Secure Cognitive Radio Networks with Multi-Phase Smart Relaying and Cooperative Jamming

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

Pin-Hsun Lin and Eduard A. Jorswieck

Dresden University of Technology, Germany

Due to the broadcast nature of wireless networks, communications are potentially subject to attacks, such as passive eavesdropping or active jamming. Instead of using the traditional cryptographic approaches [2] to combat the malicious users, we consider the information-theoretic secrecy. Note that the information-theoretic secrecy approach, initiated by Shannon [3] and developed by Wyner [4], can exploit the randomness of the wireless channels to ensure the secrecy of the transmitted messages while there is no assumption on the computation capabilities at the malicious users. As a performance measure for communication systems with secrecy constraints, a secrecy rate is defined as a rate at which the message can be transmitted reliably and securely between the legitimate nodes. However, similar to communication networks without secrecy constraints, the overall performance is limited by the relative channel qualities to guarantee secure communications. Many signal processing and multi-user techniques have therefore been proposed to overcome this limitation such as the use of multiple antennas.

Recently, there has been a substantial interest in the secrecy of multi-user systems [5], with a particular emphasis on potential cooperation between users to enhance the secrecy of communications. Cooperation in communication networks is an emerging technique to improve the reliability of wireless communication systems, and it involves multiple parties assisting each other in the transmission of messages, see e.g., [6]. Assuming that the cooperative node(s) can be trusted and that they aim at increasing the secrecy of the original transmission in the presence of a possible external eavesdropper, several cooperative strategies have been proposed. As one kind of cooperative communications schemes, cognitive radio technology has been proposed by Mitola in [7] as an efficient way to enhance the spectrum efficiency which has considerable development over the last few decades. The concept of cooperation for secrecy, and the corresponding cooperative techniques can naturally be applied to the cognitive radio network.

In this article, we consider a cognitive radio (CR) network including four single-antenna half-duplex nodes, where the CR receiver is treated as a potential eavesdropper with respect to the primary transmission [1]. In exchange for cooperation from the CR user to improve/maintain his own secrecy rate, the primary user allows the CR user to share part of the spectrum. Compared to some important literature in this research line, e.g., [8], [9], and [10], etc., we additionally consider the following secure coexistence conditions:

(i) the transmission of CR transmitter does not degrade the primary user’s secrecy rate, and
(ii) the encoder and decoder at the primary transmitter and receiver, respectively, are left intact whether CR transmits or not.

The reasons to consider the secure coexistence conditions are twofold. First, to utilize the time-frequency slot in the overlay sense, cognitive radio systems are obligated not to interfere the primary systems, which is common in cognitive radio systems design. Second, with the condition (ii), cognitive radios are backward compatible with the legacy systems, which cannot sense and adapt to the environment agilely. This conditions make the cognitive radio capable of operating in broader usage scenarios. One of the possible practical scenarios of the considered model is that, the primary users belong to a licensed system, who sells rights of the spectrum usage to a femtocell system. Here we can let the CR transmitter and receiver be the femtocell base station and users, respectively. However, the femtocell operator may not be able to guarantee that the femtocell users are malicious or not. Thus, to provide a secrecy transmission to the primary users, not only the primary base station needs to use the wiretap coding, but also the femtocell base station needs to help to maintain the secrecy transmission for the primary system.

We analyze the achievable secrecy rate with weak secrecy of the cognitive user in the cognitive radio network under the secure coexistence conditions. In addition, we derive the rate constraints to guarantee that the primary user’s weak secrecy is unchanged as well, which requires different analysis compared to [8], [9], [10]. For example, the relation between channels observed by the primary transmitter before and after the cognitive transmitter is active should investigated for proper relay and jamming design. Otherwise, either the reliability of the cognitive user or the secrecy of the primary user will be violated. In Fig. 1 we show two improper system designs, where the black rectangular denotes the wiretap code used by the primary user, i.e., the row and column of it are indexed by the secure and confusion messages, respectively and each entry is a codeword. The height and width of the blue rectangular denote the capacity of the channels between the primary transmitter to the primary receiver and that between the primary transmitter to the CR receiver, respectively, after CR transmitter starts to transmit. Fig. 1 (a) shows that, both reliability and secrecy are fulfilled. However, the cognitive transmitter may overdesign the relay power for the primary user’s signal such that the capacity is too large, which is inefficient for the CR user. In particular, that means CR transmitter wastes power on constructing a too good channel for the primary user., while the remained power for CR’s own transmission is reduced. In contrast, Fig. 1 (b) shows that, the relay is efficient, i.e., the new channel is efficient for the transmission of the secure message. However, the confusion rate is not high enough for the new channel, which causes that the secrecy is violated. Therefore, the analysis of the aforementioned rate constraints is important. In addition, we also derive a capacity upper bound for the CR user under both discrete memoryless and additive white Gaussian noise (AWGN) channels to evaluate the performance of the achievable scheme.

Fig. 1. Improper design of the relay and jamming.

We then propose a multi-phase transmission scheme, which considers the following additional phases. First, to accommodate the operations of practical systems, we take into account the first additional phase for listening to/decoding the primary’s signal at the CR transmitter. Note that the primary user’s signal is commonly assumed non-causally known at CR transmitter. Second, we introduce another additional phase as the third one to endow the cognitive system an extra degree of freedom for utilizing different transmission schemes. For AWGN channels, this degree of freedom improves the performance by exploiting pure relaying and jamming but not simultaneously transmitting cognitive user’s own signal.

Finally, we illustrate our results through one numerical example as shown in Fig. 2 based on a geometrical setup, which highlights the impact of the node geometry on the achievable rates and on the optimal power allocation and time splitting of the CR transmitter. Note that we fix the locations of the primary transmitter and receiver at the coordinates (0,0) and (1,0), respectively. The CR receiver is fixed at (1,-1). We assume a path-loss model with path-loss exponent. The power constraints at the primary and CR transmitters are 10 dB and 20 dB, respectively. Note that we also include the power control as a possible design parameter for the CR transmitter, i.e., the transmission power utilized is not necessarily fixed to its maximum. The unit of rate results is bit per channel use. Further numerical results in [1] show that 1) the proposed 3-phase clean relaying scheme indeed improves the cognitive user’s rate; 2) the proposed achievable scheme is close to capacity when the CR transmitter/receiver is far/close enough to the primary receiver/transmitter, respectively.

Fig. 2. Maximum achievable CR user’s rates as a function of the position of the CR transmitter.



[1] P. -H LIn, F. Gabry, R. Thobaben, E. A. Jorswieck and M. Skoglund, “Multi-Phase Smart Relaying and Cooperative Jamming in Secure Cognitive Radio Networks”, IEEE Transactions on Cognitive Communications and Networking, Vol. 2, No 1 pp. 38-52, Mar. 2016

[2] A. J. Menezes, P. C. van Oorschot, and S. A. Vanstone, Handbook of Applied Cryptography. Boca Raton, FL, USA: CRC Press, 1996.

[3] C. E. Shannon, “Communication theory of secrecy systems”, Bell Syst. Tech. J., vol. 28, no 4, pp. 656-715, Oct. 1949.

[4] A. D. Wyner, “The wire-tap channel”, Bell Syst. Tech. J., vol. 54, no 8, pp. 1355-1387, Oct. 1975.

[5] Y. Liang, A. Somekh-Baruch, H. V. Poor, S. S. Shamai, and S. Verdú, “Capacity of cognitive interference channels with and without secrecy,” IEEE Trans. Inf. Theory, vol. 55, no. 2, pp. 604–619, Feb. 2009.

[6] H. G. Bafghi, S. Salimi, B. Seyfe, and M. R. Aref, “Cognitive interference channel with two confidential messages,” in Proc. IEEE Int. Symp. Inf. Theory Appl. (ISITA), Taichung, Taiwan, 2010, pp. 952–956.

[7] R. K. Farsani and R. Ebrahimpour, “Capacity theorems for the cognitive radio channel with confidential messages,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Honolulu, HI, USA, 2014, pp. 1416–1420.

Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks

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

Siddique Latif¹, Farrukh Pervez¹, Muhammad Usama², Junaid Qadir²

¹National University of Science and Technology, Islamabad
²Information Technology University (ITU), Lahore

The explosive increase in number of smart devices hosting sophisticated applications is rapidly affecting the landscape of information communication technology industry. Mobile subscriptions, expected to reach 8.9 billion by 2022 [1], would drastically increase the demand of extra capacity with aggregate throughput anticipated to be enhanced by a factor of 1000 [2]. In an already crowded radio spectrum, it becomes increasingly difficult to meet ever growing application demands of wireless bandwidth. It has been shown that the allocated spectrum is seldom utilized by the primary users and hence contains spectrum holes that may be exploited by the unlicensed users for their communication. As we enter the Internet Of Things (IoT) era in which appliances of common use will become smart digital devices with rigid performance requirements (such as low latency, energy efficiency, etc.), current networks face the vexing problem of how to create sufficient capacity for such applications. The fifth generation of cellular networks (5G) envisioned to address these challenges are thus required to incorporate cognition and intelligence to resolve the aforementioned issues. Cognitive radios (CRs) and self-organizing wireless networks are two major technologies that are envisaged to meet the future needs of such next generation wireless networks.

CRs are intelligent and fully programmable radios that can dynamically adapt according to their prevalent environment. In other words, they sense the spectrum and dynamically select the clearer frequency bands for better communication in the most prevailing conditions. In this way, CRs can adaptively tune their internal parameters to optimize the spectrum usage, transmission waveform, channel access methods and modulation schemes with enhanced coverage. However, it is due to the recent advancements in machine learning, software defined radio (SDR) that CR is able to emerge from simulation environment to the real-time applications [3].

The overwhelming traffic growth coupled with the greedy approach towards high quality of service (QoS) has been a major challenge for current wireless systems in terms of network resources and QoS. A new paradigm for wireless communication called 5G has been envisioned to address these challenges. The major component of the envisioned 5G scheme is Self-Organizing Network (SON). SON is a relatively new concept in perspective of wireless cellular networks, it refers to an intelligent network that learns from its immediate environment, while autonomously adapting accordingly to ensure reliable communication. In fact, SON underlines new aspect for automation of future networks in 5G era.

The sensing, learning and reasoning behavior of both CRs and SON is achieved by extensively using artificial intelligence (AI) and machine-learning techniques. The CRs are an evolved form of SDRs, realized by the embodiment of cognitive engine (CE) that exploits the AI techniques for the cognitive behavior to decide optimally.

The CR network (CRN) follows the cognitive cycle, for unparalleled resource management and better network performance. Cognitive cycle, as illustrated in figure 1, begins with sensing of dynamic radio environment parameters, subsequently observing and learning recursively the sensed values for reconfiguration of the critical parameters in order to achieve the desired objectives.

Fig. 1: Learning process in cognitive radios

Cognitive cycle is elaborated in figure 2, which highlights the parameters that CR needs to quantify in order to utilize the available spectrum without affecting primary user’s performance. The sensed parameters are treated as stimuli for achieving different performance objectives, for instance, minimizing the bit error rate or minimizing the power consumption etc. [4]. To achieve the aforementioned objectives, CR adaptively learns deciding optimal values for various significant variables such as power control, frequency band allocation, etc. [4].

Fig. 2: The cognitive cycle of CR

CR incorporates machine learning techniques for dynamic spectrum access (DSA) and capacity maximization. AI-based techniques for decision making such as optimization theory, Markov decision processes (MDPs), and game theory is used to encompass a wide range of applications [3]. The popular learning techniques used in cognitive cycle are support vector machine (SVM), artificial neural networks (ANNs), metaheuristic algorithms, fuzzy logic, genetic algorithms, hidden Markov models (HMMs), Bayesian learning, reinforcement learning, multi-agent systems. Fuzzy logic theory has been used for effective bandwidth, resource allocation, interference and power management [3], [5]–[7]. Genetic algorithms (GAs) have been employed for CRs spectrum and parameters optimization [8]–[10]. ANNs have been incorporated to improve the spectrum sensing and adaptively learn complex environments, without substantial overhead [11], [12]. Game theory enables CRNs to learn from its history, scrutinize the performance of other CRNs, and adjust their own behavior accordingly [13], [14]. In multi-agent domain, reinforcement learning (RL) a reward-penalty based technique, which reinforces immediate rewards to maximize long term goals has been employed for efficient spectrum utilization [15], minimum power consumption [16] and filling the spectrum holes dynamically [17]. SVM, a supervised classification model, is being utilized for channel selection [18], adaptation of transmission parameters [19] and beam-forming design [20]. In CRNs, HMMs have been widely used to identify spectrum holes detection [21], spectrum handoff [22], and competitive spectrum access [23]. The range of AI-based techniques are not limited to the above mentioned applications, other applications of AI in CRNs are expressed in [3], [4]. By combining increasing spectrum agility, context aware adaptability of CR and AI techniques, CR has become an increasingly important feature of wireless systems. IEEE 802.16h has recommended CR as one of its key features and a lot of efforts are being made to introduce CR features in 3GPP LTE-Advance.

The rapid proliferation of multi-radio access technology-disparate smart devices has resulted in complicated heterogeneous mobile networks thus making configuration, management and maintenance cumbersome and error-prone. 5G, expected to handle diverse devices at a massive scale, is foreseen as one of the most complicated networks and hence extensive efforts are being carried out for its standardization. In the recent years, SONs, as depicted in figure 3, have gained significant attention regarding self-configuration, self-optimization and self-healing of such complex networks. The idea behind SONs is to automate network planning, configuration and optimization jointly in a single process in order to minimize human involvement. The planning phase, which includes ascertaining cells locations, inter-cell connecting links and other associated network devices as well as parameters, precedes the configuration phase [24]. Self-configuration means that a newly deployed cell is able to automatically configure, test and authenticate itself and adjust its parameters such as transmission power, inter-cell interference etc. in a plug and play fashion [24]. Self-healing allows trouble-free maintenance and enables networks to recover from failures in an autonomous fashion. In addition, it also helps in routine upgrades of the equipments in order to remove legacy bugs. Self-optimization is the ability of the network to keep improving its performance with respect to various aspects including link quality, coverage, mobility and handoff with an objective to achieve network level goals [24]. Since AI-based techniques are capable to handle complex problems in large systems intrinsically, these techniques are now being proposed to achieve Self Organization (SO) in 5G.

Fig. 3: Illustration of AI-based self-organization in the networks

Self-configuration, in cellular networks, refers to the automatic configuration of initial parameters—neighbouring cells list, IP Addresses and radio access parameters—by a node itself. AI techniques like Dynamic Programming (DP), RL and Transfer Learning (TL) may be employed in 5G to automatically configure a series of parameters to render best services. RL, as opposed to DP which initially builds the environment model to operate, is a model free learning technique that iterates through to reach optimal strategy and may yield superior results in dynamically changing radio conditions [25]. Self-healing is about automatic fault detection, its classification and initiating necessary actions for recovery. Irregularities and anomalies in network may be timely spotted to further restore the system by leveraging different AI based sensing techniques like Logistic Regression (LR), SVM and HMM [25]. Self-optimization includes continuous optimization of parameters to achieve system-level objectives such as load balancing, coverage extension, and interference avoidance. AI techniques that may be exploited to optimize provisioning of QoS to various services mainly belong to the class of unsupervised learning. Besides Gradient Boosting Decision Tree (a supervised learning technique), Spectral Clustering, One-class SVM and Recurrent Neural Networks are few examples in this regard [25]. Figure 4 summarizes the AI algorithms that can be utilized to enhance cellular networks performance.

Fig. 4: AI algorithms for 5G

AI techniques may also exploit network traffic patterns to predict future events and help pre-allocate network resources to avoid network overloading. Furthermore, user-centric QoS-provisioning across tiers of heterogeneous cells may also be granted using AI [25]. Similarly, GAs are employed for cell planning and optimization of coverage with power adjustment [26]. GAs are also suited for the problem of finding the shortest path routing in a large scale dynamic networks [27]. Wenjing et al. in [28] proposed an autonomic particle swarm compensation algorithm for cell outage compensation. The study in [29] introduces the self-optimization technique for the transmission power and antenna configuration by exploiting the fuzzy neural network optimization method. It integrates fuzzy neural network with cooperative reinforcement learning to jointly optimize coverage and capacity by intelligently adjusting power and antenna tilt settings [29]. It adopts a hybrid approach in which cells individually optimize respective radio frequency parameters through reinforcement learning in a distributed manner, while a central entity manages to cooperate amongst individual cells by sharing their optimization experience on a network level [29]. Cells iteratively learn to achieve a trade-off between coverage and capacity through optimization, since increase in coverage leads to reduction in capacity, while additionally improving energy efficiency of the network [29]. ANNs can also be effectively utilized for the estimation of link quality [30]. Mobile devices in an indoor environment have also been localized through the use of ANNs [31]. In fact, AI-based techniques enable network entities to automatically configure their initial parameters before becoming operational [24], adaptively learn radio environment parameters to provide optimal services [25], autonomously perform routine maintenance and upgrades and recover from network failures [24],[25].

In view of the continued proliferation of smart devices, we anticipate that CRs and SON will soon become the basic building blocks of future wireless networks. These technologies will transform future networks into an intelligent network that would encompass user preferences alongside network priorities/constraints. AI, being the basis of both these technologies, will continue to drive ongoing 5G standardization efforts and therefore be the cause of a major paradigm shift. AI techniques will continue to intervene future networks finding their usage in from radio resource management to management and orchestration of networks. In fact, we anticipate that future wireless networks would be completely dominated by AI.


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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.

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[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

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.