Modulation classification based compressed sensing for communication signals
2009
The theory of compressed sensing (CS) has shown that compressible signals can be accurately reconstructed from a very
small set of randomly projected measurements. Sparse representation of the signals plays an important role in the signal
reconstruction of compressed sensing. In this paper, we propose to use signal modulation information to obtain a better
sparse representation for communication signals in compressed sensing. In our approach, a tree-structured modulation
classification system is used to classify five types of signal modulations: Amplitude Modulation (AM), Frequency
Modulation (FM), Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK) and Phase Shift Keying (PSK). The
tree-structured classification system uses four signal features to classify the five modulation types, and all features are
computable in the analog domain. To select a sparse transformation for the input signal, we propose a pre-trained
Karhunen-Loeve transform (KLT) based CS, in which a set of KLT transformation matrices is obtained by an offline
learning process for all modulation types. In an online real-time process, the modulation information of the input signal
is classified and then used to select one of the pre-trained KLT matrices for providing a better sparse representation of
the signal for CS-based signal reconstruction. Our experimental results show that our modulation classification technique
is effective in identifying the five modulation types of noisy input signals, and our KLT based CS reconstruction has
much better performances than Fourier and wavelet packet based CS for the communication signals we tested.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
5
References
0
Citations
NaN
KQI