Many natural and manmade processes have periodic statistical characteristics. These processes are called cyclostationary and they are commonly encountered in various fields of science and technology, such as climatology, mechanics, astronomy and communications. For instance, in communications, periodicity is typically induced by modulation, sampling, and multiplexing operations.
Currently, this project deals with the detection of discrete-time (almost) cyclostationarity. A potential application of such a detector is cognitive radio, where we need to detect vacant communication channels by spectrum sensing.
In case of questions, suggestions, problems etc. please send an email.
Stefanie Horstmann: [email protected]
[1] S. Horstmann, D. Ramírez, and P. J. Schreier, "Detection of Almost-Cyclostationarity: An Approach Based on a Multiple Hypothesis Test", Proc. Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, USA, November 2017
[2] A. Pries, D. Ramírez, and P. J. Schreier, "LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure", IEEE Transactions on Wireless Communications, vol. 17, no. 9, pp. 6321-6334, Sept. 2018
[3] S. Horstmann, D. Ramirez, and P.J. Schreier, "Joint Detection of Almost-Cyclostationary Signals and Estimation of their Cycle Period", IEEE Signal Processing Letters, vol. 25, no. 11, pp. 1695-1699, 2018.
[4] S. Horstmann, D. Ramírez, and P.J. Schreier, "Two-Channel Passive Detection Exploiting Cyclostationarity", accepted for EUSIPCO 2019 (http://arxiv.org/abs/1906.06973)
[5] S. Horstmann, D. Ramírez, and P.J. Schreier, "Two-Channel Passive Detection of Cyclostationary Signals" submitted to IEEE Transaction on Signal Processing, June 2019