6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented.

[1]  Walid Saad,et al.  Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage , 2016, IEEE Communications Letters.

[2]  Walid Saad,et al.  Echo-Liquid State Deep Learning for 360° Content Transmission and Caching in Wireless VR Networks With Cellular-Connected UAVs , 2018, IEEE Transactions on Communications.

[3]  Mehdi Bennis,et al.  Wireless Network Intelligence at the Edge , 2018, Proceedings of the IEEE.

[4]  Mehdi Bennis,et al.  Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[5]  Rangeet Mitra,et al.  Least Minimum Symbol Error Rate Based Post-Distortion for VLC Using Random Fourier Features , 2020, IEEE Communications Letters.

[6]  Robert Abbas,et al.  Machine Learning based Anomaly Detection for 5G Networks , 2020, ArXiv.

[7]  Mehdi Bennis,et al.  Remote UAV Online Path Planning via Neural Network-Based Opportunistic Control , 2019, IEEE Wireless Communications Letters.

[8]  Hong Liu,et al.  Securing wireless communications of connected vehicles with artificial intelligence , 2017, 2017 IEEE International Symposium on Technologies for Homeland Security (HST).

[9]  Mehdi Bennis,et al.  GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning , 2019, J. Mach. Learn. Res..

[10]  Rudolf Mathar,et al.  A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[11]  H. Vincent Poor,et al.  Experienced Deep Reinforcement Learning With Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication , 2019, IEEE Transactions on Communications.

[12]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[13]  Walid Saad,et al.  Data Correlation-Aware Resource Management in Wireless Virtual Reality (VR): An Echo State Transfer Learning Approach , 2019, IEEE Transactions on Communications.

[14]  Geoffrey Ye Li,et al.  Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems , 2017, IEEE Wireless Communications Letters.

[15]  Julian Ereth,et al.  DataOps - Towards a Definition , 2018, LWDA.

[16]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[17]  Mihaela van der Schaar,et al.  Jamming Bandits—A Novel Learning Method for Optimal Jamming , 2016, IEEE Transactions on Wireless Communications.

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

[19]  Yiran Chen,et al.  A Survey of Accelerator Architectures for Deep Neural Networks , 2020 .

[20]  Christian Wietfeld,et al.  Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction , 2019, IEEE Transactions on Intelligent Transportation Systems.

[21]  H. Vincent Poor,et al.  Convergence Time Optimization for Federated Learning Over Wireless Networks , 2020, IEEE Transactions on Wireless Communications.

[22]  Mérouane Debbah,et al.  Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? , 2019, IEEE Transactions on Communications.

[23]  Mihaela van der Schaar,et al.  Machine Learning in the Air , 2019, IEEE Journal on Selected Areas in Communications.

[24]  S. D. Erokhin,et al.  Machine learning approach on synchronization for FEC enabled channels , 2018, 2018Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO).

[25]  Ahmad Shawahna,et al.  FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review , 2019, IEEE Access.

[26]  Vimal Bhatia,et al.  Mixture-Kernel Based Post-Distortion in RKHS for Time-Varying VLC Channels , 2019, IEEE Transactions on Vehicular Technology.

[27]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[28]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[29]  Walid Saad,et al.  Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks , 2018, IEEE Transactions on Wireless Communications.

[30]  R. M. A. P. Rajatheva,et al.  Fast Uplink Grant for Machine Type Communications: Challenges and Opportunities , 2018, IEEE Communications Magazine.

[31]  Zhuo Sun,et al.  Deep Learning-based Frame and Timing Synchronization for End-to-End Communications , 2019, Journal of Physics: Conference Series.

[32]  Walid Saad,et al.  Virtual Reality Over Wireless Networks: Quality-of-Service Model and Learning-Based Resource Management , 2017, IEEE Transactions on Communications.

[33]  Yalin E. Sagduyu,et al.  Deep Learning for Launching and Mitigating Wireless Jamming Attacks , 2018, IEEE Transactions on Cognitive Communications and Networking.

[34]  Takayuki Nishio,et al.  Extreme URLLC: Vision, Challenges, and Key Enablers , 2020, ArXiv.

[35]  R. M. A. P. Rajatheva,et al.  Sleeping Multi-Armed Bandits for Fast Uplink Grant Allocation in Machine Type Communications , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[36]  Mehdi Bennis,et al.  Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory , 2020, IEEE Transactions on Communications.

[37]  Yair Be'ery,et al.  Learning to decode linear codes using deep learning , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[38]  Walid Saad,et al.  A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.

[39]  Ninghui Sun,et al.  DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning , 2014, ASPLOS.

[40]  Mehdi Bennis,et al.  Edge computing meets millimeter-wave enabled VR: Paving the way to cutting the cord , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[41]  Athina P. Petropulu,et al.  A Deep Learning Framework for Optimization of MISO Downlink Beamforming , 2019, IEEE Transactions on Communications.

[42]  Fredrik Gunnarsson,et al.  LTE release 14 outlook , 2016, IEEE Communications Magazine.

[43]  Rahim Tafazolli,et al.  Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[44]  Ghaya Rekaya-Ben Othman,et al.  DNN assisted Sphere Decoder , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

[45]  Walid Saad,et al.  Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).