Frequency-domain GLR detection of a second-order cyclostationary signal over fading channels
Cyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period T can be asymptotically represented as a rank-1 frequency-domain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2014 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/22790 |
| Online Access: | https://hdl.handle.net/2117/22790 https://dx.doi.org/10.1109/TSP.2014.2303433 |
| Access Level: | Open access |
| Keyword: | Signal processing Cognitive radio networks GLRT LMPIT Cognitive radio Cyclostationarity based detection Spectral correlation Timing synchronization Frequency-smoothed cyclic periodogram Maximum-likelyhood Component Tests Noise Tractament del senyal Ràdio cognitiva Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal |
| Summary: | Cyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period T can be asymptotically represented as a rank-1 frequency-domain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support of 1/T. By resorting to the fast Fourier transform (FFT), this formulation obviates the need of estimating a cumbersome covariance matrix to characterize the likelihood function. We then derive the generalized likelihood ratio test (GLRT) for the detection of a cyclostationary signal in unknown white noise without the need of a assuming a synchronized receiver. This provides a sound theoretical basis for the exploitation of the cyclostationary feature and highlights an explicit link with classical square timing recovery schemes, which appear implicitly in the core of the GLRT. Moreover, to avoid the well-known sensitivity of cyclostationary-based detection schemes to frequency-selective fading channels, a parametric channel model based on a lower bound on the coherence bandwidth is adopted and incorporated into the GLRT. By exploiting the rank-1 structure of small spectral covariance matrices, the obtained detector outperforms the classical spectral correlation magnitude detector. |
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