A Cooperative Sensing Method Using Katz Fractal Dimension in Frequency Domain Jun AN, Lu-yong ZHANG, Pei-pei ZHU and Dian-jun CHEN
|
|
- Rosanna Nelson
- 5 years ago
- Views:
Transcription
1 206 International Conference on Wireless Communication and Network Engineering (WCNE 206) ISBN: A Cooperative Sensing Method Using Katz Fractal Dimension in Frequency Domain Jun AN, Lu-yong ZHANG, Pei-pei ZHU and Dian-jun CHEN Beijing University of Post and Telecommunications, China Keywords: Cognitive radio, Fractal theory, Cooperative perception, Katz fractal dimension. Abstract. Under the circumstances that signal and the noise have different Katz fractal dimension in frequency domain, single Secondary User can make a quick decision about the existence of the Primary User. By using the cooperation sensing method based on Katz fractal dimension in frequency domain, it can fuse the decision of multiple users and give a more reliable judgment. It is possible to enhance the detection performance, while contrast detect method of box dimension, it can get a higher probability of detection under the lower SNR situation. Introduction Cognitive radio technology provide many viable solutions for the growing tension in the spectrum resources, the core idea is CR (Cognitive Radio) has a self-learning capabilities, it can make corresponding adjustments according to the spectral changes in the surrounding environment, without prejudice to the primary the user's normal communication. It can dynamically manage the spectrum and make full use of the spectrum. Wherein the spectrum sensing technology is an important part of CR, it is the basis for spectrum allocation, spectrum of switching. Currently several single-user spectrum sensing technologies include energy detection technology, matched filtering detection, Cyclic feature detection. Energy detection is relatively simple and easy to implement, is now widely used method, but this method has some limitations in the case of low SNR. Matched filter detection can achieve better detection results, but users need to know a priori information. Cyclic feature detection distinguishes the noise and signal by using the fact that a signal having a stable cycle characteristics, noise and interference does not have this property. But this method requires a large amount of calculation, the calculation method is complicated. Cooperative sensing method improve the sensing performance by fusing the decisions that multiple users have made and it can finally give a more reliable judgment [,2]. Fractal dimension can quantify the complexity of Fractal Set, communication signal can be described by fractal dimension because it is time sequences or frequency sequences and have complexity and randomness. There are many kinds of computing definitions, include Hausdorff dimension and box dimension, box dimension calculation comparing the Hausdorff dimension is more simple, commonly used to describe the complexity of the fractal signal by many papers. Paper [3] proposed an algorithm for fast calculation of fractal box dimension, paper[8] proposed a spectrum sensing method based on fractal box dimension, paper[6] proposed a sensing method based on Constant False Alarm Rate (CRAR) Algorithm and fractal box dimension. The spectrum sensing method as these papers has mentioned are ineffective when the SNR condition is low. Sevcik proposed a method to calculate the fractal dimension that target on the nonlinear systems [3], namely Sevcik fractal dimension, this method can quickly calculate the fractal dimension of the signal sequence, since the noise signal and the modulation signal have different Sevcik fractal dimension in the frequency domain, this method can be used to distinguish each other. Paper [7] proposed a method that using Katz fractal dimension in frequency domain to quickly sense the spectrum, this method made a good sensing performance in the low SNR single-user situation. By studying Katz fractal dimension[], a method is proposed based on the Katz fractal dimension in frequency domain and cooperative spectrum sensing in this paper. This method distinguishes between modulation signal and noise by measuring the difference there are shown in
2 Katz fractal dimension in frequency domain, then combine all the local judgment made by every secondary users in CR during the data fusing process to get the optimal results. Katz Fractal Dimension The calculation method of Katz fractal dimension is as follows [4]: By the definition of fractal dimension, we can get conclusion: The is the length of waveform, and is the diameter estimated as the distance between the first point of the sequence and the point of the sequence that provides the farthest distance. can be expressed as follow: (2) Considering the distance between each point of the sequence and the first, point is the one that maximizes the distance with respect to the first point. The fractal dimension compares the actual number of units that compose a waveform with the minimum number of units required to reproduce a pattern of the same spatial extent. Fractal dimension computed in this fashion depend upon the measurement units used. If the units are different, then so are the fractal dimension. Katz s approach solves this problem by creating a general unit or yardstick: the average step or average distance between successive points,. Normalizing distances in () by this average results in follow: Defining as the number of steps in the waveform, then, and (3) can be written as follow: Expression (6) summarizes Katz s approach to calculate the fractal dimension of a waveform. Methods Based on Cognitive Radio and Fractal Dimension Cooperative Sensing Model Assuming that the number of SU (Secondary User) is M in CR network, the sensing action of each SU is independent, when the local sensing is complete, the local sensing result will be sent to data fusing center, in data fusing center, all the local sensing results will be fused to one final decision by a specific policy. The model for cooperative sensing is given as follow[2]: () (3) (4) Where is the signal received by the i-th SU in k time slot, represent the absence of the (5) PU(Primary User) and the SU can use this frequency band, and the SU can not use this frequency band, we assume represent the presence of the PU is AWGN which mean is zero and variance is [0].
3 Katz Fractal Dimension in Frequency domain Katz Fractal Dimension in Frequency Domain Frequency Domain Katz Fractal Dimension With N=5000 number of samples, we simulate the Katz fractal dimension in frequency domain of AWGN in different power of noise, the Figure shows that the Katz fractal dimension in frequency domain of noise is stable in the range of.029. Then we simulate the Katz fractal dimension in frequency domain of some modulation signals in different SNR situation, the Figure 2 shows that with the improvement of SNR, the Katz fractal dimension in frequency domain of modulation signal will decrease. So we can distinguish the noise and the signal by its Katz fractal dimension in frequency domain when SNR arrive a specific threshold. Which means we can know whether the PU is existing in CR network Noise Variance Figure. The Katz fractal dimension in frequency domain of AWGN in different power of noise ASK 2PSK 2FSK SNR Figure 2. The Katz fractal dimension in frequency domain of signals in different SNR. We assume that the Katz fractal dimension in frequency domain of received signal is D, so we can judge the presence of PU by D as follow: (6) is the threshold of judgment, As the Figure 2 shows, when there are signal existing, its D is continually decreasing when SNR is rising, so represent presence of PU, represent absence of PU.
4 Katz Fractal Dimension in Frequency domain average Pf=0.05 Pf=0.03 Pf= sequence length # 0 4 Figure 3. Determination of threshold. When the number of samples N is changing from 000 to 20000, Monte Carlo simulation times is 000. As Figure shows that the Katz fractal dimension in frequency domain of AWGN is not relevant to the power of noise, and Figure 3 shows that the Katz fractal dimension in frequency domain of AWGN is relevant to N, so we can determine the λ of equation (6) by knowing the N and probability of false alarm. Cooperative Sensing Method Based on Katz Fractal Dimension in Frequency Domain Following are the processes when using the cooperative sensing method based on Katz fractal dimension in frequency domain, we assume that each SU i, for, performs spectrum sensing individually:. Each SU sample the received signal, according to the number of samples and probability of false alarm, SU determines the of formula (9). 2. Each SU calculates, is the Katz fractal dimension in frequency domain of i-th SU received signal samples, each SU make the local judgment. 3. Each SU sends its local judgment result to data fusing center, data fusing center fusing all the local results by Hard decision guideline, then get the final judgment. (7) The whole process is given by Figure 4: (8) Figure 4. The process of Cooperative Sensing Method Based on Katz Fractal Dimension in Frequency Domain.
5 Pd Pd Simulation Results In this section, we present simulation results to demonstrate the performance of the cooperative method based on Katz fractal dimension in frequency domain. We concentrate on on the AWGN channels, and we assume that each SU has same noise condition, PU modulate the signal using BPSK, the frequency of carrier wave is khz, the sampling frequency is 0kHz, the probability of false alarm is, the number of samples are N=5000, the number of cooperative SU are 5, can be implied by Figure 3. A. When SNR ranges from -25dB to 0dB, we compare four spectrum sensing methods of spectrum sensing. According to the Figure 5, the probability of detection of cooperative sensing method based on Katz fractal dimension in frequency domain is better than the rest of methods. Its detection rate has been greatly improved even at -20dB SNR Katz single-user Katz multi-user BoxDimension single-user BoxDimension multi-user SNR Figure 5. The probability of detection in different SNR condition. B. When SNR is set as -20dB and changes from to, the ROC curve of four spectrum sensing method can be obtained as Figure 6 shows. We can know that the cooperative sensing method based on Katz fractal dimension in frequency domain is better than the rest of spectrum sensing methods when is confirmed Katz single-user Katz multi-user BoxDimension single-user BoxDimension multi-user Pf Figure 6. ROC curves of four spectrum sensing methods. C. When the number of cooperative SU is set as, 3, 5. The probability of detection will be improved when the number of cooperative SU is increased according to Figure 7. But we should also pay attention to the question that too many cooperative SU will lower the CR performance.
6 Pd user 3 user user SNR Figure 7. The probability of detection when the number of cooperative SU is different. Conclusion In this paper, the cooperative sensing method is based on Katz fractal dimension in frequency domain, we target on the disadvantage of single user when using Katz fractal dimension in frequency domain. The simulation results show that our method improves the probability of detection effectively, and it have a great performance in the lower SNR condition. Our method improves the spectrum sensing ability of CR system. In the follow-up work, we can target on the fractal dimension of different model of modulation signal and different type of noise. Acknowledgment This work was supported by the National Natural Science Foundation of China (637906, 64706). References [] Du Hong. Research on Spectrum Sensing Optimization and Radio Resource Management in Cognitive Radio[D]. Beijing University of Posts and Telecommunications, 202. [2] Amal S Kannan, Ebin M. Manuel. Performance analysis of blind spectrum sensing in cooperative environment[j]. Control Communication and Computing(ICCC) [3] Lv Tiejun, Guo Shuangbing, Xiao Xianci. Research on fractal characteristics of modulated signal[j]. Science in China(Series E), 200. [4] Carlos Katz. A procedure to Estimate the Fractal Dimension of Waveforms[J]. Complexity International, 998. [5] Chen Xiaobo, Chen Hong, Cai Xiaoxia. Double Threshold Cooperative Spectrum Sensing Method Based on Fractal Box Dimension[J]. Telecommunication Engineering, 20. [6] Liu Wentao, Chen Hong, Cai Xiaoxia. Constant False Alarm Detection Method Based on Fractal Box Dimension[J]. Electronic Countermeasure Technology, 203. [7] Fu Shuang, Li Yibing, Ye Fang. Fast Blind Spectrum Sensing based on Katz Fractal Dimension in Frequency Domain[J]. Journal of Jilin University(Engineering and Technology Edition), 204. [8] Zhao Chunhui, Ma Shuang, Yang Weichao. Spectrum Sensing in Cognitive Radios Based on Fractal Box Dimension[J]. Journal of Electronics & Information Technology, 20. [9] Katz M J, Fractals and the Analysis of Waveforms[J]. Computers in Biology and Medicine, 988.
7 [0] Tandra R, Sahai A., SNR Walls for Signal Detection[J]. IEEE Journal of Selected Topics in Signal Processing, [] Gnitecki J, Moussavi Z, The Fractality of Lung Sounds: A Comparison of Three Waveform Fractal Dimension Algorithms[J]. Chaos, Solitons & Fractals, [2] Hu X N, Wu G F, Hu H Y, Cooperative Spectrum Sensing with Double Threshold under Noise Uncertainty[J]. Computer Engineering and Applications, 202.
Energy Detection Technique in Cognitive Radio System
International Journal of Engineering & Technology IJET-IJENS Vol:13 No:05 69 Energy Detection Technique in Cognitive Radio System M.H Mohamad Faculty of Electronic and Computer Engineering Universiti Teknikal
More informationEffect of Time Bandwidth Product on Cooperative Communication
Surendra Kumar Singh & Rekha Gupta Department of Electronics and communication Engineering, MITS Gwalior E-mail : surendra886@gmail.com, rekha652003@yahoo.com Abstract Cognitive radios are proposed to
More informationDemonstration of Real-time Spectrum Sensing for Cognitive Radio
Demonstration of Real-time Spectrum Sensing for Cognitive Radio (Zhe Chen, Nan Guo, and Robert C. Qiu) Presenter: Zhe Chen Wireless Networking Systems Laboratory Department of Electrical and Computer Engineering
More informationCooperative Spectrum Sensing in Cognitive Radio
Cooperative Spectrum Sensing in Cognitive Radio Project of the Course : Software Defined Radio Isfahan University of Technology Spring 2010 Paria Rezaeinia Zahra Ashouri 1/54 OUTLINE Introduction Cognitive
More informationSpectrum Sensing Using Bayesian Method for Maximum Spectrum Utilization in Cognitive Radio
5 Spectrum Sensing Using Bayesian Method for Maximum Spectrum Utilization in Cognitive Radio Anurama Karumanchi, Mohan Kumar Badampudi 2 Research Scholar, 2 Assoc. Professor, Dept. of ECE, Malla Reddy
More informationJournal of Asian Scientific Research DEVELOPMENT OF A COGNITIVE RADIO MODEL USING WAVELET PACKET TRANSFORM - BASED ENERGY DETECTION TECHNIQUE
Journal of Asian Scientific Research ISSN(e): 2223-1331/ISSN(p): 2226-5724 URL: www.aessweb.com DEVELOPMENT OF A COGNITIVE RADIO MODEL USING WAVELET PACKET TRANSFORM - BASED ENERGY DETECTION TECHNIQUE
More informationStudy on OFDM Symbol Timing Synchronization Algorithm
Vol.7, No. (4), pp.43-5 http://dx.doi.org/.457/ijfgcn.4.7..4 Study on OFDM Symbol Timing Synchronization Algorithm Jing Dai and Yanmei Wang* College of Information Science and Engineering, Shenyang Ligong
More informationCooperative Spectrum Sensing and Decision Making Rules for Cognitive Radio
ISSN (Online) : 2319-8753 ISSN (Print) : 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology Volume 3, Special Issue 3, March 2014 2014 International Conference
More informationBER Performance Analysis of Cognitive Radio Network Using M-ary PSK over Rician Fading Channel.
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 12, Issue 3, Ver. I (May.-Jun. 2017), PP 39-43 www.iosrjournals.org BER Performance Analysis
More informationEnergy Detection Spectrum Sensing Technique in Cognitive Radio over Fading Channels Models
Energy Detection Spectrum Sensing Technique in Cognitive Radio over Fading Channels Models Kandunuri Kalyani, MTech G. Narayanamma Institute of Technology and Science, Hyderabad Y. Rakesh Kumar, Asst.
More informationOverview. Cognitive Radio: Definitions. Cognitive Radio. Multidimensional Spectrum Awareness: Radio Space
Overview A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications Tevfik Yucek and Huseyin Arslan Cognitive Radio Multidimensional Spectrum Awareness Challenges Spectrum Sensing Methods
More informationReview of Energy Detection for Spectrum Sensing in Various Channels and its Performance for Cognitive Radio Applications
American Journal of Engineering and Applied Sciences, 2012, 5 (2), 151-156 ISSN: 1941-7020 2014 Babu and Suganthi, This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0
More informationIMPLEMENTATION OF CYCLIC PERI- ODOGRAM DETECTION ON VEE FOR COG- NITIVE
IMPLEMENAION OF CYCLIC PERI- ODOGRAM DEECION ON VEE FOR COG- NIIVE Agilent echnologies IMPLEMENAION OF CYCLIC PERIODOGRAM DEECION ON VEE FOR COGNIIVE RADIO Zaichen Zhang and iaodan u National Mobile Communications
More informationCo-Operative Spectrum Sensing In Cognitive Radio Network in ISM Band
Co-Operative Spectrum Sensing In Cognitive Radio Network in ISM Band 1 D.Muthukumaran, 2 S.Omkumar 1 Research Scholar, 2 Associate Professor, ECE Department, SCSVMV University, Kanchipuram ABSTRACT One
More informationOPTIMIZATION OF SPECTRUM SENSING IN COGNITIVE RADIO BY DEMAND BASED ADAPTIVE GENETIC ALGORITHM
OPTIMIZATION OF SPECTRUM SENSING IN COGNITIVE RADIO BY DEMAND BASED ADAPTIVE GENETIC ALGORITHM Subhajit Chatterjee 1 and Jibendu Sekhar Roy 2 1 Department of Electronics and Communication Engineering,
More informationEfficient Multi Stage Spectrum Sensing Technique For Cognitive Radio Networks Under Noisy Condition
Efficient Multi Stage Spectrum Sensing Technique For Cognitive Radio Networks Under Noisy Condition Gajendra Singh Rathore 1 M.Tech (Communication Engineering), SENSE VIT University, Chennai Campus Chennai,
More informationIMPROVED PROBABILITY OF DETECTION AT LOW SNR IN COGNITIVE RADIOS
87 IMPROVED PROBABILITY OF DETECTION AT LOW SNR IN COGNITIVE RADIOS Parvinder Kumar 1, (parvinderkr123@gmail.com)dr. Rakesh Joon 2 (rakeshjoon11@gmail.com)and Dr. Rajender Kumar 3 (rkumar.kkr@gmail.com)
More informationCYCLOSTATIONARITY BASED SIGNAL DETECTION IN COGNITIVE RADIO NETWORKS
CYCLOSTATIONARITY BASED SIGNAL DETECTION IN COGNITIVE RADIO NETWORKS 1 ALIN ANN THOMAS, 2 SUDHA T 1 Student, M.Tech in Communication Engineering, NSS College of Engineering, Palakkad, Kerala- 678008 2
More informationThe Measurement and Analysis of Bluetooth Signal RF Lu GUO 1, Jing SONG 2,*, Si-qi REN 2 and He HUANG 2
2017 2nd International Conference on Wireless Communication and Network Engineering (WCNE 2017) ISBN: 978-1-60595-531-5 The Measurement and Analysis of Bluetooth Signal RF Lu GUO 1, Jing SONG 2,*, Si-qi
More informationPerformance Evaluation of BPSK modulation Based Spectrum Sensing over Wireless Fading Channels in Cognitive Radio
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 9, Issue 6, Ver. IV (Nov - Dec. 2014), PP 24-28 Performance Evaluation of BPSK modulation
More informationCognitive Radio Techniques
Cognitive Radio Techniques Spectrum Sensing, Interference Mitigation, and Localization Kandeepan Sithamparanathan Andrea Giorgetti ARTECH HOUSE BOSTON LONDON artechhouse.com Contents Preface xxi 1 Introduction
More informationA Wideband Spectrum Data Compression Algorithm base on Energy Detection
Appl. Math. Inf. Sci. 9, No. 1, 419-424 (215) 419 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/1.12785/amis/9149 A Wideband Spectrum Data Compression Algorithm
More informationCognitive Ultra Wideband Radio
Cognitive Ultra Wideband Radio Soodeh Amiri M.S student of the communication engineering The Electrical & Computer Department of Isfahan University of Technology, IUT E-Mail : s.amiridoomari@ec.iut.ac.ir
More informationImplementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization
www.semargroups.org, www.ijsetr.com ISSN 2319-8885 Vol.02,Issue.11, September-2013, Pages:1085-1091 Implementation of Cognitive Radio Networks Based on Cooperative Spectrum Sensing Optimization D.TARJAN
More informationContinuous Monitoring Techniques for a Cognitive Radio Based GSM BTS
NCC 2009, January 6-8, IIT Guwahati 204 Continuous Monitoring Techniques for a Cognitive Radio Based GSM BTS Baiju Alexander, R. David Koilpillai Department of Electrical Engineering Indian Institute of
More informationSpectrum Sensing Using OFDM Signal and Cyclostationary Detection Technique In Cognitive Radio
ISSN: 2319-7463, Vol. 5 Issue 4, Aril-216 Spectrum Sensing Using OFDM Signal and Cyclostationary Detection Technique In Cognitive Radio Mudasir Ah Wani 1, Gagandeep Singh 2 1 M.Tech Student, Department
More informationPERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME FOR COOPERATIVE SPECTRUM SENSING IN CR
Int. Rev. Appl. Sci. Eng. 8 (2017) 1, 9 16 DOI: 10.1556/1848.2017.8.1.3 PERFORMANCE MEASUREMENT OF ONE-BIT HARD DECISION FUSION SCHEME FOR COOPERATIVE SPECTRUM SENSING IN CR M. AL-RAWI University of Ibb,
More informationResearch on Development & Key Technology of PLC
Research on Development & Key Technology of PLC Jie Chen a, Li Wang b College of Electronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; avircochen@foxmail.com,
More informationRESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS
Abstract of Doctorate Thesis RESEARCH ON METHODS FOR ANALYZING AND PROCESSING SIGNALS USED BY INTERCEPTION SYSTEMS WITH SPECIAL APPLICATIONS PhD Coordinator: Prof. Dr. Eng. Radu MUNTEANU Author: Radu MITRAN
More informationSPECTRUM SENSING BY CYCLO-STATIONARY DETECTOR
SPECTRUM SENSING BY CYCLO-STATIONARY DETECTOR 1 NIYATI SOHNI, 2 ANAND MANE 1,2 Sardar Patel Institute of technology Mumbai, Sadar Patel Institute of Technology Mumbai E-mail: niyati23@gmail.com, anand_mane@spit.ac.in
More informationReview On: Spectrum Sensing in Cognitive Radio Using Multiple Antenna
Review On: Spectrum Sensing in Cognitive Radio Using Multiple Antenna Komal Pawar 1, Dr. Tanuja Dhope 2 1 P.G. Student, Department of Electronics and Telecommunication, GHRCEM, Pune, Maharashtra, India
More informationCooperative Spectrum Sensing and Spectrum Sharing in Cognitive Radio: A Review
International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] Cooperative Spectrum Sensing and Spectrum Sharing in Cognitive Radio: A Review
More informationAN EFFECTIVE WIDEBAND SPECTRUM SENSING METHOD BASED ON SPARSE SIGNAL RECONSTRUC- TION FOR COGNITIVE RADIO NETWORKS
Progress In Electromagnetics Research C, Vol. 28, 99 111, 2012 AN EFFECTIVE WIDEBAND SPECTRUM SENSING METHOD BASED ON SPARSE SIGNAL RECONSTRUC- TION FOR COGNITIVE RADIO NETWORKS F. L. Liu 1, 2, *, S. M.
More informationResource Allocation for Delay Minimization for Cognitive Radio using M-QAM, AWGN Model
ISSN: 2454-2377, Resource Allocation for Delay Minimization for Cognitive Radio using M-QAM, AWGN Model Sonu Dabas 1 & Amanpreet Kaur 2 1 Student, EECE Department, The North Cap University, Gurugram, India
More informationDetection of an LTE Signal Based on Constant False Alarm Rate Methods and Constant Amplitude Zero Autocorrelation Sequence
Detection of an LTE Signal Based on Constant False Alarm Rate Methods and Constant Amplitude Zero Autocorrelation Sequence Marjan Mazrooei sebdani, M. Javad Omidi Department of Electrical and Computer
More informationOpen Access AOA and TDOA-Based a Novel Three Dimensional Location Algorithm in Wireless Sensor Network
Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 2015, 7, 1611-1615 1611 Open Access AOA and TDOA-Based a Novel Three Dimensional Location Algorithm
More informationCooperative Spectrum Sensing in Cognitive Radio using Flower Pollination Optimization Algorithm
Cooperative Spectrum Sensing in Cognitive Radio using Flower Pollination Optimization Algorithm Sudhir Shukla #1, Amandeep Singh Bhandari * 1 M.Tech, Scholar Department of ECE, Punjabi University Patiala,
More informationData Fusion Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks
Data Fusion Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networs D.Teguig ((2, B.Scheers (, and V.Le Nir ( Royal Military Academy Department CISS ( Polytechnic Military School-Algiers-Algeria
More informationDesign of Spread-Spectrum Communication System Based on FPGA
Sensors & Transducers 203 by IFSA http://www.sensorsportal.com Design of Spread-Spectrum Communication System Based on FPGA Yixin Yan, Xiaolei Liu, 2* Xiaobing Zhang College Measurement Control Technology
More informationA Research on Implementing GPS to Synchronize Sampling in a Disturbed Phase Difference s High-precision Measure System for Insulation Testing
International Conference on Advances in Energy and Environmental Science (ICAEES 05) A Research on Implementing GPS to Synchronize Sampling in a Disturbed Phase Difference s High-precision Measure System
More informationOPTIMUM RELAY SELECTION FOR COOPERATIVE SPECTRUM SENSING AND TRANSMISSION IN COGNITIVE NETWORKS
OPTIMUM RELAY SELECTION FOR COOPERATIVE SPECTRUM SENSING AND TRANSMISSION IN COGNITIVE NETWORKS Hasan Kartlak Electric Program, Akseki Vocational School Akdeniz University Antalya, Turkey hasank@akdeniz.edu.tr
More informationSPECTRUM SENSING METHODS IN COGNITIVE RADIO A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
SPECTRUM SENSING METHODS IN COGNITIVE RADIO A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor of Technology In Electronics and Communication Engineering Under the
More informationPerformance Analysis of Cooperative Spectrum Sensing in CR under Rayleigh and Rician Fading Channel
Performance Analysis of Cooperative Spectrum Sensing in CR under Rayleigh and Rician Fading Channel Yamini Verma, Yashwant Dhiwar 2 and Sandeep Mishra 3 Assistant Professor, (ETC Department), PCEM, Bhilai-3,
More informationEffects of Malicious Users on the Energy Efficiency of Cognitive Radio Networks
Effects of Malicious Users on the Energy Efficiency of Cognitive Radio Networks Efe F. Orumwense 1, Thomas J. Afullo 2, Viranjay M. Srivastava 3 School of Electrical, Electronic and Computer Engineering,
More informationAttack-Proof Collaborative Spectrum Sensing in Cognitive Radio Networks
Attack-Proof Collaborative Spectrum Sensing in Cognitive Radio Networks Wenkai Wang, Husheng Li, Yan (Lindsay) Sun, and Zhu Han Department of Electrical, Computer and Biomedical Engineering University
More informationIMAGE TYPE WATER METER CHARACTER RECOGNITION BASED ON EMBEDDED DSP
IMAGE TYPE WATER METER CHARACTER RECOGNITION BASED ON EMBEDDED DSP LIU Ying 1,HAN Yan-bin 2 and ZHANG Yu-lin 3 1 School of Information Science and Engineering, University of Jinan, Jinan 250022, PR China
More informationFrequency-Domain Equalization for SC-FDE in HF Channel
Frequency-Domain Equalization for SC-FDE in HF Channel Xu He, Qingyun Zhu, and Shaoqian Li Abstract HF channel is a common multipath propagation resulting in frequency selective fading, SC-FDE can better
More informationHybrid Simulation of ±500 kv HVDC Power Transmission Project Based on Advanced Digital Power System Simulator
66 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 11, NO. 1, MARCH 213 Hybrid Simulation of ±5 kv HVDC Power Transmission Project Based on Advanced Digital Power System Simulator Lei Chen, Kan-Jun
More informationCooperative Orthogonal Space-Time-Frequency Block Codes over a MIMO-OFDM Frequency Selective Channel
Cooperative Orthogonal Space-Time-Frequency Block Codes over a MIMO-OFDM Frequency Selective Channel M. Rezaei* and A. Falahati* (C.A.) Abstract: In this paper, a cooperative algorithm to improve the orthogonal
More informationAn Optimized Energy Detection Scheme For Spectrum Sensing In Cognitive Radio
International Journal of Engineering Research and Development e-issn: 78-067X, p-issn: 78-800X, www.ijerd.com Volume 11, Issue 04 (April 015), PP.66-71 An Optimized Energy Detection Scheme For Spectrum
More informationResponsive Communication Jamming Detector with Noise Power Fluctuation using Cognitive Radio
Responsive Communication Jamming Detector with Noise Power Fluctuation using Cognitive Radio Mohsen M. Tanatwy Associate Professor, Dept. of Network., National Telecommunication Institute, Cairo, Egypt
More informationSPECTRUM SHARING IN CRN USING ARP PROTOCOL- ANALYSIS OF HIGH DATA RATE
Int. J. Chem. Sci.: 14(S3), 2016, 794-800 ISSN 0972-768X www.sadgurupublications.com SPECTRUM SHARING IN CRN USING ARP PROTOCOL- ANALYSIS OF HIGH DATA RATE ADITYA SAI *, ARSHEYA AFRAN and PRIYANKA Information
More informationSequential Multi-Channel Access Game in Distributed Cognitive Radio Networks
Sequential Multi-Channel Access Game in Distributed Cognitive Radio Networks Chunxiao Jiang, Yan Chen, and K. J. Ray Liu Department of Electrical and Computer Engineering, University of Maryland, College
More informationRecent Advances in Cognitive Radios
Page 1 of 8 Recent Advances in Cognitive Radios Harit Mehta, harit.mehta@go.wustl.edu (A paper written under the guidance of Prof. Raj Jain) Download Abstract Recent advances in the field of wireless have
More informationPerformance of OFDM-Based Cognitive Radio
International Journal of Engineering Science Invention ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 Volume 2 Issue 4 ǁ April. 2013 ǁ PP.51-57 Performance of OFDM-Based Cognitive Radio Geethu.T.George
More informationOn Optimum Sensing Time over Fading Channels of Cognitive Radio System
AALTO UNIVERSITY SCHOOL OF SCIENCE AND TECHNOLOGY Faculty of Electronics, Communications and Automation On Optimum Sensing Time over Fading Channels of Cognitive Radio System Eunah Cho Master s thesis
More informationNoise Removal of Spaceborne SAR Image Based on the FIR Digital Filter
Noise Removal of Spaceborne SAR Image Based on the FIR Digital Filter Wei Zhang & Jinzhong Yang China Aero Geophysical Survey & Remote Sensing Center for Land and Resources, Beijing 100083, China Tel:
More informationSpectrum Sensing and Data Transmission Tradeoff in Cognitive Radio Networks
Spectrum Sensing Data Transmission Tradeoff in Cognitive Radio Networks Yulong Zou Yu-Dong Yao Electrical Computer Engineering Department Stevens Institute of Technology, Hoboken 73, USA Email: Yulong.Zou,
More informationEvaluation of BER and PAPR by using Different Modulation Schemes in OFDM System
International Journal of Computer Networks and Communications Security VOL. 3, NO. 7, JULY 2015, 277 282 Available online at: www.ijcncs.org E-ISSN 2308-9830 (Online) / ISSN 2410-0595 (Print) Evaluation
More informationStudy on the UWB Rader Synchronization Technology
Study on the UWB Rader Synchronization Technology Guilin Lu Guangxi University of Technology, Liuzhou 545006, China E-mail: lifishspirit@126.com Shaohong Wan Ari Force No.95275, Liuzhou 545005, China E-mail:
More informationMulti-Band Spectrum Allocation Algorithm Based on First-Price Sealed Auction
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 17, No 1 Sofia 2017 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.1515/cait-2017-0008 Multi-Band Spectrum Allocation
More informationDifferent Spectrum Sensing Techniques For IEEE (WRAN)
IJSRD National Conference on Technological Advancement and Automatization in Engineering January 2016 ISSN:2321-0613 Different Spectrum Sensing Techniques For IEEE 802.22(WRAN) Niyati Sohni 1 Akansha Bhargava
More informationSpectrum Management and Cognitive Radio
Spectrum Management and Cognitive Radio Alessandro Guidotti Tutor: Prof. Giovanni Emanuele Corazza, University of Bologna, DEIS Co-Tutor: Ing. Guido Riva, Fondazione Ugo Bordoni The spectrum scarcity problem
More informationEasyChair Preprint. A User-Centric Cluster Resource Allocation Scheme for Ultra-Dense Network
EasyChair Preprint 78 A User-Centric Cluster Resource Allocation Scheme for Ultra-Dense Network Yuzhou Liu and Wuwen Lai EasyChair preprints are intended for rapid dissemination of research results and
More informationAnalysis on detection probability of satellite-based AIS affected by parameter estimation
2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) Analysis on detection probability of satellite-based AIS affected by parameter estimation Xiaofeng
More informationDECEPTION JAMMING SUPPRESSION FOR RADAR
DECEPTION JAMMING SUPPRESSION FOR RADAR Dr. Ayesha Naaz 1, Tahura Iffath 2 1 Associate Professor, 2 M.E. Student, ECED, Muffakham Jah college of Engineering and Technology, Hyderabad, (India) ABSTRACT
More informationENERGY DETECTION BASED SPECTRUM SENSING FOR COGNITIVE RADIO
ENERGY DETECTION BASED SPECTRUM SENSING FOR COGNITIVE RADIO M.Lakshmi #1, R.Saravanan *2, R.Muthaiah #3 School of Computing, SASTRA University, Thanjavur-613402, India #1 mlakshmi.s15@gmail.com *2 saravanan_r@ict.sastra.edu
More informationCooperative anti-collision algorithm based on relay sensor in RFID system Xinxian Li, Xiaoling Sun2, b, Weiqin Li2, c, Daisong Shi2, d
rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 0) Cooperative anti-collision algorithm based on relay sensor in RFID system, a Xinxian Li, Xiaoling
More informationDynamic Spectrum Access in Cognitive Radio Networks. Xiaoying Gan 09/17/2009
Dynamic Spectrum Access in Cognitive Radio Networks Xiaoying Gan xgan@ucsd.edu 09/17/2009 Outline Introduction Cognitive Radio Framework MAC sensing Spectrum Occupancy Model Sensing policy Access policy
More informationA Certain Open Pit Slope Blasting Vibration Law Research
2017 2 nd International Conference on Architectural Engineering and New Materials (ICAENM 2017) ISBN: 978-1-60595-436-3 A Certain Open Pit Slope Blasting Vibration Law Research Lihua He ABSTRACT In order
More informationBioTechnology. An Indian Journal FULL PAPER. Trade Science Inc. Research on the monitoring method of fiber bragg grating seismic waves ABSTRACT
[Type text] [Type text] [Type text] ISSN : 0974-7435 Volume 10 Issue 19 BioTechnology 2014 An Indian Journal FULL PAPER BTAIJ, 10(19), 2014 [11549-11555] Research on the monitoring method of fiber bragg
More informationBER ANALYSIS OF WiMAX IN MULTIPATH FADING CHANNELS
BER ANALYSIS OF WiMAX IN MULTIPATH FADING CHANNELS Navgeet Singh 1, Amita Soni 2 1 P.G. Scholar, Department of Electronics and Electrical Engineering, PEC University of Technology, Chandigarh, India 2
More informationFuzzy Logic Based Smart User Selection for Spectrum Sensing under Spatially Correlated Shadowing
Open Access Journal Journal of Sustainable Research in Engineering Vol. 3 (2) 2016, 47-52 Journal homepage: http://sri.jkuat.ac.ke/ojs/index.php/sri Fuzzy Logic Based Smart User Selection for Spectrum
More informationAnalysis of Interference in Cognitive Radio Networks with Unknown Primary Behavior
EEE CC 22 - Cognitive Radio and Networks Symposium Analysis of nterference in Cognitive Radio Networks with Unknown Primary Behavior Chunxiao Jiang, Yan Chen,K.J.RayLiu and Yong Ren Department of Electrical
More informationPerformance Evaluation of different α value for OFDM System
Performance Evaluation of different α value for OFDM System Dr. K.Elangovan Dept. of Computer Science & Engineering Bharathidasan University richirappalli Abstract: Orthogonal Frequency Division Multiplexing
More information[Kavyalakshmi*, 4.(12): December, 2015] ISSN: (I2OR), Publication Impact Factor: 3.785
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY A CROSSLAYER PROTOCOL DESIGN APPROACH BETWEEN PHY AND MAC LAYER FOR COGNITIVE RADIO NETWORKS Kavyalakshmi.K*, Mrs.Padmavathi.G
More informationTRANSMITING JPEG IMAGE OVER USING UPA AND CHOTIC COMMUNICATION
TRANSMITING JPEG IMAGE OVER MIMO USING UPA AND CHOTIC COMMUNICATION Pravin B. Mali 1, Neetesh Gupta 2,Amit Sinhal 3 1 2 3 Information Technology 1 TIT, Bhopal 2 TIT, Bhopal 3 TIT, Bhopal 1 pravinmali598@gmail.com
More informationEffect of light intensity on Epinephelus malabaricus s image processing Su Xu 1,a, Kezhi Xing 1,2,*, Yunchen Tian 3,* and Guoqiang Ma 3
2nd International Conference on Electrical, Computer Engineering and Electronics (ICECEE 2015) Effect of light intensity on Epinephelus malabaricus s image processing Su Xu 1,a, Kezhi Xing 1,2,*, Yunchen
More informationCatchIt: Detect Malicious Nodes in Collaborative Spectrum Sensing
CatchIt: Detect Malicious Nodes in Collaborative Spectrum Sensing Wenkai Wang, Husheng Li, Yan (Lindsay) Sun, and Zhu Han Department of Electrical, Computer and Biomedical Engineering University of Rhode
More informationSpectrum Sensing for Wireless Communication Networks
Spectrum Sensing for Wireless Communication Networks Inderdeep Kaur Aulakh, UIET, PU, Chandigarh ikaulakh@yahoo.com Abstract: Spectrum sensing techniques are envisaged to solve the problems in wireless
More informationMulti-Radio Channel Detecting Jamming Attack Against Enhanced Jump-Stay Based Rendezvous in Cognitive Radio Networks
Multi-Radio Channel Detecting Jamming Attack Against Enhanced Jump-Stay Based Rendezvous in Cognitive Radio Networks Yang Gao 1, Zhaoquan Gu 1, Qiang-Sheng Hua 2, Hai Jin 2 1 Institute for Interdisciplinary
More informationPerformance Comparison of the Standard Transmitter Energy Detector and an Enhanced Energy Detector Techniques
International Journal of Networks and Communications 2016, 6(3): 39-48 DOI: 10.5923/j.ijnc.20160603.01 Performance Comparison of the Standard Transmitter Energy Detector and an Enhanced Energy Detector
More informationSense in Order: Channel Selection for Sensing in Cognitive Radio Networks
Sense in Order: Channel Selection for Sensing in Cognitive Radio Networks Ying Dai and Jie Wu Department of Computer and Information Sciences Temple University, Philadelphia, PA 19122 Email: {ying.dai,
More informationModeling Study on Dynamic Spectrum Sharing System Under Interference Temperature Constraints in Underground Coal Mines
Send Orders for Reprints to reprints@benthamscienceae 140 The Open Fuels & Energy Science Journal, 2015, 8, 140-148 Open Access Modeling Study on Dynamic Spectrum Sharing System Under Interference Temperature
More informationMITIGATING INTERFERENCE TO GPS OPERATION USING VARIABLE FORGETTING FACTOR BASED RECURSIVE LEAST SQUARES ESTIMATION
MITIGATING INTERFERENCE TO GPS OPERATION USING VARIABLE FORGETTING FACTOR BASED RECURSIVE LEAST SQUARES ESTIMATION Aseel AlRikabi and Taher AlSharabati Al-Ahliyya Amman University/Electronics and Communications
More informationUsing the Time Dimension to Sense Signals with Partial Spectral Overlap. Mihir Laghate and Danijela Cabric 5 th December 2016
Using the Time Dimension to Sense Signals with Partial Spectral Overlap Mihir Laghate and Danijela Cabric 5 th December 2016 Outline Goal, Motivation, and Existing Work System Model Assumptions Time-Frequency
More informationNumerical Simulation of Chaotic Laser Secure Communication. Qiang Ke
Advanced Materials Research Online: 013-09-10 ISSN: 166-8985, Vols. 798-799, pp 570-573 doi:10.408/www.scientific.net/amr.798-799.570 013 Trans Tech Publications, Switzerland Numerical Simulation of Chaotic
More informationA variable step-size LMS adaptive filtering algorithm for speech denoising in VoIP
7 3rd International Conference on Computational Systems and Communications (ICCSC 7) A variable step-size LMS adaptive filtering algorithm for speech denoising in VoIP Hongyu Chen College of Information
More informationA NOVEL FREQUENCY-MODULATED DIFFERENTIAL CHAOS SHIFT KEYING MODULATION SCHEME BASED ON PHASE SEPARATION
Journal of Applied Analysis and Computation Volume 5, Number 2, May 2015, 189 196 Website:http://jaac-online.com/ doi:10.11948/2015017 A NOVEL FREQUENCY-MODULATED DIFFERENTIAL CHAOS SHIFT KEYING MODULATION
More informationBEING wideband, chaotic signals are well suited for
680 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 51, NO. 12, DECEMBER 2004 Performance of Differential Chaos-Shift-Keying Digital Communication Systems Over a Multipath Fading Channel
More informationSpectrum Sensing by Scattering Operators in Cognitive Radio
45, Issue 1 (2018) 13-19 Journal of Advanced Research in Applied Mechanics Journal homepage: www.akademiabaru.com/aram.html ISSN: 2289-7895 Spectrum Sensing by Scattering Operators in Cognitive Radio Open
More informationA Brief Review of Cognitive Radio and SEAMCAT Software Tool
163 A Brief Review of Cognitive Radio and SEAMCAT Software Tool Amandeep Singh Bhandari 1, Mandeep Singh 2, Sandeep Kaur 3 1 Department of Electronics and Communication, Punjabi university Patiala, India
More informationPerformance Improvement of OFDM System using Raised Cosine Windowing with Variable FFT Sizes
International Journal of Research (IJR) Vol-1, Issue-6, July 14 ISSN 2348-6848 Performance Improvement of OFDM System using Raised Cosine Windowing with Variable FFT Sizes Prateek Nigam 1, Monika Sahu
More informationDesign of Frequency Characteristic Test Instrument Based on USB
Design of Frequency Characteristic Test Instrument Based on USB Zhengling Wu, Nannan Zhang College of information and control engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, P.R. China.
More informationStability Analysis for Network Coded Multicast Cell with Opportunistic Relay
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 00 proceedings Stability Analysis for Network Coded Multicast
More informationAchievable Transmission Capacity of Cognitive Radio Networks with Cooperative Relaying
Achievable Transmission Capacity of Cognitive Radio Networks with Cooperative Relaying Xiuying Chen, Tao Jing, Yan Huo, Wei Li 2, Xiuzhen Cheng 2, Tao Chen 3 School of Electronics and Information Engineering,
More informationConsensus Algorithms for Distributed Spectrum Sensing Based on Goodness of Fit Test in Cognitive Radio Networks
Consensus Algorithms for Distributed Spectrum Sensing Based on Goodness of Fit Test in Cognitive Radio Networks Djamel TEGUIG, Bart SCHEERS, Vincent LE NIR Department CISS Royal Military Academy Brussels,
More informationAbstract. Keywords - Cognitive Radio, Bit Error Rate, Rician Fading, Reed Solomon encoding, Convolution encoding.
Analysing Cognitive Radio Physical Layer on BER Performance over Rician Fading Amandeep Kaur Virk, Ajay K Sharma Computer Science and Engineering Department, Dr. B.R Ambedkar National Institute of Technology,
More informationSPLIT MLSE ADAPTIVE EQUALIZATION IN SEVERELY FADED RAYLEIGH MIMO CHANNELS
SPLIT MLSE ADAPTIVE EQUALIZATION IN SEVERELY FADED RAYLEIGH MIMO CHANNELS RASHMI SABNUAM GUPTA 1 & KANDARPA KUMAR SARMA 2 1 Department of Electronics and Communication Engineering, Tezpur University-784028,
More informationEvaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel
ISSN (Online): 2409-4285 www.ijcsse.org Page: 1-7 Evaluation of channel estimation combined with ICI self-cancellation scheme in doubly selective fading channel Lien Pham Hong 1, Quang Nguyen Duc 2, Dung
More information