Autoencoder-Based Transceiver Design for OWC Systems in Log-Normal Fading Channel

Abstract

In this paper, we construct the autoencoder (AE) for optical wireless communication (OWC) systems with non-negativity and peak power constraints, which provides effective transceiver design in log-normal channel. We consider the cases where perfect channel state information (CSI) or noisy CSI can be obtained under three kinds of communication rate, which is defined as the ratio of channel use number to bit number. Meanwhile, we present the block error rate (BLER) performance to further demonstrate our transceivers' superior performance than common model-based methods. The learned constellation points distribution is provided to understand the transmitter's performance. Numerical simulations are conducted to ensure the best convergence. The results indicate that AE-based transceivers can achieve model-based optimal BLER performance or provide significantly better BLER performance.

Keywords

• Deep learning
• autoencoder (AE)
• optical wireless communication (OWC)
• log-normal fading
• transceiver design.