Performance Analysis of Cognitive Radio Systems with Imperfect Channel Knowledge

Originally posted in Sec-IG blog (link to the original post)

Ankit Kaushik, Friedrich Jondral
Communications Engineering Lab, Karlsruhe Institute of Technology, Germany
Contact Information:

Over the last decade, wireless communication is witnessing a tremendous growth in the data traffic due to ever-increasing number of connected devices. According to the recent surveys on mobile traffic by prominent market leaders Cisco [1] and Ericsson [2], the existing mobile traffic is expected to increase 11-fold  by 2021. Certainly, in future, the state-of-the-art standards (fourth-Generation (4G) – LTE, WiMAX) are incapable of sustaining the substantial amount of data traffic, originating from these devices. It is being visualized that a major portion of this requirement can be satisfied through an additional spectrum. Due to exclusive usage, the spectrum below 6 GHz is not able to meet this demand of additional spectrum, leading to its scarcity. To this end, Cognitive Radio (CR), along with millimeter-wave technology [3] and visible-light communication [4], is envisaged as an alternative source of spectrum. The latter techniques are limited to a point-to-point communication, by which mobility is compromised. In contrast, a CR system aims at an efficient utilization of the spectrum below 6 GHz – suitable for mobile communications – by enabling a secondary access to the licensed spectrum while ensuring a sufficient protection to the licensed users (also referred as a primary system).

Despite the fact that an extensive amount of literature – including [5]-[7] – has been dedicated to the field of CR, its performance analysis has been dealt inadequately from a deployment perspective. Therefore, making it difficult to understand the extent of vulnerability caused to the primary system. In this context, it is essential to establish a deployment-centric viewpoint for analyzing the performance of a CR system. Following this viewpoint, it has been identified that the involved channels’ knowledge at the secondary transmitter is pivotal for the realization of cognitive techniques. However, the aspect of channel knowledge in context to CR systems, particularly its impact on the performance, has not been clearly understood. With the purpose of curtailing this gap, the research carried out at Communications Engineering Lab (CEL), Karlsruhe Institute of Technology (KIT) proposes a successful integration of this knowledge – by carrying out channel estimation – in reference to different CR systems, namely interweave, underlay and hybrid systems. More specifically, our work outlines the following aspects corresponding to the aforementioned CR systems, employing different cognitive techniques such as spectrum sensing, power control and their combination.

Analytical Framework
First, an analytical framework is established to characterize the effects such as time allocation and variation, arising due to the incorporation of imperfect channel knowledge, that are detrimental to the performance of the CR systems [8,9].

In order to satisfy the low complexity and the versatility towards unknown primary user signal requirements, which are necessary for the deployment of the CR systems, a received power-based channel estimation is included in the proposed framework. In this regard, an effort has been made to facilitate a direct incorporation of the estimated parameter (received power) to the performance characterization of the CR systems.

Besides, a stochastic approach is followed for characterizing the variations in the system. In particular, these variations cause uncertainty in the interference power received at the primary system, which may completely disrupt the operation of the CR systems. In order to maintain this uncertainty below a desired level, new interference constraints are proposed.

Performance Tradeoffs
Second, as a major observation, it has been identified that the estimation time is closely associated with the performance of the CR systems. On one side, it is related to the variations incurred in the system, through which the level of uncertainty in the interference can be effectively controlled, ultimately affecting the performance in terms of throughput at secondary receiver, defined as secondary throughput. While on the other side, the time resource allocated for channel estimation directly influences the secondary throughput. In our work, this kind of dual dependency of the secondary throughput on the estimation time has been investigated in the form of performance tradeoffs, namely estimation-sensing-throughput tradeoff for the interweave system and the hybrid system, and estimation-throughput tradeoff for the underlay system, cf. Figure 1.


Figure 1. An illustration of a) estimation-sensing-throughput and b) estimation-throughput tradeoff for interweave system and underlay systems, respectively. The performance tradeoff depicts the achievable throughput at secondary receiver.

These tradeoffs present a useful tool for visualizing the response of a CR system to different choices of the estimation time so that the performance degradation introduced due to the channel estimation can be precisely regulated. In other words, a system designer can utilize these tradeoffs to preclude situations under which the performance degradation becomes intolerable. Conversely, from a theoretical perspective, these tradeoffs can be used to determine a suitable estimation time that yields the maximum achievable secondary throughput while obeying the interference constraints.

Hardware Deployment
Third, using a software defined radio platform, hardware implementations are carried out to validate the feasibility of the analysis proposed [10]-[12]. In addition to this, hardware demonstrators are deployed, which in a way present the operation of CR systems in more practical conditions.

[1] Cisco, “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013 – 2018,” White Paper, Feb. 2014.
[2] Ericsson,“Ericsson  Mobility Report,” Tech. Rep., Nov. 2015. [Online]. Available:
[3] T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!” IEEE Access, vol. 1, pp. 335–349, 2013.
[4] S. Wu, H. Wang, and C. H. Youn, “Visible light communications for 5G wireless networking systems: from fixed to mobile communications,” IEEE Netw., vol. 28, no. 6, pp. 41–45, Nov. 2014.
[5] Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensing-Throughput Tradeoff for Cognitive Radio Networks,” IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337, Apr. 2008.
[6] X. Kang, Y. C. Liang, A. Nallanathan, H. K. Garg, and R. Zhang, “Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity,” IEEE Trans. Wireless Commun., vol. 8, no. 2, pp. 940–950, Feb. 2009.
[7] X. Kang, Y.-C. Liang, H. K. Garg, and L. Zhang, “Sensing-Based Spectrum Sharing in Cognitive Radio Networks,” IEEE Trans. Veh. Technol., vol. 58, no. 8, pp. 4649–4654, Oct. 2009.
[8] A. Kaushik, S. K. Sharma, S. Chatzinotas, B. Ottersten, and F. K. Jondral, “Sensing-Throughput Tradeoff for Interweave Cognitive Radio System: A Deployment-Centric Viewpoint,” IEEE Trans. Wireless Commun., vol. 15, no. 5, pp. 3690–3702, May 2016.
[9] A. Kaushik, S. K. Sharma, S. Chatzinotas, B. Ottersten, and F. K. Jondral, “On the Performance Analysis of Underlay Cognitive Radio Systems: A Deployment Perspective,” IEEE Trans. on Cogn. Commun. Netw., vol. 2, no. 3, pp. 273–287, Sep. 2016.
[10] A. Kaushik, M. Mueller, and F. K. Jondral, “Cognitive Relay: Detecting Spectrum Holes in a Dynamic Scenario,” in Tenth International Symposium on Wireless Communication Systems (ISWCS), 2013, pp. 1–2.
[11] A. Kaushik, F. Wunsch, A. Sagainov, N. Cuervo, J. Demel, S. Koslowski, H. J ̈ kel, and F. Jondral, “Spectrum sharing a for 5G wireless systems (Spectrum sharing challenge),” in IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Sep. 2015, pp. 1–2.
[12] H. Becker, A. Kaushik, S. K. Sharma, S. Chatzinotas, and F. K. Jondral, “Experimental Study of an Underlay Cognitive Radio System: Model Validation and Demonstration,” in 11th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), May-Jun. 2016, pp. 511–523.

Call For Papers for the Special Issue on “Enabling Mobile Computing and Cognitive Networks through Artificial Intelligence”

Originally posted in Sec-IG blog (link to the original post)

The advent of new wireless technologies such as mobile cloud computing (MCC), Internet of Things (IoT), and mobile edge computing (MEC) have increased expectations for optimal and reliable resource management of mobile web traffic. Cognitive Networking (CN) refers to networking that follows three basic principles: (1) observation and awareness, (2) cognition, and (3) reconfiguration. An important use case of CN would be to use artificial intelligence (AI) to make optimal spectrum decisions and reconfigure a cognitive radio network. In this application, it is also possible to use CN principles in mobile computing to provide better computational support for mobile users.
In this special issue, we focus on the new problems involving AI-based concepts in CN for mobile devices such as: machine learning for spectrum decision, mobile computing, edge analytics, predictive modeling, and resource management especially in video streaming. We invite submissions of high-quality articles for publication in the website of IEEE Communication Society: Cognitive Network Security Special Interest Group. Articles must be at least 500 words long with unlimited number of figures and references. The due date for submission of articles is January 15th.
The topics in this call include but are not limited to the following areas:

  • Machine learning based strategies for mobile computing
  • Cognition-based networking using Artificial Intelligence (AI)
  • Spectrum-awareness in Mobile Edge Computing (MEC)
  • Dynamic spectrum access and management in Mobile Cloud Computing (MCC)
  • Convergence of cognitive networking and cognitive computing
  • The era of cognitive mobile networking in Internet of Things (IoT) and Internet of Everything (IoE)
  • Cognitive radio realities in mobile computing
  • Data Analytics and Predictive Modeling in mobile CRN
  • Mobility in computation offloading
  • Optimization and resource management of CRNs in MCC and MEC environments
  • Real-time video streaming using mobile CRN
  • Mobile analytics and measurements for (emotion, object, pattern) recognition applications in CRN

Please e-mail manuscripts as  tex or word attachments, and illustrations as a jpg, png, pdf or gif attachment, to:

Eman Mahmoodi
Guest Editor

Cognitive Mobile Cloud Computing

Originally posted in Sec-IG blog (link to the original post)

The huge growth in mobile data traffic is generally because of increasingly sophisticated smart phone applications (e.g. Computer Vision, Augmented Reality, 3D gaming, cameras, and security). These applications are a major market driver for mobile equipment providers, service providers and IT players. The increasing sophistication of these applications, combined with the resource constraint on the hand-helds running multiple apps and user preference for lighter mobile devices (e.g. iPhone7) and wearables (e.g. Microsoft HoloLens, Apple watch, Fitbit) has made a case for offloading some of the computations supporting the apps to a resource rich cloud.

The term cloud offloading can mean either data flow offloading in networking applications or offloading computation intense processes on to the cloud. Here, we refer to the latter. Cloud offloading can be classified into three categories: (a) those that always offload to the cloud; (b) “all or nothing offloading” where either the entire application is offloaded to the cloud or executed locally, typically using an energy threshold to decide between offloading and not [1]; and (c) piecewise decisions, where some parts are executed locally while the others are offloaded to the cloud [2]. The third category offers the most flexibility for trade-offs, and can be done either at the coarse component level or at finer, method or instruction levels.

This computation offloading, especially for the apps consuming large data, occupies a considerable part of limited bandwidth over wireless networks. Spectrum-aware cognitive mobile cloud computing is the new concept (started from 2015 [3]) to use dynamic spectrum opportunistically from wireless networking to effect computation offloading and scheduling solutions that achieves efficient trade-offs between the mobile device and wireless resources. In this concept, cognitive cloud offloader schedules appropriate components of the application to run either on the mobile device or on the resource-rich cloud, while staying adaptive to the realtime changes of the wireless network [4]. Here, all the viable radio available interfaces (e.g. WiFi, LTE) in multi-RAT enabled devices are deployed to increase the throughput capacity and reliability, while balancing energy consumption for data transferring. Moreover, this concept allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the app component tasks towards a more spectrum aware scheduling order. Hence, this solution can shorten runtimes by parallel processing proper app component tasks in the mobile device and the cloud [5]. Finally, the dynamic algorithms used in the cognitive cloud offloader have several control knobs that can be used to balance the trade-off among the relative importance of battery power, end-to-end network delay, bandwidth, monetary cost of network and cloud access.


Figure 1. The Future: cognitive cloud offloading in heterogeneous mobile networks [7].

Cognitive cloud offloader can be used in the new wireless technologies such as mobile edge computing (MEC) and C-RAN as shown in Figure 1. With the advent of newer mobile edge computing (MEC) paradigms [6], where the cloud is closer to the end device than before, the offloading option becomes even more attractive in terms of keeping up with near real-time user responsiveness of the apps.

Given recent advances in technologies that enable bandwidth aggregation in wireless devices, the cognitive cloud offloader solution is implementable in practice. Other works that fall under general umbrella of the radio-aware computation offloading include, where the best of the available wireless interfaces is chosen (only one of the wireless interfaces) for data transfer, rather than a solution that considers using all of the radio interfaces simultaneously [8,9]. In recent works cloud offloading scheduling mechanisms are proposed for queue stability, but these works only deal with multi-channel systems, not multi-radio networks [1]. Evaluation results in [3,4,6] for the cognitive cloud offloader, using real data measurements from Android smart phones running multi-component applications and Amazon EC2 and NSFCloud over LTE and WiFi, show that the proposed strategies reduce energy consumption for the mobile device by 23% to 68% and speed-up in runtime by 46% to 66% compared to the state of art. Note also that the proposed suite of solutions provide higher degrees of freedom in protocol design for serving applications over multi-RAT enabled mobile devices and is an inter-disciplinary solution combining mobile computing and cognitive radio networking.


[1] W. Zhang, Y. Wen, K. Guan, D. Kilper, H. Luo, and D. Wu, “Energy-optimal mobile cloud computing under stochastic wireless channel,” IEEE Transactions on Wireless Communications, vol. 12, no. 9, pp. 4569–4581, Sep. 2013.

[2] E. Cuervo, A. Balasubramanian, D.-k. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, “MAUI: Making smartphones last longer with code offload,” in Proceedings of the International Conference on Mobile Systems, Applications, and Services, ser. MobiSys. ACM, 2010, pp. 49–62.

[3] “System Apparatus and Methods for Cognitive Cloud Offloading in a Multi-RAT Enabled Wireless Device”, S. Eman Mahmoodi and K. P.Subbalakshmi, Provisional U.S. Patent filed, US 62/262,624, December 2015.

[4] S. E. Mahmoodi and K. P. S. Subbalakshmi, “A Time-Adaptive Heuristic for Cognitive Cloud Offloading in Multi-RAT Enabled Wireless Devices,” in IEEE Transactions on Cognitive Communications and Networking, vol. 2, no. 2, pp. 194-207, June 2016.

[5] S. E. Mahmoodi, R. N. Uma, and K. P. Subbalakshmi, “Optimal joint scheduling and cloud offloading for mobile applications,” IEEE Transactions on Cloud Computing, vol. PP, no. 99, pp. 1–1, early access 2016.

[6] M. Patel, B. Naughton, C. Chan, N. Sprecher, S. Abeta, A. Neal et al., “Mobile-edge computing introductory technical white paper,” White Paper, Mobile-edge Computing (MEC) industry initiative, 2014.

[7] S. Eman Mahmoodi, K. P. Subbalakshmi, and R. N. Uma. 2016. Harnessing spectrum awareness to enhance mobile computing: poster. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking (MobiCom ’16). ACM, New York, NY, USA, 460-461.

[8] D. Huang, P. Wang, and D. Niyato, “A dynamic offloading algorithm for mobile computing,” IEEE Transactions on Wireless Communications, vol. 11, no. 6, pp. 1991–1995, Jun. 2012.

[9] P. Shu, F. Liu, H. Jin, M. Chen, F. Wen, and Y. Qu, “eTime: Energyefficient transmission between cloud and mobile devices,” in IEEE Proceedings of INFOCOM, 2013, pp. 195–199.


S. Eman Mahmoodi
The Information Networks and Security Lab
Department of Electrical & Computer Engineering,
Stevens Institute of Technology, Hoboken, NJ, USA

S. Eman Mahmooodi is currently pursuing his PhD degree at the Department of Electrical and Computer Engineering, Stevens Institute of Technology. He received the BS and MS degree in Electrical Engineering from Iran University of Science and Technology, respectively in 2009 and 2012. He has been working on Mobile Cloud Computing, Optimization Algorithms and Predictive Modeling, Internet of Things, Cognitive Networking, and Wireless Communications. Mahmoodi is a Stevens Innovation and Entrepreneurship Doctoral Fellow, and he has also received the graduate student inventor award from the New Jersey Inventors Hall of Fame (NJIHoF), 2016.

Information Communication Technology Standards: A Policy Perspective

Originally posted in Sec-IG blog (link to the original post)

The  Senior Deputy Coordinator for International Communications and Information Policy at the U.S Department of State, Ms. Julie Napier Zoller,  just published a blog post on the importance of information communication technology standards and the processes that are involved in making them. This is a great read for those interested in international policies on information communication.

The post is available here: Fast-Moving Technologies Need Bottom-Up Standards

Second Issue of the TCCN Newsletter Available

Dear Fellow TCCN Members,

I am very happy to introduce to you the second issue of TCCN Newsletter (previously called TCCN Communications). I would like to express my sincere thanks to TCCN Chair, Prof. Ying-Chang Liang, and other TCCN officers for their enthusiastic support for this initiative to serve the community. The issue can be downloaded here.

TCCN Newsletter is an electronic platform dedicated to excel in the following aspects:

  • Introducing forward-looking research ideas,
  • Updating members on new industry, standard, and policy initiatives,
  • Promoting top-quality publications with high potential impacts,
  • Increasing the visibility of TCCN within ComSoc and beyond.

In this issue, we introduce a new series of “virtual interviews”, with some of the influential researchers in the TCCN community. We asked each interviewee to share with TCCN members regarding his/her most significant recent work in cognitive networks, the most unique and impressive aspects of the work, the challenges and lessons encountered during the research, and the plans for the next few years.

I would like to thank Prof. Lingjie Duan from Singapore University of Technology and Design, who serves as the editor of this virtual interviews series. After sending out the interview invitations early 2016, we have received enthusiastic responses from the community. The interview results published in this issue only represent a subset of interviews that we have been working on. In the future, we will regularly publish virtual interviews with researchers of diverse research and geographical backgrounds.
As always, I would like to welcome any suggestions from TCCN members regarding how to make TCCN Newsletter more interesting and informative to the community. Please feel free to contact me at if you have any suggestions.

Thanks and best regards,

Jianwei Huang
Vice Chair, IEEE ComSoc TCCN
IEEE Fellow
IEEE ComSoc Distinguished Lecturer
Department of Information Engineering
The Chinese University of Hong Kong,

5G and Cognitive Networking Workshop and Summer School 2016

Originally posted in Sec-IG blog (link to the original post)

The NSF SAVI: Institute for Cognitive Networking and the University of Pretoria, South Africa conduct a Summer School in University of Pretoria.

More details here:

More about Institute for Cognitive Networking can be found here:

To join the Sec-IG, please visit the Linkedin page at: More about Sec-IG can be found here.

Information about SIGs in TCCN can be found here.


Cognitive Radio Networks and Security Threats

Originally posted in Sec-IG blog (link to the original post)

The allocation of the Industrial, Medical and Scientific (ISM) band has enabled the explosion of new technologies (e.g. WiFi) due to its licence-exempt characteristic. Millions of users worldwide can enjoy anywhere-anytime and affordable Internet access, with devices (e.g. laptops, smartphones, etc.) that operate in the ISM band.

Nowadays, the explosion of plethora of applications based mainly on the social media, and the Internet-of-Things (IoT) paradigm, usually causes overcrowding in this band, with undesired consequences. Overcrowding in the free spectrum often creates harmful interference, and wireless channel contention between the networking devices that cause link quality degradation, and poor network performance, negatively affecting user experience.

On the other hand, several studies (e.g. [1, 2]) have shown that licensed band utilisation is low, and according to FCC [3], temporal and geographical variations in the assigned spectrum can range from 15% to 85%. These free (un-utilised or under-utilised) portions of the spectrum are called as spectrum holes or white spaces.

Cognitive radio (CR) technology has emerged as a solution to the spectrum under-utilisation issue. CR-enabled devices can sense (detect) the spectrum holes, and use them in an opportunistic manner. In general, spectrum users are divided into two categories: (i) primary or incumbent users (PUs) that hold a license for a specific portion of the spectrum, and (ii) cognitive or secondary users (SUs) that use parts of the spectrum in an opportunistic way. SUs can make use of the CR technology and transmit in the licensed vacant bands. However, CR technology should cause minimum interference to PUs, and when a PU signal is detected, SUs shall immediately stop transmitting in this band.

CR technology, as every wireless network technology, faces a number of security threats and attacks, due to the open medium used for the transmissions. Common attacks for these technologies include MAC spoofing, jamming and congestion attacks, small back-off window attacks, etc.

Additionally to these attacks, CR networks face new types of attacks because of their two unique features: (i) cognitive capability that enables the CR devices to sense the environment and select the best available spectrum portions, and (ii) re-configurability that makes feasible for the CR devices to change on-the-fly several of their transmission characteristics (e.g. frequency, modulation, transmission power, etc.).

Attackers by taking advantage of the cognitive capability of CR devices, can mimic incumbent transmitters so as to enforce SUs vacate the specific band. This attack is referred as primary user emulation attack (PUEA), and can be regarded as a DoS type of attack. PUEAs can also be launched by greedy users aiming to force all other users to vacate a specific band, and acquire its exclusive use.

For the detection of PUEAs, many state-of-the-art contributions assume that the locations of the PUs are known in advance (e.g. [4, 5]). During operation, and when an incumbent signal is detected, several algorithms considering physical layer characteristics, for example the Received-Signal-Strength-Indicator, can estimate the location of the transmitter, and then compare it to the known PU locations, and infer about if a PUEA is in progress. Other works assume that no a-priori knowledge of the PUs is available, and try to detect fake primary signals using characteristics of the multi-path components, like the ratio of the first and the second multi-path components of the received signal at a helper node that is located very close to a PU.

Another type of attack against CR networks is the spectrum sensing data falsification attack (SSDF). Assume a CR network where SUs take part in a distributed spectrum sensing scheme, reporting their findings to a fusion centre (FC) that decides about spectrum availability, based on the observations from all SUs. Such distributed schemes aim to address issues related to undetected primary signals due to signal fading, multi-path, etc. A user can take advantage of this scheme, by reporting false observations to the FC; this is the SSDF attack.

The motives for this attack can vary, and a malicious user aims to make FC or other SUs to falsely conclude that PUs are active, or make them believe there are no active PUs when there are. In the last case, harmful interference will be created for the PUs. In other situations, greedy users launch SSDF attacks with the goal to monopolise a specific band by forcing all other SUs to evacuate it. In all cases, the reliability of the distributed scheme is severely degraded by the faulty observations.

For the detection of these attacks, the proposed algorithms (e.g. [6, 7]) adopt trust-based schemes where several fusion rules are used by the FC (AND, OR, average, Dempster-Shafer theory of evidence, etc.). Based on these schemes, the reputation of each SU is estimated, and if an SU is characterised as an adversary, its reports are ignored by the FC.

Both PUEA and SSDF, are severe attacks that can be easily implemented with off-the-shelf hardware and affect all parts of the so-called cognitive cycle (1) [8].

Figure 1. The cognitive cycle

Figure 1. The cognitive cycle

CR research focusing on several areas (security, spectrum sensing, spectrum analysis, etc.) has been significantly boosted using Software-Defined Radios (SDRs), devices that are highly re-programmable. SDRs allow for on-the-fly re-configuration of several parameters like the frequency, modulation, transmission power, etc.
In our laboratory, the Telecommunications and Networks Lab of ICS-FORTH, we follow a vertical approach to the SDR technology, providing everything, from higher layer software functionality and optimization, down to driver and hardware design of devices. Accordingly, we have developed an SDR based platform that is able to efficiently support heterogeneous wireless standards. Our platform enables the concurrent transmission and reception of multiple standards and channels, within the same radio band, utilizing a single workstation with open-source software and an SDR device. Two prevalent standards of IoT, the IEEE 802.11 & 802.15.4, are fully implemented in software, and are able to simultaneously interact with real devices. This platform can serve as the basis for any CR-related research, without the need for multiple transceivers and complex integration schemes, providing unique flexibility and upgradability, supporting effortlessly the most advanced cognitive radio schemes and techniques.

Alexandros Fragkiadakis
Researcher at the Telecommunications and Networks Lab
Institute of Compute Science, FORTH, Heraklion, Crete (Greece)

[1] K. Qaraqe, H. Celebi, A. Gorcin, A. El-Saigh, H. Arslan, and M. Alouini, “Empirical results for wideband multidimensional spectrum usage,” in Proc. 20th IEEE Personal, Indoor and Mobile Radio Communications, 2009, 2009, pp. 1262–1266.

[2] V. Valenta, Z. Fedra, R. Marsalek, and M. Villegas, “Analysis of spectrum utilization in suburb environment evaluation of potentials for cognitive radio,” in Proc. Ultra Modern Telecommunications and Workshops, ICUMT 2009, 2009, pp. 1–6.

[3] FCC, 1985, authorization of Spread Spectrum Systems Under Parts 15 and 90 of the FCC Rules and Regulations. Federal Communications Commission. June 18, 1985.

[4] Z. Jin and K. Subbalakshmi, “Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks,” in Proc. ICC, 2009, pp. 1–5.

[5] R. Chen, J. Park, and J. Reed, “Defense against primary user emulation attacks in cognitive radio networks,” IEEE J. Sel. Areas Commun, vol. 26, pp. 25–37, 2008.

[6] A. Rawat, P. Anand, H. Chen, and P. Varshney, “Countering byzantine attacks in cognitive radio networks,” in Proc. ICASSP, 2010, pp. 3098–3101.

[7] W. Wang, H. Li, Y. Sun, and Z. Han, “Attack-proof collaborative spectrum sensing in cognitive radio networks,” in Proc. CISS, 2009, pp. 130–134.

[8] A. G. Fragkiadakis, E. Z. Tragos, and I. G.Askoxylakis, “A survey on security threats and detection techniques in cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol.15, no.1, pp. 428-445, First Quarter 2013.

To join the Sec-IG, please visit the Linkedin page at: More about Sec-IG can be found here.

Information about SIGs in TCCN can be found here.

NSF SAVI iCON Inaugural Workshop

Originally posted in Sec-IG blog (link to the original post).

The National Science Foundation funded Science Across Virtual Institutes (SAVI): Institute on Cognitive Networking (iCON) held its inaugural workshop on January 19th, 2016. The workshop participants from Academia, Industry and Government, from the US and South Africa, exchanged ideas on joint research projects, systems development and spectrum policies for low cost wireless connectivity exploiting spectrum white spaces. A full list of participants and the agenda of the workshop can be found at:

The Stevens Institute of Technology led NSF iCON aims to promote and sustain cognitive wireless networking related research and education collaborations. While the initial focus is on establishing collaborations between the U.S. and South Africa (funded by ICASA and SITA) in partnership with the U. Washington and U. Pretoria, future efforts will expand to other countries in Africa and beyond. A major emphasis is on the investigation of the fundamental challenges related to low cost, reliable wireless broadband access technologies for traditionally underserved areas using dynamic spectrum access/sharing/management techniques that exploit spectrum (e.g., T.V.) white spaces (WS). More information on this institute can be found at:

To join the Sec-IG, please visit the Linkedin page at: More about Sec-IG can be found here.

Information about SIGs in TCCN can be found here.

TCCN Communications launched!

I am very happy to introduce to you the inaugural issue of TCCN Communications. I would like to express my sincere thanks to TCCN Chair, Dr. Ying-Chang Liang, and other TCCN officers for their enthusiastic support for starting this initiative to serve the community.

This inaugural December 2015 issue (can be downloaded here) includes two special issues on some very hot topics of cognitive communications and networking:

A special issue on “Cognitive Radio for Heterogeneous Networks” edited by Prof. Walid Saad from Virginia Tech,
A special issue on “TV White Space Communications and Networking” edited by Prof. Yue Gao from Queen Mary University of London.

These two special issues contain a total of 13 contributions from leading experts in the field, and cover both recent progress and forward-looking insights regarding technology, economics, and regulations of cognitive radio networks. I would like to congratulate Walid and Yue for the excellent work. I also want to thank Prof. Lingjie Duan from Singapore University of Technology and Design for taking care of the formatting and final editing details as the Publication Editor.

Besides publishing the special issues, we plan to further recommend selected top papers and PhD Dissertations in future TCCN Communication issues. We also welcome any suggestions from TCCN members regarding how to make this platform most interesting and useful to the community. Please feel free to contact me at if you have any suggestions.

Thanks and best regards,

Jianwei Huang

IEEE Fellow (Class of 2016)
Vice Chair, IEEE ComSoc Cognitive Network Technical Committee (TCCN)
Director, IEEE TCCN Communications
IEEE ComSoc Distinguished Lecturer