Analysis of Typical Applications and Key Challenges in Optical Communications within the Context of the Internet of Things

Abstract

As the demand for interconnecting IoT devices and transmitting data grows, traditional wireless communications face bottlenecks in capacity and stability. Optical communication technology, with its advantages of high bandwidth, low latency, and high security, has become a key enabling technology for the Internet of Things, offering broad application prospects in industrial and agricultural sectors, smart cities, and other fields. However, its widespread adoption still faces numerous challenges, such as insufficient environmental adaptability, poor software-hardware compatibility, and the lack of effective non-linear compensation. This paper focuses on fields such as industrial manufacturing, smart cities, and smart homes, systematically analyzing typical applications of optical communications alongside existing challenges. To address signal degradation in dynamic environments, it explores deep learning equalization techniques based on LSTMDNN hybrid models. To facilitate hardware-software compatibility and industrialization, strategies including developing low-cost LED driver chips and establishing unified interface standards are proposed. Furthermore, CEO-OFDM combined with LSTM-DNN hybrid models is employed to resolve nonlinear compensation issues. This research aims to provide theoretical underpinnings and practical guidance for the application of optical communications within the Internet of Things.