Maximum-Minimum Eigen Value Based Spectrum Scanner Mohamed Hamid and Niclas Björsell Center for RF measurement Technology, University of Gävle, Sweden.

Slides:



Advertisements
Similar presentations
A Synchronization Technique to Model Output Behavior of Wide Bandwidth Signals Efrain Zenteno & Magnus Isaksson Center for RF measurement Technology, University.
Advertisements

Optimization of Radio resources Krishna Chaitanya Kokatla.
A discussion on channel sensing techniques By James Xu Supervised by Dr. Fakhrul Alam.
Multistage Spectrum Sensing for Cognitive Radios UCLA CORES.
SoNIC: Classifying Interference in Sensor Networks Frederik Hermans et al. Uppsala University, Sweden IPSN 2013 Presenter: Jeffrey.
1 Helsinki University of Technology,Communications Laboratory, Timo O. Korhonen Data Communication, Lecture6 Digital Baseband Transmission.
Doc.: IEEE /0685r0 Submission May 2011 Ron Porat, Broadcom S1G Spectrum Regulations Outside the US Date: Authors: Slide 1.
TxMiner: Identifying Transmitters in Real World Spectrum Measurements
Spectrum Awareness in Cognitive Radio Systems based on Spectrum Sensing Miguel López-Benítez Department of Electrical Engineering and Electronics University.
Adapting the Ranging Algorithm to the Positioning Technique in UWB Sensor Networks Mats Rydström, Erik G. Ström and Arne Svensson Dept. Signals and Systems.
Geolocation databases for spectrum sharing : ECC findings and studies EC DG CONNECT Workshop, 20 March 2015 Bruno Espinosa, Deputy Director, ECO.
System Design for Cognitive Radio Communications
Fundamentals of Networking Lab Seattle Spectrum Measurements MHz.
Spectrum Sensing Based on Cyclostationarity In the name of Allah Spectrum Sensing Based on Cyclostationarity Presented by: Eniseh Berenjkoub Summer 2009.
A Wireless Spectrum Analyzer in Your Pocket
Evolved Harmonic sampling: a tool to reduce the digital bandwidth requirement of RF receivers Charles Nader 1,2,3, Wendy Van Moer 3, Kurt Barbé 3, Niclas.
Requirements of wireless systems in industrial areas Requirements of wireless systems in industrial areas Javier Ferrer-Coll, Per Ängskog, José Chilo,
1 Spectrum Sensing Marjan Hadian. 2 Outline Cognitive Cycle Enrgy Detection Matched filter cyclostationary feature detector Interference Temperature Spectral.
Performance Analysis of Energy Detector in Relay Based Cognitive Radio Networks Saman Atapattu Chintha Tellambura Hai Jiang.
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha Presented by Ray Lam Oct 23, 2004.
Multiantenna-Assisted Spectrum Sensing for Cognitive Radio
COGNITIVE RADIO FOR NEXT-GENERATION WIRELESS NETWORKS: AN APPROACH TO OPPORTUNISTIC CHANNEL SELECTION IN IEEE BASED WIRELESS MESH Dusit Niyato,
College of Engineering Resource Management in Wireless Networks Anurag Arepally Major Adviser : Dr. Robert Akl Department of Computer Science and Engineering.
1 Waveform Design For Active Sensing Systems – A Computational Approach.
1 A Matched Filter for Cosmic Ray Detection from Eletromagnetic Wave Reflection Luciano Andrade Thiago Ciodaro José Seixas Federal University of Rio de.
POWER CONTROL IN COGNITIVE RADIO SYSTEMS BASED ON SPECTRUM SENSING SIDE INFORMATION Karama Hamdi, Wei Zhang, and Khaled Ben Letaief The Hong Kong University.
Signal Propagation Propagation: How the Signal are spreading from the receiver to sender. Transmitted to the Receiver in the spherical shape. sender When.
Utility Based Scheduling in Cognitive Radio Networks Term Project CmpE-300 Analysis of Algorithms Spring 2009 Computer Engineering, Boğaziçi University,
Performance of Energy Detection: A Complementary AUC Approach
EELE 5490, Fall, 2009 Wireless Communications Ali S. Afana Department of Electrical Engineering Class 5 Dec. 4 th, 2009.
Bingxuan ZHAO Wireless Communication and Satellite Communication Project II Shimamoto Laboratory, GITS Waseda University Ph.D Academy.
EXPERIMENTAL STUDY OF RADIO FREQUENCY INTERFERENCE DETECTION ALGORITHMS IN MICROWAVE RADIOMETRY José Miguel Tarongí Bauzá Giuseppe Forte Adriano Camps.
Simulation Of A Cooperative Protocol For Common Control Channel Implementation Prepared by: Aishah Thaher Shymaa Khalaf Supervisor: Dr.Ahmed Al-Masri.
Submitted By: PVS Soumya [2/4] Sai Nandini T [2/4] GNITS GNITS
Chapter 21 R(x) Algorithm a) Anomaly Detection b) Matched Filter.
A Survey of Spectrum Sensing Algorithm for Cognitive Radio Applications YaGun Wu netlab.
White Space Internet Device By: Sean Iveson William Sadler.
Copyright © 2010 National Institute of Information and Communications Technology. All Rights Reserved 1 R&D and Standardization Activities on Distributed.
Wireless Communication Technologies 1 Phase noise A practical oscillator does not produce a carrier at exactly one frequency, but rather a carrier that.
Advanced Spectrum Management in Multicell OFDMA Networks enabling Cognitive Radio Usage F. Bernardo, J. Pérez-Romero, O. Sallent, R. Agustí Radio Communications.
Analysis of Optimal and Suboptimal Discrete-Time Digital Communications Receivers Clemson University SURE Program 2005 Justin Ingersoll, Prof. Michael.
March, 2006 Doc: IEEE a Zhen, Li, and Kohno (NICT) SlideTG4a1 Project: IEEE P Working Group for Wireless Personal Area Networks.
Kin Seong Leong Auto-ID Adelaide
Zaid A. Shafeeq Mohammed N. Al-Damluji Al-Ahliyya Amman University Amman - Jordan September
Nonbinary Orthogonal Modulation in Direct- Sequence Spread Spectrum Communication Systems Michael Y. Tan Home Institution: Clemson University Advisor:
Compromising Electromagnetic Emanations of Wired and Wireless Keyboards Presented By: Justin Rilling Written By: Martin Vuagnoux and Sylvain Pasini.
Doc.: IEEE /0032r0 Submission January 2007 Slide 1 Soo-Young Chang, Huawei Technologies Interference Detection Using Preambles for Sensing IEEE.
Spectrum Sensing In Cognitive Radio Networks
Presenter: Renato Iide, Le Wang Presentation Date: 12/16/2015.
Outline Transmitters (Chapters 3 and 4, Source Coding and Modulation) (week 1 and 2) Receivers (Chapter 5) (week 3 and 4) Received Signal Synchronization.
Single Correlator Based UWB Receiver Implementation through Channel Shortening Equalizer By Syed Imtiaz Husain and Jinho Choi School of Electrical Engineering.
Chance Constrained Robust Energy Efficiency in Cognitive Radio Networks with Channel Uncertainty Yongjun Xu and Xiaohui Zhao College of Communication Engineering,
1 Architecture and Behavioral Model for Future Cognitive Heterogeneous Networks Advisor: Wei-Yeh Chen Student: Long-Chong Hung G. Chen, Y. Zhang, M. Song,
Doc.: IEEE /0034r0 Submission January 2007 Slide 1 Soo-Young Chang, Huawei Technologies Simulation Results for Spectral Correlation Sensing with.
1 Spectrum Co-existence of IEEE b and a Networks using the CSCC Etiquette Protocol Xiangpeng Jing and Dipankar Raychaudhuri, WINLAB Rutgers.
RADIO RECIEVERS.
Overcoming the Sensing-Throughput Tradeoff in Cognitive Radio Networks ICC 2010.
Ashish Rauniyar, Soo Young Shin IT Convergence Engineering
A discussion on channel sensing techniques By James Xu.
Younes Abdi, PhD Faculty of Information Technology
Phd Proposal Investigation of Primary User Emulation Attack in Cognitive Radio Networks Chao Chen Department of Electrical & Computer Engineering Stevens.
Eng.: Ahmed Abdo AbouelFadl
Cognitive Radio Based 5G Wireless Networks
PERFORMANCE ANALYSIS OF SPECTRUM SENSING USING COGNITIVE RADIO
Design Tool for Spectrum Sensing of Cognitive Radio
Phase Noise… How much is too much?
IX International Workshop ACAT
Phase Noise… How much is too much?
Phase Noise… How much is too much?
Presentation transcript:

Maximum-Minimum Eigen Value Based Spectrum Scanner Mohamed Hamid and Niclas Björsell Center for RF measurement Technology, University of Gävle, Sweden Communications Systems Lab, Royal Institute of Technology, Stockholm, 2011, Gävle, October, 2011

Outlines Introduction Spectrum Detection Techniques Maximum-Minimum Eigen Value Detection (MMEVD) Rectangular Filtering for sub-bands spectrum scanning with MMEVD Measurements Results Conclusions

Introduction Current spectrum regulation policy relies on Static spectrum access Wireless services and technologies are growing rapidly Lack of radio resources Radio Spectrum is under-utilized Dynamic Spectrum access (DSA) policy

Introduction(Cont.) What is the spectrum opportuinty?! A free of use channel (band) subject to the recieved power in a specific time at a specific location How to find a spectrum Opportuity Spectrum Sensing and/or Geolocation Databse Beacon based Spectrum Opportunities

Spectrum Detection Techniques Energy Detector Requires a prior knowledge of the system background noise Matched Filtering Auto-correlation Detection Require a prior knowledge of the primary system signal Maximum-Minimum Eigen Value Detection

Maximum-Minimum Eigen Value Detection (MMEVD) Signal frequency components α Eigen values of the auto correlation Matrix of the signal λ max λ min = σ n 2

Maximum-Minimum Eigen Value Detection (MMEVD) Recieved Signal r(t) Upon predefined threshold γ of the ratio λ max / λ min the decission is made if it is a signal or just noise MMEVD tests the extent of the flatness of the spectrum Filtering is a problimatic when scanning for sub-bands is to take place

Rectangular Filtering for sub-bands spectrum scanning with MMEVD Solution: Rectangular Filtering, i.e. talking the spectrum lines lie inside the sub-band of interest and throw away the rest Time Domain Signals Do MMEVD

Measurements Results Measured BW : 10 MHz # Sub-channels: 5 (2MHz each)

Conclusions Sub-bands spectrum scanning is feasible with rectangular filtering and MMEVD MMEVD introduces probability of false alarm much less than the one introduced by Energy Detection

THANKS FOR YOUR ATTENTION QUESTIONS AND COMMENTS ARE WELCOME