Applied Mechanics and Materials Vol 573 (2014) pp 429-434 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.573.429
Online: 2014-06-18
Unsupervised Learning for Robust Signal Classification A. Uma Maheswari1,a and Dr.K.Latha2,b 1
Assistant Professor, Dept. of Electronics & Communication Engg, Anna University Chennai-BIT Campus, Tiruchirappalli, Tamilnadu, India. 2
Assistant Professor, Dept. of Computer Science & Engg, Anna University Chennai-BIT Campus, Tiruchirappalli, Tamilnadu, India. a
email:
[email protected] and bemail:
[email protected]
Keywords: Signal classification, Cognitive Radio Networks, dynamic spectrum access, TV white space, VSB, QAM, IEEE 802.22
Abstract. Cognitive Radio Networks (CRNs) have been proposed to increase the efficiency of channel utilization; presently the demand for wireless bandwidth is increased. Cognitive Radio Network can enable the sharing of channels among unlicensed and licensed users on a noninterference basis. An unlicensed user (i.e., secondary user) should monitor for the presence of a licensed user (i.e., primary user) to avoid interfering with a primary user. However to get more gain, an attacker also called selfish secondary user may copy a primary user’s signal to cheat other secondary users. Therefore a primary user detection method is needed to detect the difference between a primary user’s signal and secondary user’s signal. In this paper, unsupervised learning methods such as K-means and SOM techniques are used to classify the signals and also measure the performance parameters such as throughput, end-to-end delay, energy consumption, packet delivery ratio and collision rate of the channel. Introduction Spectrum sensing is the task of finding spectrum holes by sensing the radio spectrum in the local neighborhood of the cognitive radio receiver in an unsupervised manner. To be specific, the task of spectrum sensing involves the following subtasks: i) detection of spectrum holes, ii) spectral resolution of each spectrum hole, iii) estimation of the spatial directions of incoming interference, iv) signal classification. Cognitive Radio (CR) is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones [17]. Dynamic Spectrum Access (DSA) is a new spectrum sharing paradigm that allows secondary users to access the abundant spectrum holes or white spaces in the licensed spectrum bands [7]. DSA is a promising technology to alleviate the spectrum scarcity problem and increase spectrum utilization. It allows unlicensed wireless devices to opportunistically access unoccupied licensed spectrum bands. DSA also called “white space networking” aims to solve the spectrum scarcity problem in wireless communications. In cognitive radio terminology, primary users can be defined as the users who have higher priority or legacy rights on the usage of a specific part the spectrum. On the other hand, secondary users, who have lower priority, exploit this spectrum in such a way that they do not cause interference to primary users. Therefore secondary users need to have cognitive radio capabilities, such as sensing the spectrum reliability to check whether it is being used by a primary user and to change the radio parameters to exploit the unused part of the spectrum. Unlike a conventional radio, a CR has the capability to sense and understand its environment and proactively change its mode of operation as needed. CRs are able to carry out spectrum sensing for the purpose of identifying fallow licensed spectrum i.e., “white spaces”. Once white spaces are identified, CRs opportunistically utilize these white spaces by operating in them without causing interference to primary users. Ensuring the trustworthiness of the spectrum sensing process is a particularly important problem that needs to be addressed. The key to addressing this problem is All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (ID: 132.239.1.231, University of California, San Diego, La Jolla, USA-07/06/15,08:24:22)
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being able to distinguish primary user signals from secondary user signals in a robust way. In a CR network, secondary users are permitted to operate in licensed bands only on a non interference basis to primary users. Because the primary user’s usage of licensed spectrum bands may be sporadic, a CR must constantly monitor for the presence of primary user signals in the current operating band and candidate bands. If a secondary user (with a CR) detects the presence of primary user signals in the current band, it must immediately switch to one of the fallow candidate bands. On the other hand, if the secondary user detects the presence of an unlicensed user, it invokes a coexistence mechanism to share spectrum resources. Classification algorithms based on machine learning techniques fall into one of two major categories: supervised learning and unsupervised learning. In supervised learning training data includes both the inputs and desired outputs. In unsupervised learning, the model is not provided with the correct results during the training. This paper focuses on using both K- means clustering and self organizing maps (SOMs) to perform signal classification in the absence of training data. DTV White Space Usage Example Throughout this paper we will continue to refer to a usage example in order to better demonstrate how signal classification systems operate in a practical scenario. On June 28, 2006, the Senate Commerce Committee adopted “The Advanced Telecommunications and Opportunity Reform Act of 2006”, which built upon the May 2004 Federal Communications Commission (FCC)[18]. It allows the unlicensed devices to utilize unused spectrum in the TV band. Based upon these FCC actions, we have chosen to use a Digital Television (DTV) scenario, where the secondary users are next- generation network devices that have DSA capabilities, the primary users are the DTV signals. Two well defined signal characteristics, as specified by the Advanced Television Systems Committee (ATSC), are the signal bandwidth and modulation. These features would be used by secondary users operating in the DTV spectrum to identify DTV signals. A standard DTV signal uses 6 MHz of bandwidth and local broadcasters are to use 8-level Vestigial Sideband Modulation(8 VSB) for the transmission. The DTV 8VSB modulated signal carries a symbol rate of 10.76 symbols/ second and uses a derivative of Amplitude Modulation (AM) with a portion of the lower sideband cut off. A realistic assumption for secondary signals is that the IEEE 802.22 standard will be used within the DTV whitespace as the next generation regional access network. Features for IEEE 802.2 signals have varying bandwidth s in order to fit into whitespaces and will make use of the same adaptive digital modulation and coding techniques as many of the current commercial wireless technologies. Initially a single type of secondary system is used to focus on the problem of malicious attempts to induce misclassification; we assume only a single type of secondary system. Later X- Means approach can be extended to support simultaneous classification of multiple classes of secondary users [16]. Conventional Method In the conventional method, without using any classification algorithm, they consider three networks. Each and every network has two types of users called registered (primary) users and non registered (secondary) users. Spectrum should send the signals to destination either from primary user or from secondary users. Here the spectrum is unable to find whether the signal is from primary or secondary user. This is the disadvantage in the existing method. In this method packet loss will occur, if the spectrum receives the signal from primary and secondary users at a time. To overcome this problem unsupervised learning technique is introduced in this paper.
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Fig. 1 Snapshot for the network animator for the conventional method Unsupervised Classification Unsupervised Learning has no assumptions about annotated training data. The classifier figures out as it progresses. Examples of unsupervised learning include K-Means clustering and self organizing maps. Self- Organizing Maps using unsupervised learning algorithm to identify hidden patterns in unlabelled input data. Approaches using unsupervised learning have a key advantage over traditional approaches that utilize neural networks and support vector machines because they do not require a training phase. This unsupervised refers to the ability to learn and organize information without providing an error signal to evaluate the potential solution. These algorithms would be useful for scanning an entire frequency band of interest, locating energy, and then feeding features from that energy into an untrained classifier. With no training phase, we learn from live data. The fundamental assumption is that signals of the same class will have similar values in feature space and signals of different classes will have dissimilar values. In this paper a DTV signal is acting as a primary user and a secondary user employing BPSK modulation.
Fig. 2 Snapshot for the network animator for clustering the networks with users In Fig. 2, Initially clusters are formed with separate nodes. Color variation is shown to differentiate the clusters. Each and every node consists of an energy value. Fig. 3 shows that each cluster has separate primary and secondary users.
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Fig. 3 Snapshot for the network animator for cluster has PU and SU. Simulation Results In this paper, we considered a primary user is DTV signal and secondary user is BPSK modulated signal. The secondary user must wait until the spectrum became free. If the secondary user hacks primary user’s properties and trying to send signal to spectrum, spectrum find automatically that it hacked data. Immediately it displays the misuse. Fig. 4 and Fig.5 shows this operation.
Fig. 4 Snapshot for the network animator for detecting hack data
Fig. 5 Snapshot for the network animator for detecting misuse
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Threats to Unsupervised Learning Before we can say about the attack simulations, we must define the signals used in simulation. Consider an 8VSB primary user, seeking to copy a DTV transmission. Consider multiple classes of secondary users: 16 QAM and OFDM. The OFDM signal is the malicious agent, wishing to be misclassified as a primary user. The connection attack seeks to confuse the classifier by blurring two classes together [3]. To do this, an adversary adds chaff points by transmitting signals with specific features in the region between the two classes it wishes to confuse. For the simulation, the point cluster class was generated using an OFDM signal of very narrow bandwidth. The goal of the point cluster attack is to focus a significant number of chaff points all in a single area. This attack is highly effective in causing complete misclassification. Both the K-means and SOM based classifiers are equally ill-equipped to handle this attack[15]. In the random noise attack, chaff points are randomly distributed uniformly, over the probability space. This can be difficult to achieve operationally, depending on the types of features used. Outputs For K-means algorithm
Fig. 6 Throughput
End-to-end
Fig. 8 Energy consumption
Fig. 9 Packetdelivery- ratio
Fig. 10 Collision rate
Fig. 12 End to end delay
Fig. 13 Energy consumption
Fig. 14 Packet delivery ratio
Fig. 15 Collision rate
Fig. 7 delay
For SOM algorithm
Fig. 11 Throughput
Summary In this paper, we have simulated the CR environment for the licensed and unlicensed users and obtained the above graphs for the measure of the performance parameters. The output graphs generated using the K-means algorithm proves to be more efficient, for the primary users with respect to the performance parameters such as throughput,end-to-end delay,energy consumption, packet delivery ratio, than the SOM algorithm. Whereas it remains the same for the collision rate parameter using both the algorithms.
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References [1] T. Charles Clancy, A.Khawar and Timothy R. Newman, “Robust Signal Classification Using Unsupervised Learning," IEEE Transactions on wiresless communications., vol. 10, No. 4,pp.12891299, April 2011 [2] T. Charles Clancy, Timothy R. Newman, “Robust Signal classification using unsupervised learning”, April 2009. [3] T.Newman and T.Clancy, “ Security threats to cognitive radio signal classifiers, “ in Vitgina Tech Wireless Personal Commun Symp., June 2009. [4] A.Khawar and T.Clancy, “Signal classifier using self-organising maps: performance and robustness,” in SDR Forum Technical Conf., Dec 2009. [5] T.Clancy and A.Khawar, “Security threats to cognitive radio radio signal classifiers,” in International Conf. Cognitive Radio Oriented wireless Netw.Commun., June 2009. [6] A.Fehske, J.Gaeddert and J.Reed, “ A new approach to signal classification using signal correlation and neural networks,” in New Frontiers Dynamic Spectrum access Netw., Nov 2005. [7] T.Yucek and H.Arslan.”A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Commun Surveys Tutorials, vol.11 Mar 2009. [8] R.Chen, J.Park and J.Reed, “Defence against primary user emulation attacks in cognitive radio networks,” IEEE J.Sel. Areas Commun, vol 26,pp.25-37, Jan 2008. [9] S.Kay and J.Marple “Spectrum analysis-a modem perspective,”proc. IEEE, vol.69,pp. 13801419, Nov. 1981 [10]W.A.Gardner, Stasistical Spectal Analysis: A Nonprobabilistic Theory. Prentice Hall. 1988. [11]W.A.Gardner,Introduction to Random Process with Applications to Signals and System. Macmillan. 1996. [12]P.Sutton.K Nolan and L.Dovle. “Cyclostationary signatures for rendezyous in OFDM-based dynamic spectrum access networks. “2nd IEEE International Symp, New Frontiers Dynamic Spectrum Access New. Pp.220-231, Apr.2007. [13]J.Hartigan and M.Wong, “A K-means clustering algorithm,”Applied Stastistics, vol.28 pp.100108,1979 [14]T.Kohonen. “The self-organizing map, “Neurocomputing, vol.21, pp.1-6 Nov-1998 [15]T.Clancy and N.Geoergen, “Security in congnitive radio networks:threats and mitigation, “in International Conf. Cognitive Radio Oriented Wireless Netw. Commun. May 2008. [16]D.Pelleg and A.Moore, “X-means: extending k-means with efficient estimation of the number of clusters, “in International Conf. Machine Learning.June 2000. [17] J.Mitola,” Cognitive radio: an integrated agent architecture for software defined radio,” Ph.D dissertation, KTH 2000. [18] FCC, “FCC adopts rules for unlicensed use of television white spaces".Available: http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-04-113A1.pdf.
Advancements in Automation and Control Technologies 10.4028/www.scientific.net/AMM.573
Unsupervised Learning for Robust Signal Classification 10.4028/www.scientific.net/AMM.573.429
