Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel signal classification method using cyclic spectral analysis and neural networks for multipath fading channels. The proposed system provides excellent classification performance in realistic multipath fading channels at low SNR, while assuming no a priori knowledge of the signal statistics, including carrier frequency, phase offset, or symbol rate. Due to its insensitivity to these statistics and its robustness to multipath fading channels, the Spectral Coherence Function (SOF) is employed in the proposed system to produce a highly reliable classifier. Additionally, by employing a multiple-antenna based system, even greater advantages are achieved by exploiting spatial diversity. Numerical results demonstrate the classifier performance under a variety of channel conditions. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/GLOCOM.2008.ECP.584 | IEEE Global Telecommunications Conference (Globecom) |
Keywords | Field | DocType |
modulation,neural nets,ofdm,spatial diversity,fading,a priori knowledge,correlation,neural network,signal to noise ratio,antennas | Multipath propagation,Diversity scheme,Antenna diversity,Rake receiver,Telecommunications,Fading,Computer science,Delay spread,Algorithm,Real-time computing,Fading distribution,Orthogonal frequency-division multiplexing | Conference |
ISSN | Citations | PageRank |
1930-529X | 6 | 0.57 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Like | 1 | 33 | 4.37 |
| Vasu Chakravarthy | 2 | 161 | 26.35 |
| Robert Husnay | 3 | 12 | 1.22 |
| Zhiqiang Wu | 4 | 30 | 6.60 |