Cyclostationarity-based spectrum sensing properties for signals of opportunity

Performance enhancement in indoor positioning is one of the main concerns in recent days. Seeking such improvements, developments in navigation systems are employing Signals of Opportunity (SoO), meaning signals not originally developed for positioning purposes, such as wireless communication signals and Ultra Wideband (UWB) signals. Cyclostationary methods can provide necessary tools for signal detection for these systems. The detection part is only the first step towards cognitive positioning, and this is the part addressed in this paper. However, this work is not limited to cognitive positioning area, but it can find its usability in cognitive spectrum sensing as well. The aim of this paper is to provide a better understanding of the cyclostationary spectrum sensing properties of the most encountered modulations techniques for the SoO signals, namely CDMA, OFDM and TH-PPM UWB.

[1]  Hüseyin Arslan,et al.  Utilization of Location Information in Cognitive Wireless Networks , 2007, IEEE Wireless Communications.

[2]  A. Dempster,et al.  System-Level Considerations for Signal-of-Opportunity Positioning , 2010 .

[3]  Mengüç Öner.  On the Spectral Correlation of UWB Impulse Radio Signals , 2008, IEEE Communications Letters.

[4]  Marcos E. Castro,et al.  CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO S YSTEMS , 2011 .

[5]  Georgios B. Giannakis,et al.  Cyclostationary Signal Analysis , 2009 .

[6]  Hasari Celebi,et al.  Location awareness in cognitive radio networks , 2008 .

[7]  Gonzalo Seco-Granados,et al.  C/N_0-Based Criterion for Selecting BOC-Modulated GNSS Signals in Cognitive Positioning , 2013, IEEE Communications Letters.

[8]  Bo Hu,et al.  Accurate evaluation of multiple-access performance in TH-PPM and TH-BPSK UWB systems , 2004, IEEE Transactions on Communications.

[9]  Andrew M. Gillman.  Non Co-Operative Detection of LPI/LPD Signals via Cyclic Spectral Analysis , 2012 .

[10]  Khalid A. Qaraqe,et al.  Location-Aware Cognitive Communication Systems for Public Safety , 2010 .

[11]  Antonio Napolitano,et al.  Cyclostationarity: Half a century of research , 2006, Signal Process..

[12]  Steven R. Schnur.  Identification and classification of OFDM based signals using preamble correlation and cyclostationary feature extraction , 2009 .

[13]  Jukka Manner,et al.  Seventh International Workshop on Selected Topics in Mobile and Wireless Computing (STWiMob), Larnaca, Cyprus, 8-10 October 2014 , 2014 .

[14]  Caroline Tom.  Cyclostionary Spectral Analysis of Typical Satcom Signals using the FFT Accumulation Method , 1995 .

[15]  Brent Edward Guenther.  Multi-User Signal Classification via Cyclic Spectral Analysis , 2010 .

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

[17]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Tho Le-Ngoc,et al.  Efficient Cooperative Cyclostationary Spectrum Sensing in Cognitive Radios at Low SNR Regimes , 2011, IEEE Transactions on Wireless Communications.

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

[20]  Friedrich K. Jondral,et al.  Air interface identification for software radio systems , 2007 .

[21]  Jose A. Lopez-Salcedo,et al.  Cyclic Frequencies of BOC‐Modulated GNSS Signals and Their Potential Within a Cognitive Positioning Framework , 2014 .

[22]  H. Vincent Poor,et al.  Spectrum Sensing in Cognitive Radios Based on Multiple Cyclic Frequencies , 2007, 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications.