Optical wireless communications for next-generation radio access networks

Abstract

High-speed and high-bandwidth capabilities provided by free space optical wireless communication (FSO-WC) improve communication technologies with better channel security. With its high carrier frequency, wide bandwidth, and use of unlicensed spectrum, FSO has been identified by researchers looking into innovations in next-generation wireless communications as a promising way to deliver ultrafast data links to meet the growing demands for massive connectivity and high data rates in a variety of 6G applications, such as cellular wireless backhauls and heterogeneous networks. However, issues like atmospheric turbulence, absorption, and scattering have a major impact on the system’s performance by raising the bit error rate (BER) and symbol error rate (SER). In order to tackle these problems, this paper looks at Deep Neural Network (DNN) models, particularly Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). We experiment DNN-based equalizer in context of Open Radio Access Network (O-RAN), which aims to minimize SER and BER. According to the investigation, CNNs use more processing resources than MLPs, although offering superior error reduction. Our investigation shows that FSO can be adopted in high data rate front haul between the distributed units (DUs) and radio units (RUs).