LMPIT-inspired tests for detecting a cyclostationary signal in noise with spatio-temporal structure (Aaron Pries, David Ramírezand Peter J. Schreier) IEEE Trans. Wirel. Commun.17 (9), pp. 6321–6334, September2018. Available at https://arxiv.org/abs/1803.08791 [BibTeX] @article{pries2018,
author = {Aaron Pries and David Ram{\'i}rez and Peter J. Schreier},
month = {September},
title = {{LMPIT}-inspired tests for detecting a cyclostationary signal in noise with spatio-temporal structure},
year = {2018},
journal = {{IEEE} {T}rans. {W}irel. {C}ommun.},
number = {9},
pages = {6321–6334},
volume = {17},
note = {Available at https://arxiv.org/abs/1803.08791},
} [Abstract]
Conference article
2
Detection of cyclostationarity in the presence of temporal or spatial structure with applications to cognitive radio (Aaron Pries, David Ramírezand Peter J. Schreier) Proc. IEEE Int. Conf. Acoustics, Speech and Signal Process., pp. 4249–4253, Shanghai, China, March2016. DOI:10.1109/ICASSP.2016.7472478. [BibTeX] @inproceedings{pries2016,
abstract = {One approach to spectrum sensing for cognitive radio is the detection of cyclostationarity. We extend an existing multi-antenna detector for cyclostationarity proposed by Ram{\'i}rez et al. [1], which makes no assumptions about the noise beyond being (temporally) wide-sense stationary. In special cases, the noise could be uncorrelated among antennas, or it could be temporally white. The performance of a general detector can be improved by making use of a priori structural information. We do not, however, require knowledge of the exact values of the temporal or spatial noise covariances. We develop an asymptotic generalized likelihood ratio test and evaluate the performance by simulations.},
address = {Shanghai, China},
author = {Aaron Pries and David Ram{\'i}rez and Peter J. Schreier},
booktitle = {{P}roc.\ {IEEE} {I}nt.\ {C}onf.\ {A}coustics, {S}peech and {S}ignal {P}rocess.},
month = {{M}arch},
title = {Detection of cyclostationarity in the presence of temporal or spatial structure with applications to cognitive radio},
year = {2016},
pages = {4249–4253},
doi = {10.1109/ICASSP.2016.7472478},
} [Abstract] One approach to spectrum sensing for cognitive radio is the detection of cyclostationarity. We extend an existing multi-antenna detector for cyclostationarity proposed by Ramírez et al. [1], which makes no assumptions about the noise beyond being (temporally) wide-sense stationary. In special cases, the noise could be uncorrelated among antennas, or it could be temporally white. The performance of a general detector can be improved by making use of a priori structural information. We do not, however, require knowledge of the exact values of the temporal or spatial noise covariances. We develop an asymptotic generalized likelihood ratio test and evaluate the performance by simulations.
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