Receiver architecture for cognitive positioning with CDMA and OFDM signals

This paper proposes a cognitive positioning architecture. This architecture uses cyclostationary analysis to understand the contents of the spectrum surrounding the receiver and exploit this information for activating the necessary tracking and demodulation loops. This paper assumes the presence of CDMA and OFDM signals. The results show the performance of the spectrum sensing technique used along with the expected positioning accuracy.

[1]  Mario Tanda,et al.  On the second-order cyclostationarity properties of long-code DS-SS signals , 2006, IEEE Transactions on Communications.

[2]  Mahsa Shafiee,et al.  WiFi-based Fine Timing Assistance for GPS Acquisition , 2013 .

[3]  R. Jafari,et al.  A Segmentation Technique Based on Standard Deviation in Body Sensor Networks , 2007, 2007 IEEE Dallas Engineering in Medicine and Biology Workshop.

[4]  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).

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

[6]  Peter Williams.  Evaluating the State Probabilities of M out of N Sliding Window Detectors , 1998 .

[7]  A.H. Sayed,et al.  Network-based wireless location: challenges faced in developing techniques for accurate wireless location information , 2005, IEEE Signal Processing Magazine.

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

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

[10]  Linda Doyle,et al.  Cyclostationary Signatures in Practical Cognitive Radio Applications , 2008, IEEE Journal on Selected Areas in Communications.

[11]  Reinhard Exel,et al.  Carrier-based ranging in IEEE 802.11 wireless local area networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

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

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

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

[15]  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.

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

[17]  Feng Xia,et al.  Localization Technologies for Indoor Human Tracking , 2010, 2010 5th International Conference on Future Information Technology.

[18]  Joseph A. Paradiso,et al.  An Inertial Measurement Framework for Gesture Recognition and Applications , 2001, Gesture Workshop.

[19]  Ramjee Prasad,et al.  5G Based on Cognitive Radio , 2011, Wirel. Pers. Commun..

[20]  H Arslan,et al.  Cognitive-Radio Systems for Spectrum, Location, and Environmental Awareness , 2010, IEEE Antennas and Propagation Magazine.

[21]  D. Ucci,et al.  Spectral signatures and interference of 802.11 Wi-Fi signals with Barker code spreading , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[22]  Yiu-Tong Chan,et al.  Exact and approximate maximum likelihood localization algorithms , 2006, IEEE Trans. Veh. Technol..