Cyclostationary feature analysis of CEN-DSRC for cognitive vehicular networks

Cognitive vehicular networks provide the necessary intelligence for vehicular communication networks in order to optimally utilize the limited resources and maximize the performance. One of the important functions of cognitive networks is to learn the radio environment by means of detecting and identifying existing radios. In this context we use the cyclostationarity features of dedicated short range communication (DSRC) signals to blindly detect them in the environment. We present experimental results on the cyclostationarity properties of DSRC wireless transmissions considering the CEN (European) standards for both uplink and downlink signals. By performing cyclostationarity analysis we compute the cyclic power spectrum (CPS) of the CEN DSRC signals which is then used for detecting the presence of the CEN DSRC radios. We obtain CEN DSRC signals from experiments and use the recorded data to perform post-signal analysis to determine the detection performance. The probability of false alarm and the probability of missed detection are computed and the results are presented for different detection strategies. Results show that the cyclostationarity feature based detection can be robust compared to the well known energy based technique for low signal to noise ratio levels.

[1]  Gianmarco Baldini,et al.  Preliminary experimental results on the spectrum sensing performances for UWB-Cognitive Radios for detecting IEEE 802.11n systems , 2009, 2009 6th International Symposium on Wireless Communication Systems.

[2]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[3]  Andrea Giorgetti,et al.  Distributed 'Ring-Around' Sequential Spectrum Sensing for Cognitive Radio Networks , 2011, 2011 IEEE International Conference on Communications (ICC).

[4]  Dusit Niyato,et al.  Optimal Channel Access Management with QoS Support for Cognitive Vehicular Networks , 2011, IEEE Transactions on Mobile Computing.

[5]  Andrea Giorgetti,et al.  Periodic Spectrum Sensing Performance and Requirements for Detecting Legacy Users with Temporal and Noise Statistics in Cognitive Radios , 2009, 2009 IEEE Globecom Workshops.

[6]  F. Caneschi.  CEN Standardization of the Electronic Toll Systems Architecture , 2007, 2007 7th International Conference on ITS Telecommunications.

[7]  Andrea Giorgetti,et al.  Time-Divisional Cooperative Periodic Spectrum Sensing for Cognitive Radio Networks , 2010, 2010 IEEE International Conference on Communications.

[8]  T. C. Aysal,et al.  Bayesian Tracking in Cooperative Localization for Cognitive Radio Networks , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[9]  Alessandro Guidotti,et al.  Cognitive Satellite Terrestrial Radios , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[10]  Andrea Giorgetti,et al.  Cooperative weighted centroid localization for cognitive radio networks , 2012, 2012 International Symposium on Communications and Information Technologies (ISCIT).

[11]  R.W. Brodersen,et al.  Cyclostationary Feature Detector Experiments Using Reconfigurable BEE2 , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[12]  Gianmarco Baldini,et al.  Experimentally detecting IEEE 802.11n Wi-Fi based on cyclostationarity features for ultra-wide band cognitive radios , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[13]  M. Oner,et al.  Cyclostationarity based air interface recognition for software radio systems , 2004, Proceedings. 2004 IEEE Radio and Wireless Conference (IEEE Cat. No.04TH8746).

[14]  S. Enserink,et al.  A cyclostationary feature detector , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[15]  Imrich Chlamtac,et al.  Periodic Sensing in Cognitive Radios for Detecting UMTS/HSDPA Based on Experimental Spectral Occupancy Statistics , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[16]  W. Gardner.  Exploitation of spectral redundancy in cyclostationary signals , 1991, IEEE Signal Processing Magazine.

[17]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[18]  K. R. Chowdhury,et al.  Smart Radios for Smart Vehicles: Cognitive Vehicular Networks , 2012, IEEE Vehicular Technology Magazine.

[19]  T. C. Aysal,et al.  Cooperative Spectrum Sensing over Imperfect Channels , 2008, 2008 IEEE Globecom Workshops.

[20]  T. C. Aysal,et al.  Time divisional and time-frequency divisional cooperative spectrum sensing , 2009, 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[21]  W. A. Brown,et al.  Computationally efficient algorithms for cyclic spectral analysis , 1991, IEEE Signal Processing Magazine.

[22]  Imrich Chlamtac,et al.  Spectrum Sensing for Cognitive Radios with Transmission Statistics: Considering Linear Frequency Sweeping , 2010, EURASIP J. Wirel. Commun. Netw..

[23]  Andrea Giorgetti,et al.  Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation, and Localization , 2012 .

[24]  Vijay K. Bhargava,et al.  Cognitive Wireless Communication Networks , 2007 .

[25]  T. C. Aysal,et al.  Cooperative Spectrum Sensing with Noisy Hard Decision Transmissions , 2009, 2009 IEEE International Conference on Communications.

[26]  Haroon Rasheed,et al.  Spectrum Sensing for Cognitive Vehicular Networks over Composite Fading , 2011 .

[27]  Jeffrey H. Reed,et al.  Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[28]  Oriol Sallent,et al.  ETSI reconfigurable radio systems: status and future directions on software defined radio and cognitive radio standards , 2010, IEEE Communications Magazine.

[29]  Miao Pan,et al.  Cooperative Communication Aware Link Scheduling for Cognitive Vehicular Networks , 2012, IEEE Journal on Selected Areas in Communications.