Direct Symbol Decoding using GA-SVM in Chaotic Baseband Wireless Communication System

TL;DR

This paper introduces a GA-SVM based symbol detection method for chaotic baseband wireless communication, converting symbol decoding into a binary classification task to enhance BER performance and simplify the detection process.

Contribution

It proposes a novel GA-SVM approach for direct symbol decoding in chaotic wireless systems, reducing complexity and improving BER over traditional threshold-based methods.

Findings

Better BER performance in static and time-varying channels

Effective in practical wireless multipath environments

Simplifies symbol detection process

Abstract

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.