Virtual Seminar by Kaushik Chowdhury

Date and Time: December 16 at 9AM ET

Registration Process: Please register using the following link. You will receive a link in your email to attend the talk online.

Abstract: Network densification is poised to enable the massive throughout jump expected in the era of 5G and beyond. In the first part of the talk, we identify the challenges of verifying identity of a particular emitter in a large pool of similar devices based on unique distortions in the signal, or ‘RF fingerprints’, as it passes through a given transmitter chain. We show how deep convolutional neural networks can uniquely identify a radio in a large signal dataset composed of over a hundred WiFi radios with accuracy close to 99%. For this, we use tools from machine learning, namely, data augmentation, attention networks and deep architectures that have proven to be successful in image processing and modify these methods to work in the RF-domain. In the second part of the talk, we show how intentional injection of distortions and carefully crafted FIR filters applied to the transmitter-side can help in enhanced classification. Finally, we discuss how to detect new devices not previously seen during training using observed statistical patterns. We conclude by showing a glimpse of other applications of RF fingerprinting, like 5G waveform detection in large-scale experimental platforms and identifying a specific UAV in a swarm.

Bio: Kaushik Chowdhury is Professor and Faculty Fellow in the ECE department and Associate Director at the Institute for the Wireless IoT at Northeastern University, Boston. He was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2017, the DARPA Young Faculty Award in 2017, the Office of Naval Research Director of Research Early Career Award in 2016, and the NSF CAREER award in 2015. He has received best paper awards at several conferences that include, Infocom, Globecom, ICC (3x), SenSys, ICNC, and DySpan. He is presently a co-director of the Platforms for Advanced Wireless Research (PAWR) project office and the Colosseum RF emulator. His current research interests span applied machine learning to wireless systems, networked robotics, wireless charging and at-scale experimentation for emerging 5G and beyond networks.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar #5

Dear TCCN fellow members, 

Please find the details of the fifth virtual seminar.

Time and date: EDT 9:00am-10:00am, Wednesday, 18 November 2020

Venue: online registration via

Title: Secure Computation with Privacy Preservation for Cyber Physical System Applications

Presenter:  Professor Zhu Han, University of Houston, USA

Abstract: Cyber Physical System (CPS) have infiltrated into many areas such as aerospace, automobiles, chemical processing, civil infrastructure, energy, healthcare, transportation, entertainment, and consumer appliances due to their tight integration of computation and networking capabilities to monitor and control the underlying systems. Many domains of CPS such as smart metering, sensor/data aggregation, crowd sensing, traffic control etc., typically collect huge amounts of individual information for data analysis and decision making, therefore privacy is a serious concern in CPS. Most of the traditional approaches protect the privacy of individual’s data by employing trusted third parties or entities for data collection and computation. An important challenge in these large-scale distributed applications is how to protect the privacy of the participants during computation and decision making, especially when such third party entities are untrusted. Considering various CPS applications involving modeling, we first discuss on utilizing applied cryptographic techniques for privacy preserving secure computation. Then we focus on the differential privacy based secure computation that guarantees individual privacy in presence of untrusted third party entities. Since confidential information must not be inappropriately released, and the use of untrusted information must not corrupt trusted computation and the utility. This talk concludes by focusing on the development of such tools for state-of-the-art applications by considering application-specific information security requirements.

Bio:  Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communication and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communication Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han is the winner 2021 IEEE Kiyo Tomiyasu Award. He has been IEEE fellow since 2014, AAAS fellow since 2020 and IEEE Distinguished Lecturer from 2015 to 2018. Dr. Han is 1% highly cited researcher according to Web of Science since 2017.

Best regards,
Yue Gao
Chair, Technical Committee on Cognitive Networks (TCCN)

Virtual Seminar by Prof. Vincent Poor

Dear Colleagues,

The IEEE TCCN Security Special Interest Group is launching a monthly virtual seminar series hosting speakers to highlight the challenges in securing the next generation (xG) wireless networks. The theme of the talks will feature cutting edge research directions addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

The first talk of the series will be delivered by Prof. Vincent Poor, Princeton University.

Date and Time: November 17 at 9AM ET

Registration: Please register using the following link. You will receive a link in your email to attend the talk online.

Title: Physical Layer Security in Wireless Networks

Abstract: The increasing deployment of wireless systems poses security challenges in emerging dynamic and decentralized networks consisting of very large numbers of low-cost and low-complexity devices. Over the last two decades alternative/complementary means to secure data exchange in wireless settings have been investigated in the framework of physical layer security (PLS), addressing jointly the issues of reliability and secrecy. PLS takes advantage of the inherent randomness of wireless communication channels and/or the unclonability of hardware fabrication processes, to harvest entropy and deliver authentication, confidentiality, message integrity, and privacy in demanding scenarios. In this talk, we review these issues from an information theoretic security perspective. PLS relies on information theoretic proofs of (weak or strong) perfect secrecy, a notion first introduced by Shannon in 1949. As such, PLS systems cannot be “broken” irrespective of the adversarial computational power, i.e., the proofs do not rely on any assumptions regarding the hardness of particular families of algebraic problems. There are some fundamental differences between information theoretic security and classical cryptography, and we will also discuss some of the pros and cons of each.

Bio:  Dr. Vince Poor is the Michael Henry Strater University Professor of Electrical Engineering at Princeton University, where his interests include information theory, machine learning and network science, and their applications in wireless networks, energy systems and related fields.  He is a member of the National Academy of Engineering and the National Academy of Sciences, and a foreign member of the Chinese Academy of Sciences and the Royal Society.  Recognition of his work includes the 2009 ComSoc Edwin Howard Armstrong Award, the 2017 IEEE Alexander Graham Medal, and honorary doctorates from universities in Asia, Europe and North America.

-TCCN SIG on Cognitive Security

Virtual Seminar #4

Dear TCCN fellow members,

Please find our 4th free virtual seminar organised by the IEEE ComSoc TCCN.

Time and date: EDT 9:00am-10:00am (BST 14:00-15:00), Tuesday, 20 October 2020

Venue: online registration via

Title: Symbiotic Radio: Achieving Mutualism Spectrum Sharing using Reconfigurable Intelligent Surfaces

Presenter: Professor Ying-Chang Liang, IEEE Fellow and Editor-in-Chief, IEEE Trans Cognitive Communications and Networking, University of Electronic Science and Technology of China (UESTC), China

Abstract: The heterogeneous wireless services and exponentially growing traffic call for novel spectrum- and energy efficient wireless communication technologies. In this talk, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits of cognitive radio (CR) and reconfigurable intelligent surfaces (RIS), leading to mutualism spectrum sharing and highly reliable backscattering communications. We provide a systematic view for SR which integrates passive radios with active communications, and address its applications in low-power IoT communications in 6G and beyond.

Biography: Ying-Chang Liang is a Professor at University of Electronic Science and Technology of China (UESTC), China, where he leads the Center for Intelligent Networking and Communications (CINC). He was a Professor in University of Sydney, Australia, and a Principal Scientist and Technical Advisor in the Institute for Infocomm Research (I2R), Singapore. His research interest lies in the general area of cognitive radio, dynamic spectrum access, Internet-of-Things, artificial intelligence and machine learning techniques.
Dr Liang was elected a Fellow of the IEEE for contributions to cognitive radio communications, and was also recognized by Thomson Reuters (Now Clarivate Analytics) as a Highly Cited Researcher since 2014. He received the Institute of Engineers Singapore (IES)’s Prestigious Engineering Achievement Award in 2007, and the IEEE Standards Association’s Outstanding Contribution Appreciation Award in 2011. He has also received numerous paper awards, with the recent ones including IEEE ICC Best Paper Awards in 2017 and 2019, IEEE ComSoc’s TAOS Best Paper Award in 2016, and IEEE Jack Neubauer Memorial Award in 2014.
Dr Liang is Founding Editor-in-Chief of IEEE Journal on Selected Areas in Communications-Cognitive Radio Series, and Editor-in-Chief of IEEE Transactions on Cognitive Communications and Networking. He was the Chair of IEEE Communications Society Technical Committee on Cognitive Networks, and served as Guest/Associate Editor of IEEE Transactions on Wireless Communications, IEEE Journal of Selected Areas in Communications, IEEE Signal Processing Magazine, IEEE Transactions on Vehicular Technology, and IEEE Transactions on Signal and Information Processing over Network. He was also an Associate Editor-in-Chief of the World Scientific Journal on Random Matrices: Theory and Applications. Dr Liang was a Distinguished Lecturer of the IEEE Communications Society and the IEEE Vehicular Technology Society, and served as TPC Chair and Executive Co-Chair of IEEE Globecom’17. He will serve as the general chair of IEEE 2018 International Conference on Communication Systems.

Best regards,
Yue Gao
Chair, Technical Committee on Cognitive Networks (TCCN)

Call for videos on “The Role of Cognitive and Intelligent Technologies in COVID-19 Pandemic”

Dear TCCN Members,

I hope this email finds you well.
We would like to bring your attention to this new initiative of the TCCN Technical Committee

Call for videos on “The Role of Cognitive and Intelligent Technologies in COVID-19 Pandemic”

(Download PDF)

The outbreak of COVID-19 has unfortunately spread all over the world and changed the way we live. There is no doubt that the coronavirus pandemic will go down in history as one of the challenges humanity has ever faced. We are interested in receiving videos for either specialized audiences or the general public the various applications of cognitive and intelligent technologies in the fight against COVID-19 and the role of the cognitive technologies research in this battle. We would like to promote and advertise what IEEE ComSoc members are doing in this regard.

Submission instructions

Please send an email to– subject “TCCN ComSoc COVID19 video series” – with the following information:
– Author(s) name(s) and affiliation(s)
– A brief summary of the content of your video (no more than 200 words)
– The IEEE ComSoc member number of at least one of the authors
– A link to your video

The email address has been configured to request a positive confirmation. Upon sending your message you will be asked to confirm by clicking on a link.

Videos can be submitted by 31-December-2020

They will be reviewed by an editorial board appointed by the ComSoc Online Content Board and, if considered to be of enough quality and rigor, they will be edited – if needed – and published in ComSoc video channels. The consent form to be signed allowing IEEE ComSoc to edit and publicly show the video will be forwarded to the authors upon receiving and screening the content.

The editors from TCCN for this video series are Daniel B. da Costa ( and Yuan Ma ( If you have any comment/doubt, please do not hesitate to contact us.

Best Regards,
Prof. Dr. Daniel Benevides da Costa
Department of Computer Engineering, Area: Telecommunications
Federal University of Ceará (UFC)

Virtual Seminar #3

Dear TCCN fellow members,

Please find our 3rd free virtual seminar organised by the IEEE ComSoc TCCN on “Where No Cognitive Radio Has Gone Before: Machine Learning” delivered by Prof. Alexander Wyglinski.

Time and date: EDT 9:00am-10:00am (BST 14:00-15:00), Thursday, 3rd September 2020
Venue: online, registration via
Title: Where No Cognitive Radio Has Gone Before: Machine Learning
Presenter: Prof. Alexander Wyglinski, Worcester Polytechnic Institute, USA


In May 2017, the first-ever space-based cognitive radio experiments were performed using the NASA SCaN Test-bed located on the International Space Station (ISS). Followed by a second series of space-based cognitive radio tests in August 2018, these experiments yielded new knowledge and insights on how intelligent radio systems can operate in very challenging environments such as depths of space. In this talk, I will give an overview of the extensive five-year technical collaboration between Worcester Polytechnic Institute (WPI), Penn State, and NASA Glenn Research Center that resulted in these two space-based cognitive radio experiments in May 2017 and August 2018. In particular, details regarding the use of Reinforcement Learning Neural Networks (RLNNs) to form the core of a cognitive radio medium access control (MAC) layer will be presented, and issues such as catastrophic forgetting affecting the performance of our proposed cognitive radio will be discussed.


Dr. Alexander M. Wyglinski is an internationally recognized expert in wireless communications, cognitive radio, 5G, connected vehicles, software-defined radio, dynamic spectrum access, satellite communications, vehicular technology, wireless system optimization and adaptation, autonomous vehicles, and cyber-physical systems. Dr. Wyglinski is a Full Professor of Electrical and Computer Engineering and a Full Professor of Robotics Engineering (courtesy appointment) at Worcester Polytechnic Institute, Worcester, MA, USA, as well as the Director of the Wireless Innovation Laboratory (WI Lab). Dr. Wyglinski is very active in the technical community, serving on the organizing committees of numerous technical conferences and several journal editorial boards. These activities include serving as the General Co-Chair for the 82nd IEEE Vehicular Technology Conference in Fall 2015, as well as Technical Editor of the IEEE Communications Magazine. From January 2018 to December 2019, Dr. Wyglinski served as the President of the IEEE Vehicular Technology Society, an applications-oriented society of approximately 5000 members that focuses on the theoretical, experimental, and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles, and land transportation. Throughout his academic career, Dr. Wyglinski has published approximately 45 journal papers, over 120 conference papers, nine book chapters, and three textbooks. He is currently being or has been sponsored by organizations such as The MathWorks, Toyota InfoTechnology Center U.S.A., Defense Advanced Research Projects Agency, Naval Research Laboratory, MITRE Corporation, MIT Lincoln Laboratory, Office of Naval Research, Air Force Research Laboratory Space Vehicles Directorate, and the National Science Foundation. Dr. Wyglinski is a Senior Member of the IEEE, as well as a member of Sigma Xi, Eta Kappa Nu, and the ASEE.

Best regards,
Yue Gao
Chair, Technical Committee on Cognitive Networks (TCCN)

Free Virtual Seminar #2

Dear TCCN fellow members,

Please find our 2nd free virtual seminar organised by the IEEE ComSoc TCCN on “From Cognition to Intelligence in Communications Networks” delivered by Professor Octavia Dobre.

Time and date: EDT 9:00am-10:00am (BST 14:00-15:00), Tuesday, 18th August 2020
Venue: online, registration via
Title: From Cognition to Intelligence in Communications Networks
Presenter: Prof Octavia A. Dobre, Memorial University, Canada


Since Mitola’s idea of cognitive radio which arose close to 2000, significant advancements have been made towards applying intelligence to communications networks. While 2020 marks an important milestone in the deployment of 5G wireless networks, planning to deliver enhanced mobile broadband, massive connectivity, ultra-reliability and lower latency, the need to move to beyond 5G wireless (B5G) has emerged in both industry and academia. B5G aims to provide a major paradigm shift from connected things to connected intelligence.

After 20 years, we can state that the time of extending cognition to artificial intelligence (AI) in the field of communications has arrived. The next decade is crucial for research and development activities to achieving a native AI-based 6G network, capable of not only advancing the digitalization of vertical industries, but also of addressing human challenges through a connected world.

This talk will provide a brief overview of advances in transitioning from cognition to intelligence in communications networks, with emphasis on the features of 5G, as well as on the envisioned 6G wireless. It will discuss the intelligence integration supported by mobile edge computing in both terrestrial and vertical dimensions of emerging communications networks, along with modalities of developing a deep learning network. Furthermore, applications of machine learning techniques to communications will be presented, e.g., for the identification of the signal type in both wireless and optical communications areas. Finally, the talk will highlight research directions for the application of AI to the field of communications.


Octavia A. Dobre is a Professor and Research Chair at Memorial University, Canada. Her research interests encompass various wireless technologies, such as non-orthogonal multiple access and intelligent reflective surfaces, blind signal identification, as well as optical and underwater communications. She has co-authored over 300 refereed papers in these areas.

Dr. Dobre serves as the Editor-in-Chief (EiC) of the IEEE Open Journal of the Communications Society. She was the EiC of the IEEE Communications Letters, as well as a Senior Editor, Editor, and Guest Editor for various prestigious journals and magazines.

Dr. Dobre was a Royal Society Scholar and a Fulbright Scholar. She obtained Best Paper Awards at various conferences, including IEEE ICC, IEEE Globecom, and IEEE WCNC. Dr. Dobre is a Fellow of the Engineering Institute of Canada and a Fellow of the IEEE.

Best regards,
Yue Gao
Chair, Technical Committee on Cognitive Networks (TCCN)

Free Virtual Seminar by IEEE ComSoc TCCN

Dear TCCN fellow members,

With feedback and comments from our TCCN meeting early this month, we are starting to organise free virtual seminars to our Technical Community on Cognitive Networks (TCCN) members. Please see the following talk on “Deep Learning in Wireless Communications” by Professor Geoffrey Ye Li.

Time and date: EDT 9:00am-10:00am (BST 14:00-15:00), Friday, 24th July 2020
Venue: online, registration via
Title: Deep Learning in Wireless Communications
Presenter: Geoffrey Ye Li, School of ECE, Georgia Tech


It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in wireless communications, including physical layer processing and resource allocation. DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN).
Judicious resource (spectrum, power, etc.) allocation can significantly improve efficiency of wireless networks. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to reduce the complexity of mixed integer non-linear programming (MINLP). We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.


Dr. Geoffrey Li is currently a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published over 500 referred journal and conference papers in addition to over 40 granted patents. His publications have been cited over 41,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, 2017 IEEE SPS Donald G. Fink Overview Paper Award, and 2019 IEEE ComSoc Edwin Howard Armstrong Achievement Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech

Best regards,
Yue Gao
Chair, Technical Committee on Cognitive Networks (TCCN)