  To retrieve the information from the serious distorted received signal is the
key challenge of communication signal processing. The chaotic baseband
communication promises theoretically to eliminate the inter-symbol interference
(ISI), however, it needs complicated calculation, if it is not impossible. In
this paper, a genetic algorithm support vector machine (GA-SVM) based symbol
detection method is proposed for chaotic baseband wireless communication system
(CBWCS), by this way, treating the problem from a different viewpoint, the
symbol decoding process is converted to be a binary classification through
GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at
the receiver, so as to improve the bit error rate (BER) performance of the
CBWCS and simplify the symbol detection process by removing the channel
identification and the threshold calculation process as compared to that using
the calculated threshold to decode symbol in the traditional methods. The
simulation results show that the proposed method has better BER performance in
both the static and time-varying wireless channels. The experimental results,
based on the wireless open-access research platform, indicate that the BER of
the proposed GA-SVM based symbol detection approach is superior to the other
counterparts under a practical wireless multipath channel.
