# Artificial Intelligence Enhances the Performance of Chaos-based Wireless Communication

## TL;DR

This paper demonstrates how artificial intelligence, specifically convolutional neural networks, can improve chaos-based wireless communication by predicting future symbols to reduce interference and enhance signal quality.

## Contribution

It introduces a novel AI-based approach using CNNs to predict symbols and optimize thresholds, improving chaos-based wireless communication performance.

## Key findings

- CNN-based prediction reduces inter-symbol interference
- The method improves bit error rate performance
- Numerical and experimental results validate the approach

## Abstract

Some new findings for chaos-based wireless communication systems have been identified recently. First, chaos has proven to be the optimal communication waveform because chaotic signals can achieve the maximum signal to noise ratio at receiver with the simplest matched filter. Second, the information transmitted in chaotic signals is not modified by the multipath wireless channel. Third, chaos properties can be used to relief inter-symbol interference (ISI) caused by multipath propagation. Although recent work has reported the method of obtaining the optimal threshold to eliminate the ISI in chaos-based wireless communication, its practical implementation is still a challenge. By knowing the channel parameters and all symbols, especially the future symbol to be transmitted in advance, it is almost an impossible task in the practical communication systems. Owning to Artificial intelligence (AI) recent developments, Convolutional Neural Network (CNN) with deep learning structure is being proposed to predict future symbols based on the received signal, so as to further reduce ISI and obtain better bit error rate (BER) performance as compared to that used the existing sub-optimal threshold. The feature of the method involves predicting the future symbol and obtaining a better threshold suitable for time variant channel. Numerical simulation and experimental results validate our theory and the superiority of the proposed method.