin,et al.  Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors , 2017, IEEE Transactions on Cognitive Communications and Networking.

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

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

[39]  Zhengqing Yun,et al.  Machine learning for source localization in urban environments , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.

[40]  Ephraim Zehavi,et al.  8-PSK trellis codes for a Rayleigh channel , 1992, IEEE Trans. Commun..

[41]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[42]  Zhu Han,et al.  Machine Learning Paradigms for Next-Generation Wireless Networks , 2017, IEEE Wireless Communications.

[43]  Yair Be'ery,et al.  RNN Decoding of Linear Block Codes , 2017, ArXiv.

[44]  Muhammad Ali Imran,et al.  A Cell Outage Management Framework for Dense Heterogeneous Networks , 2016, IEEE Transactions on Vehicular Technology.

[45]  Dong Chao,et al.  Universal Software Radio Peripheral , 2010 .

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

[47]  Kiran Karra,et al.  Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention , 2016, 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[48]  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.

[49]  Rong Zheng,et al.  Binary Inference for Primary User Separation in Cognitive Radio Networks , 2010, IEEE Transactions on Wireless Communications.

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

[51]  Timothy J. O'Shea,et al.  Semi-supervised radio signal identification , 2016, 2017 19th International Conference on Advanced Communication Technology (ICACT).

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

[53]  Zhe Chen,et al.  Cognitive Radio Network for the Smart Grid: Experimental System Architecture, Control Algorithms, Security, and Microgrid Testbed , 2011, IEEE Transactions on Smart Grid.

[54]  Neelesh B. Mehta,et al.  Transmit Power Control Policies for Energy Harvesting Sensors With Retransmissions , 2013, IEEE Journal of Selected Topics in Signal Processing.

[55]  Namyoon Lee,et al.  Blind detection for MIMO systems with low-resolution ADCs using supervised learning , 2016, 2017 IEEE International Conference on Communications (ICC).

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

[57]  Asoke K. Nandi,et al.  Automatic Modulation Classification Using Combination of Genetic Programming and KNN , 2012, IEEE Transactions on Wireless Communications.

[58]  Setareh Maghsudi,et al.  Channel Selection for Network-Assisted D2D Communication via No-Regret Bandit Learning With Calibrated Forecasting , 2014, IEEE Transactions on Wireless Communications.

[59]  Ekram Hossain,et al.  Estimation of Primary User Parameters in Cognitive Radio Systems via Hidden Markov Model , 2013, IEEE Transactions on Signal Processing.

[60]  Henk Wymeersch,et al.  Iterative Receiver Design , 2007 .

[61]  Martín Abadi,et al.  Learning to Protect Communications with Adversarial Neural Cryptography , 2016, ArXiv.

[62]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[63]  Shi Jin,et al.  Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning , 2015, IEEE Transactions on Wireless Communications.

[64]  Warren J. Gross,et al.  Neural offset min-sum decoding , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[65]  Timothy J. O'Shea,et al.  Radio Machine Learning Dataset Generation with GNU Radio , 2016 .

[66]  H. Harai,et al.  Optical and wireless hybrid access networks: Design and optimization , 2012, IEEE/OSA Journal of Optical Communications and Networking.

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

[68]  Jakob Hoydis,et al.  An Introduction to Deep Learning for the Physical Layer , 2017, IEEE Transactions on Cognitive Communications and Networking.

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