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A study analysis of Cooperative spectrum sensing in Cognitive Radio Networks Outline of Presentation Motivation Objectives Introduction Functions Spectrum Sensing Techniques Cooperative Spectrum Sensing Cluster Based Simulation Results Conclusion Future Work References Motivation Recent measurement by the FCC in the US show 70% of the allocated spectrum is not utilized Objectives To Maximize Probability of Detection To Minimize the probability of False alarm To Minimize sensing time To Maximize throughput Spectrum Holes Licensed User Primary User Unlicensed User Secondary User Cognitive Radio “A cognitive radio (CR) is a radio that can change its operating parameters dynamically based on interaction with the environment in which it operates”. They are designed to provide highly reliable communication for all the users of the network, wherever and whenever needed. Functions of Cognitive Radio Maximizes throughput Mitigates interference Facilitates interoperability Access secondary markets Cognitive radio cycle Cognitive Cycle Spectrum sensing Spectrum analysis Spectrum decision Design Issues • Spectrum sensing Spectrum management Spectrum sharing Spectrum mobility Spectrum Sensing Techniques The most efficient way of spectrum sensing techniques are divided into : Local Spectrum Sensing Energy detection Matched Filter detection Cyclostationary detection Cooperative Spectrum Sensing Distributive Cooperative detection Centralized Cooperative detection External Cooperative detection Spectrum Sensing using Energy Detection method Block Diagram for Energy Detection Method Energy detection • • The decision on the occupancy of a band can be obtained by comparing the decision metric against a fixed threshold. Decision metric is given as :- where Y[n] – Received signal N – no of Samples Binary Hypothesis Testing H0: x (t) = n(t), H1: x(t) = s (t) + w (t) where H0 and H1 are the sensed states for absence and presence of primary user respectively The four possible cases for detected signal are : Declaring H0 when H0 is true (H0|H0); Declaring H1 when H1 is true (H1|H1); Declaring H0 when H1 is true (H0|H1); Declaring H1 when H0 is true (H1|H0). Challenges with Energy detection method Selection of threshold level for detecting primary user. Inability to differentiate from primary user and noise. Poor performance under low SNR. Cooperative Sensing In order to reduce communication overhead, the users only share the final 1-bit decisions rearding H0 and H1 rather than the entire decision statistics. Decreases the probabilities of miss-detection and false alarm considerably. Solves hidden primary user problem and it can decrease sensing time. Cooperative Sensing • Uses control channel to share spectrum sensing result. • Co-operative sensing is usually performed in two successive stages: sensing and reporting • In order to reduce the reporting error, the cluster based architecture to be used. Cluster Architecture ON ON ON ON Cluster head Cluster head ON ON Cognitive base station ON – Ordinary Node Cluster Based Cooperative sensing Individual decision will be made by each user and is send to their cluster head. • Cluster head makes the local decision and send to the cognitive Base station(BS) • Cognitive BS decides the presence or absence of primary user and broadcasts to the cluster-heads. • Cluster based cooperative sensing The probabilities of false-alarm and detection for conventional cooperative scheme are Qd = 1- (1 - Pd)N Qf = 1- (1 - Pf)N • The probability of detection for cluster based cooperative scheme is • where N= Number of Cooperative users K= Number of cluster Pd_i = Probability of detection for ith cluster Simulation Parameters No of Users: 100 No of clusters: 10 SNR 2 dB Pf 0.01 Probability of Detection Inference: Probability of detection for proposed method is improved compared to existing method. Probability of false alarm 0 Probability of false alarm 10 Cluster Method Conventional Method -1 10 -2 10 1 2 3 4 5 6 Number of clusters 7 8 9 10 Inference: Probability of false alarm for proposed method is minimum compared to existing method. Performance analysis for Rayleigh Channel f ( ) exp 1 , 0 Pd _ R Pd ( ) f ( )d 0 Pd _ R e m 1 V VT m 2 1 VT 1 2(1T ) 1 VT 2 e e n ! 2 n ! 2 ( 1 ) n 0 n 0 VT m 2 2 n Pm _ R 1 Pd _ r Pm _ R 1 e VT m 2 2 1 VT 1 n 0 n! 2 n m 1 2(1VT ) VT m2 1 VT e 2 e n 0 n! 2(1 ) Comparison of performance of energy detection in AWGN channel and Rayleigh fading channel • • We observe that the performance of energy detection has degraded when fading channel is considered. At Pf=0.1 , the Pm for Rayleigh fading is high as compared to that of AWGN channel. Comparison of performance of energy detection in AWGN channel for different values of SNR We observe that , the performance of energy detection in AWGN channel increases as SNR increases. This indicates that the efficiency of spectrum sensing can be increased for energy detection method by increasing the value of SNR Cooperative spectrum sensing with different number of users We observe that, as no. of collaborations increases ,the performance of energy detection also increases. When N=5, the performance in fading channel is much better than AWGN channel. Conclusion Emerging cognitive radio technology has been identified as a high impact disruptive technology innovation, that could provide solutions to the “radio traffic jam” problem and provide a path to scaling wireless systems for the next 25 years. Efficient spectrum sensing can be achieved by maximizing the probability of detection. Future Work Significant new research is required to address the technical challenges of cognitive radio networking like dynamic spectrum allocation methods, spectrum sensing, cooperative communications, cognitive network security, cognitive system adaptation algorithms and emergent system behavior. A set of cognitive networking testbeds can be developed that can be used to evaluate cognitive networks at various stages of their development . References • Mitola , J., and Maguire, G. Q., “Cognitive Radio: Making Software Radios More Personal”, IEEE Pcrsonal Communications, vol. 6, no. 4, pp. 13-18, August 1999. • S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Select. Area Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005. • C. Sun, W. Zhang and K.B. Letaief, ”Cluster-based cooperative spectrum sensing in cognitive radio systems”. in Proc. IEEE ICC2007, pp. 2511- 2515, June, 2007 • Zhai Xuping and Pan Jianguo, “Energy-Detection Based Spectrum Sensing for Cognitive Radio”, IET Conference on Wireless, Mobile and Sensor Networks, 2007. (CCWMSN07), pp:944 – 947, 2007. References(contd.) • Unnikrishnan, J. , Veeravalli,V.V. , “Cooperative Sensing for Primary Detection in Cognitive Radio”, IEEE Journal, pp. 18‐27, Feb 2008. • Junyang Shen, Tao Jiang, Siyang Liu and Zhongshan Zhang, “Maximum Channel Throughput via Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE transactions on wireless communications, vol. 8, no. 10, October 2009. • Guo, Peng, Shaovi. Haiming and Wenbo “ Cooperative Spectrum Sensing with Cluster-based Architecture in cognitive Radio Networks, “ Wireless Signal Processing and Network Lab, University of Posts and Telecommunications, Beijing, China, 2009. Thank you !