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.

References

[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.

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

Siddique Latif^¹, Farrukh Pervez^¹, Muhammad Usama^², Junaid Qadir^²

^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.

References

[1] P. Cerwall, “Ericsson mobility report, mobile world congress edition,” 2016.
[2] J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. Soong, and J. C. Zhang, “What will 5G be?” IEEE Journal on selected areas in communications, vol. 32, no. 6, pp. 1065–1082, 2014.
[3] J. Qadir, “Artificial intelligence based cognitive routing for cognitive radio networks,” Artificial Intelligence Review, vol. 45, no. 1, pp. 25–96, 2016.
[4] N. Abbas, Y. Nasser, and K. El Ahmad, “Recent advances on artificial intelligence and learning techniques in cognitive radio networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1, p. 174, 2015.
[5] P. Kaur, M. Uddin, and A. Khosla, “Fuzzy based adaptive bandwidth allocation scheme in cognitive radio networks,” in Knowledge Engineering, 2010 8th International Conference on ICT and. IEEE, 2010, pp. 41–45.
[6] S. R. Aryal, H. Dhungana, and K. Paudyal, “Novel approach for interference management in cognitive radio,” in Internet (AH-ICI), 2012 Third Asian Himalayas International Conference on. IEEE, 2012, pp. 1–5.
[7] M. Matinmikko, J. Del Ser, T. Rauma, and M. Mustonen, “Fuzzy-logic based framework for spectrum availability assessment in cognitive radio systems, IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2173–2184, 2013.
[8] H. Qin, L. Zhu, and D. Li, “Artificial mapping for dynamic resource management of cognitive radio networks,” in Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on. IEEE, 2012, pp. 1–4.
[9] S. Chen, T. R. Newman, J. B. Evans, and A. M. Wyglinski, “Genetic algorithm-based optimization for cognitive radio networks,” in Sarnoff Symposium, 2010 IEEE. IEEE, 2010, pp. 1–6.
[10] M. R. Moghal, M. A. Khan, and H. A. Bhatti, “Spectrum optimization in cognitive radios using elitism in genetic algorithms,” in Emerging Technologies (ICET), 2010 6th International Conference on. IEEE, 2010, pp. 49–54.
[11] X. Tan, H. Huang, and L. Ma, “Frequency allocation with artificial neural networks in cognitive radio system,” in TENCON Spring Conference, 2013 IEEE. IEEE, 2013, pp. 366–370.
[12] T. Zhang, M. Wu, and C. Liu, “Cooperative spectrum sensing based on artificial neural network for cognitive radio systems,” in Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on. IEEE, 2012, pp. 1–5.
[13] Z. Han, Game theory in wireless and communication networks: theory, models, and applications. Cambridge University Press, 2012.
[14] D. Bellhouse, “The problem of waldegrave,” Electronic Journal for the History of Probability and Statistics, vol. 3, no. 2, pp. 1–12, 2007.
[15] S. S. Barve and P. Kulkarni, “Dynamic channel selection and routing through reinforcement learning in cognitive radio networks,” in Computational Intelligence & Computing Research (ICCIC), 2012 IEEE International Conference on. IEEE, 2012, pp. 1–7.
[16] P. Zhou, Y. Chang, and J. A. Copeland, “Reinforcement learning for repeated power control game in cognitive radio networks,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 1, pp. 54–69, 2012.
[17] S. Arunthavanathan, S. Kandeepan, and R. J. Evans, “Reinforcement learning based secondary user transmissions in cognitive radio networks,” in 2013 IEEE Globecom Workshops (GC Wkshps). IEEE, 2013, pp. 374–379.
[18] Y. Huang, H. Jiang, H. Hu, and Y. Yao, “Design of learning engine based on support vector machine in cognitive radio,” in Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on. IEEE, 2009, pp. 1–4.
[19] S. Hu, Y.-d. Yao, and Z. Yang, “Mac protocol identification using support vector machines for cognitive radio networks,” IEEE Wireless Communications, vol. 21, no. 1, pp. 52–60, 2014.
[20] M. Lin, J. Ouyang, and W.-P. Zhu, “BF design in cognitive relay networks via support vector machines,” in 2013 IEEE Global Communications Conference (GLOBECOM). IEEE, 2013, pp. 3247–3252.
[21] A. Mukherjee, S. Maiti, and A. Datta, “Spectrum sensing for cognitive radio using blind source separation and hidden markov model,” in 2014 Fourth International Conference on Advanced Computing & Communication Technologies. IEEE, 2014, pp. 409–414.
[22] C. Pham, N. H. Tran, C. T. Do, S. I. Moon, and C. S. Hong, “Spectrum handoff model based on hidden markov model in cognitive radio networks,” in The International Conference on Information Networking 2014 (ICOIN2014). IEEE, 2014, pp. 406–411.
[23] X. Li and C. Xiong, “Markov model bank for heterogenous cognitive radio networks with multiple dissimilar users and channels,” in International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, 2014. IEEE, 2014, pp. 93–97.
[24] X. Wang, X. Li, and V. C. Leung, “Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges,” IEEE Access, vol. 3, pp. 1379–1391, 2015.
[25] R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and H. Zhang, “Intelligent 5G: When cellular networks meet artificial intelligence.”
[26] L. T. Ho, I. Ashraf, and H. Claussen, “Evolving femtocell coverage optimization algorithms using genetic programming,” in 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2009, pp. 2132–2136.
[27] U. Mehboob, J. Qadir, S. Ali, and A. Vasilakos, “Genetic algorithms in wireless networking: techniques, applications, and issues,” Soft Computing, vol. 20, no. 6, pp. 2467–2501, 2016.
[28] L. Wenjing, Y. Peng, J. Zhengxin, and L. Zifan, “Centralized management mechanism for cell outage compensation in lte networks,” International Journal of Distributed Sensor Networks, 2012.
[29] S. Fan, H. Tian, and C. Sengul, “Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning,” EURASIP Journal on Wireless Communications and Networking, vol. 2014, no. 1, pp. 1–14, 2014.
[30] M. Caleffi and L. Paura, “Bio-inspired link quality estimation for wireless mesh networks,” in IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops, 2009. WoWMoM 2009. IEEE, 2009, pp. 1–6.
[31] N. Ahad, J. Qadir, and N. Ahsan, “Neural networks in wireless networks: Techniques, applications and guidelines,” Journal of Network and Computer Applications, vol. 68, pp. 1–27, 2016.

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: Ankit.Kaushik@kit.edu

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.

References
[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: http://www.ericsson.com/res/docs/2015/mobility-report/ericsson-mobility-report-nov-2015.pdf
[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.

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
smahmood@stevens.edu

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.

References

[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
Email: smahmood@stevens.edu

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.

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