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Yilmaz , Chae , Deng , J. O’Shea , Dai , Lee , and Hoydis: JCN June Special Issue on Advances and Applications of Artificial Intelligence and Machine Learning for Wireless Communications

H. Birkan Yilmaz , Chan-Byoung Chae , Yansha Deng , Timothy J. O’Shea , Linglong Dai , Namyoon Lee and Jakob Hoydis

JCN June Special Issue on Advances and Applications of Artificial Intelligence and Machine Learning for Wireless Communications

With recent advances, Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged to show great promise in the field of wireless communications. Although some researchers are skeptical due to issues concerning complexity and reliability, benefits include the near-optimal performance or the improvement over current state-of-the-art techniques. Luckily, the big data technology delivers an excellent advantage for studying the essential characteristics of wireless networks that can be integrated with AI and ML approaches. Moreover, the recent advances in deep learning, convolutional neural networks, and reinforcement learning hold significant promise. Indeed, they offer new design approaches for solving some challenging problems that, until recently, were considered intractable.

This special issue received 18 submitted manuscripts, out of which 10 papers have been accepted for publication. The editors would like to thank the authors of all papers for their submissions and special thanks go to the reviewers for their help in allowing us to complete the reviews and decisions in a timely fashion. The papers in this special issue will report research in Advances and Applications of Artificial Intelligence and Machine Learning for Wireless Communications with the following two aspects: Enhancing Communication Performance; Security, Privacy, and Anomaly Detection.

ML for Enhancing Communication Performance: This section includes 5 papers that propose new techniques that are incorporated with ML and AI techniques.

The first paper “Deep Learning-based Channel Estimation and Tracking for Millimeter-wave Vehicular Communications” by Intae Hwang et al. designs deep-learning based channel estimation method for mm-wave vehicular communications and following the channel estimation, long short-term memory is utilized to track the channel. Next, the paper “QoS Provision and Energy Saving Scheme for Distributed Cognitive Radio Networks Using Deep Learning” by Mduduzi Hlophe and B. T. Bajarah designs an effective spectrum management scheme that utilizes traffic prediction to improve the resource consumption efficiency while considering the energy budget. The paper “Collision Prediction for a Low Power Wide Area Network using Deep Learning Methods” by Shengmin Cui and Inwhee Joe presents collision prediction for a low power wide area networks (LPWANs) via long short- term memory extended Kalman filter model. The next paper, “Accelerating wireless channel autoencoders for short coherence-time communications” by M. E. Morocho-Cayamcela and Wansu Lim propose and present accelerated autoencoders for time-varying and short coherence-time channels via dynamic learning rates. Finally, the paper “UE Throughput Guaranteed Small Cell ON/OFF Algorithm with Machine Learning” by Tae-Yoon Park et al. introduce small cell on/off algorithm that aims to solve the throughput reduction during small cell off process via linear regression and correlation analysis.

ML for Security, Privacy, and Anomaly Detection: This section selects 5 papers that develop new security, anomaly detection, and privacy techniques for various communication systems and applications.

The first paper provides a survey of deep learning techniques that are utilized in telecommunications. In particular, “Adversarial Attack on DL_based Massive MIMO CSI Feedback” by Jin et al. presents usage of deep learning techniques for the channel coding, error correction, automatic modulation selection, and MIMO receiver design. Next, the paper “Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning” by Kwon et al. proposes to use supervised, unsupervised, and ensemble learning techniques for WiFi indoor localization applications. For unsupervised techniques, authors utilize isolation forest method, while the supervised techniques utilize SVM and KNN methods. The paper “AFRL: Adaptive Federated Reinforcement Learning for Intelligent Jamming Defense in FANET” by Mowla et al. presents the learning-based jamming defense that utilizes federated reinforcement learning for flying ad-hoc networks (FANET). The next paper, “Mobile Device-centric Approach for Identifying Problem Spot in Network using Deep Learning” by Kim et al. attacks to the problem of identifying problematic spots by the help of mobile nodes and deep learning. Determining the problem spots enables to decide and act more effectively for improving the QoS/QoE. Finally, the paper “Intelligent Network Data Analytics Function in 5G Cellular Networks using Machine Learning” by Sevgican et al. presents the machine learning techniques that can be easily integrated with NWDAF and the performance evaluations of the considered methods in terms of anomaly detection.


H. Birkan Yilmaz (M ’15)

H. Birkan Yilmaz (M ’15) is currently an Assistant Professor in the Department of Computer Engineering at Bogazici University, Istanbul, Turkey and a member of the Computer Networks Research Lab (NETLAB). He received his B.S. degree in Mathematics, and the M.Sc. and Ph.D. degrees in Computer Engineering from Bogazici University. He worked as a post-doctoral researcher at Yonsei Institute of Convergence Technology, Yonsei University, South Korea for four years and at Universitat Politecnica de Catalunya, Spain (via Beatriu de Pinos fellowship) for two years. His Erdős number is three. He was awarded TUBITAK National Ph.D. Scholarship during his Ph.D. studies and the Marie Sklodowska-Curie Actions Seal of Excellence in 2016. He has co-authored more than 75 ACM/IEEE journal and conference papers. He was the co-recipient of the Best Demo award in IEEE INFOCOM (2015) and Best Paper award in AICT (2010) and ISCC (2012). He is currently in the editorial board of IEEE Wireless Communications Letters and IEEE Transactions on Molecular, Biological, and Multi-Scale Communications. He is awarded certificates for the Exemplary Reviewers of the IEEE Wireless Communications Letters in 2014, 2017, and 2019. He has also served as a TPC Member for various IEEE conferences, such as IEEE ICCVE, PIMRC, GLOBECOM, WCNC, and ICC. His research interests include cognitive radio, spectrum sensing, molecular communications, and detection   estimation theory. He is a member of IEEE and TMD (Turkish Mathematical Society).


Chan-Byoung Chae (S'06 - M'09 - SM'12)

Chan-Byoung Chae (S'06 - M'09 - SM'12) is an Underwood Distinguished Professor in the School of Integrated Technology, Yonsei University, Korea. Before joining Yonsei University, he was with Bell Labs, Alcatel-Lucent, Murray Hill, NJ, USA from 2009 to 2011, as a Member of Technical Staff, and Harvard University, Cambridge, MA, USA from 2008 to 2009, as a Postdoctoral Research Fellow. He received his Ph.D. degree in Electrical   Computer Engineering from The University of Texas at Austin in 2008. Prior to joining UT, he was a research engineer at the Telecommunications R\ D Center, Samsung Electronics, Suwon, Korea, from 2001 to 2005.

He is now an Editor-in-Chief of the IEEE Trans. Molecular, Biological, and Multi-scale Communications and a Senior Editor of the IEEE Wireless Communications Letters. He has served/serves as an Editor for the IEEE Communications Magazine (2016-present), the IEEE Trans. on Wireless Communications (2012-2017), the IEEE Trans. on Molecular, Biological, and Multi-scale Comm. (2015-2018), and the IEEE Wireless Communications Letters (2016-present). He is an IEEE ComSoc Distinguished Lecturer for the term 2020-2021.

He was the recipient/co-recipient of the IEEE WCNC Best Demo Award in 2020, the Young Engineer Award from the National Academy of Engineering of Korea (NAEK) in 2019, the IEEE DySPAN Best Demo Award in 2018, the IEEE/KICS Journal of Communications and Networks Best Paper Award in 2018, the Award of Excellence in Leadership of 100 Leading Core Technologies for Korea 2025 from the NAEK in 2017, the Yonam Research Award from LG Yonam Foundation in 2016, the IEEE INFOCOM Best Demo Award in 2015, the IEIE/IEEE Joint Award for Young IT Engineer of the Year in 2014, the KICS Haedong Young Scholar Award in 2013, the IEEE Signal Processing Magazine Best Paper Award in 2013, the IEEE ComSoc AP Outstanding Young Researcher Award in 2012, the IEEE VTS Dan. E. Noble Fellowship Award in 2008.


Yansha Deng (S13-M20)

Yansha Deng (S13-M20) received the Ph.D. degree in electrical engineering from the Queen Mary University of London, U.K., in 2015. From 2015 to 2017, she was a Post-Doctoral Research Fellow with Kings College London, U.K., where she is currently a Lecturer (Assistant Professor) with the Department of Informatics. Her research interests include molecular communication, machine learning, and 5G wireless networks. She was a recipient of the Best Paper Awards from ICC 2016 and Globecom 2017 as the first author. She is currently an Associate Editor of the IEEE Transactions on Communications, IEEE Transactions on Molecular, Biological and Multi-scale Communications, and the Senior Editor of the IEEE Communication Letters. She also received the Exemplary Reviewers of the IEEE Transactions on Communications in 2016 and 2017, and IEEE Transactions on Wireless Communications in 2018. She has also served as a TPC Member for many IEEE conferences, such as IEEE GLOBECOM and ICC.


Timothy J. O’Shea

Timothy J. O’Shea is currently the Chief Technology Officer and Co-Founder at DeepSig Inc. and a Research Assistant Professor at Virginia Polytechnic Institute and State University in Arlington, Virginia, US. He received his B.S. and M.S. degrees in Computer and Electrical Engineering at North Carolina State University in 2007 and his Ph.D. degree in Electrical Engineering from Virginia Tech in 2017. He worked as a senior engineering researcher for a U.S. government communications research laboratory in College Park, MD from 2006 to 2013, and as VT research faculty from 2013-current. He has served as a technical lead and founder of technical proceedings for the GNU Radio project   conference and has led applied research and development into the convergence of machine learning and digital signal processing for communications systems over the past 14 years including for DARPA, IARPA, and NSF. He has authored key works applying deep learning to wireless communications systems including over 40 peer reviewed publications and 40+ patent applications. He helped lead technical strategy during seed stage at both Federated Wireless and Hawkeye 360, which are revolutionizing the fields of shared spectrum access and satellite RF sensing and analytics prior to co-founding DeepSig to rethink baseband processing for spectrum sensing and for L1 modem design. He has served on the editorial boards of IEEE Transactions on Wireless Communications, IEEE Transactions on Cognitive Communications and Networking, as a Co-Chair for the IEEE Emerging Technology Initiative on Machine Learning for Communications, as an organizer and chair for numerous conferences and events, and as a Supervisory Board member for the EU Horizon 2020 WINDMILL ML for Wireless Communications Project.


Linglong Dai (M’11-SM’14)

Linglong Dai (M’11-SM’14) received the B.S. degree from Zhejiang University, Hangzhou, China, in 2003, the M.S. degree (with the highest Hons.) from the China Academy of Telecommunications Technology, Beijing, China, in 2006, and the Ph.D. degree (with the highest Hons.) from Tsinghua University, Beijing, China, in 2011. From 2011 to 2013, he was a Postdoctoral Research Fellow with the Department of Electronic Engineering, Tsinghua University, where he was an Assistant Professor from 2013 to 2016 and has been an Associate Professor since 2016. His current research interests include massive MIMO, millimeter-wave communications, THz communications, NOMA, reconfigurable intelligent surface (RIS), and machine learning for wireless communications. He has coauthored the book “MmWave Massive MIMO: A Paradigm for 5G” (Academic Press, 2016). He has authored or coauthored over 60 IEEE journal papers and over 40 IEEE conference papers. He also holds 16 granted patents. He has received five IEEE Best Paper Awards at the IEEE ICC 2013, the IEEE ICC 2014, the IEEE ICC 2017, the IEEE VTC 2017-Fall, and the IEEE ICC 2018. He has also received the Tsinghua University Outstanding Ph.D. Graduate Award in 2011, the Beijing Excellent Doctoral Dissertation Award in 2012, the China National Excellent Doctoral Dissertation Nomination Award in 2013, the URSI Young Scientist Award in 2014, the IEEE Transactions on Broadcasting Best Paper Award in 2015, the Electronics Letters Best Paper Award in 2016, the National Natural Science Foundation of China for Outstanding Young Scholars in 2017, the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2017, the IEEE ComSoc Asia-Pacific Outstanding Paper Award in 2018, the China Communications Best Paper Award in 2019, and the IEEE Communications Society Leonard G. Abraham Prize in 2020. He is an Area Editor of IEEE Communications Letters, and an Editor of IEEE Transactions on Communications and IEEE Transactions on Vehicular Technology. Particularly, he is dedicated to reproducible research and has made a large amount of simulation code publicly available.


Namyoon Lee (S’11–M’14–SM’20)

Namyoon Lee (S’11–M’14–SM’20) received a Ph.D. degree from The University of Texas at Austin, in 2014. He was with Wireless Communications Research (WCR), Intel Labs, Santa Clara, USA, in 2015-2016. He is currently an Associate Professor at POSTECH, Pohang, Gyeongbuk, Korea. He was a recipient of the 2016 IEEE ComSoc Asia–Pacific Outstanding Young Researcher Award. He is currently an Editor for both the IEEE Trans. on Wireless Communications and the IEEECommunications Letters.


Jakob Hoydis

Jakob Hoydis received the diploma degree (Dipl.-Ing.) in electrical engineering and information technology from RWTH Aachen University, Germany, and the Ph.D. degree from Supélec, Gif-sur-Yvette, France, in 2008 and 2012, respectively. He is currently head of a research department at Nokia Bell Labs, France, focusing on radio systems and artificial intelligence. Prior to this, he was co-founder and CTO of the social network SPRAED and worked for Alcatel-Lucent Bell Labs in Stuttgart, Germany. His research interests are in the areas of machine learning, cloud computing, SDR, large random matrix theory, information theory, signal processing, and their applications to wireless communications. He is a co-author of the textbook ``Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency’’ (2017). He is recipient of the 2019 VTG IDE Johann-Philipp-Reis Prize, the 2019 IEEE SEE Glavieux Prize, the 2018 IEEE Marconi Prize Paper Award, the 2015 IEEE Leonard G. Abraham Prize, the IEEE WCNC 2014 best paper award, the 2013 VDE ITG Foerderpreis Award, and the 2012 Publication Prize of the Supélec Foundation. He has received the 2018 Nokia AI Innovation Award, the 2018 Nokia France Top Inventor Award, and has been nominated as an Exemplary Reviewer 2012 for the IEEE Communication Letters. He is currently chair of the IEEE COMSOC Emerging Technology Initiative on Machine Learning as well as Editor of the IEEE Transactions on Wireless Communications.