UWB Signal Detection by Cyclic Features

Ultra-wideband (UWB) impulse radio (IR) systems are well known for low transmission power, low probability of detection, and overlaying with narrowband (NB) systems. These merits in fact make UWB signal detection challenging, since several high-power wireless communication systems coexist with UWB signals. In the literature, cyclic features are exploited for signal detection. However, the high computational complexity of conventional cyclic feature based detectors burdens the receivers. In this paper, we propose computationally efficient detectors using the specific cyclic features of UWB signals. The closed-form relationships between the cyclic features and the system parameters are revealed. Then, some constant false alarm rate detectors are proposed based on the estimated cyclic autocorrelation functions (CAFs). The proposed detectors have low complexities compared to the existing ones. Extensive simulation results indicate that the proposed detectors achieve a good balance between the detection performance and the computational complexity in various scenarios, such as multipath environments, colored noise, and NB interferences.

[1]  Robert W. Brodersen,et al.  Detect and avoid: an ultra-wideband/WiMAX coexistence mechanism [Topics in Radio Communications] , 2007, IEEE Communications Magazine.

[2]  Moe Z. Win,et al.  A stochastic geometry approach to coexistence in heterogeneous wireless networks , 2009, IEEE Journal on Selected Areas in Communications.

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

[4]  Romeo Giuliano,et al.  On the coexistence of power-controlled ultrawide-band systems with UMTS, GPS, DCS1800, and fixed wireless systems , 2005, IEEE Transactions on Vehicular Technology.

[5]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[6]  Biing-Hwang Juang,et al.  Signal Processing in Cognitive Radio , 2009, Proceedings of the IEEE.

[7]  A. Molisch,et al.  IEEE 802.15.4a channel model-final report , 2004 .

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

[9]  Xiaoli Ma,et al.  Detecting UWB signals using cyclic features , 2013, 2013 IEEE International Conference on Ultra-Wideband (ICUWB).

[10]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..

[11]  Matti Latva-aho,et al.  On the UWB system coexistence with GSM900, UMTS/WCDMA, and GPS , 2002, IEEE J. Sel. Areas Commun..

[12]  Friedrich Jondral,et al.  On the extraction of the channel allocation information in spectrum pooling systems , 2007, IEEE Journal on Selected Areas in Communications.

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

[14]  Antonio Napolitano,et al.  Generalizations of Cyclostationarity: A New Paradigm for Signal Processing for Mobile Communications, Radar, and Sonar , 2013, IEEE Signal Processing Magazine.

[15]  Yonghong Zeng,et al.  A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions , 2010, EURASIP J. Adv. Signal Process..

[16]  Octavia A. Dobre,et al.  On the Cyclostationarity of OFDM and Single Carrier Linearly Digitally Modulated Signals in Time Dispersive Channels: Theoretical Developments and Application , 2010, IEEE Transactions on Wireless Communications.

[17]  Brian M. Sadler,et al.  Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing , 2012, IEEE Journal of Selected Topics in Signal Processing.

[18]  J. Antoni.  Cyclic spectral analysis in practice , 2007 .

[19]  Jitendra K. Tugnait,et al.  On Cyclostationarity Based Spectrum Sensing Under Uncertain Gaussian Noise , 2013, IEEE Transactions on Signal Processing.

[20]  Luc Vandendorpe,et al.  Multi-cycle cyclostationary based spectrum sensing algorithm for OFDM signals with noise uncertainty in cognitive radio networks , 2012, MILCOM 2012 - 2012 IEEE Military Communications Conference.

[21]  Georgios B. Giannakis,et al.  Statistical tests for presence of cyclostationarity , 1994, IEEE Trans. Signal Process..

[22]  Menguc Oner.  On the Spectral Correlation of UWB Impulse Radio Signals , 2008 .

[23]  Desimir Vucic,et al.  Cyclic Spectral Analysis of UWB-IR Signals , 2009, IEEE Signal Processing Letters.

[24]  Romano Fantacci,et al.  A Low-Complexity Cyclostationary-Based Spectrum Sensing for UWB and WiMAX Coexistence With Noise Uncertainty , 2010, IEEE Transactions on Vehicular Technology.

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

[26]  H. Vincent Poor,et al.  Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems , 2009, IEEE Transactions on Signal Processing.

[27]  Jiaming Guo,et al.  Cyclostationarity Based Multi-Antenna Spectrum Sensing in Cognitive Radio Networks , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[28]  Andrea Giorgetti,et al.  Coexistence Between UWB and Narrow-Band Wireless Communication Systems , 2009, Proceedings of the IEEE.