An IoT-Enabled Deep Learning-Based MIMO-OFDM Scheme for Optical Camera Communication in Mobile Environments

Abstract

This study proposes Multiple-Input Multiple-Output and Orthogonal Frequency-Division Multiplexing methods, as introduced in the IEEE 802.15.7a-2024 standard, to achieve higher data rates and longer transmission ranges in OCC systems where a camera is used to capture optical signals. OFDM is a multi-carrier modulation scheme extensively used in high-data-rate wireless communications to mitigate ISI caused by multipath propagation. In optical wireless communication (OWC) systems, OFDM has been widely adopted in both indoor and outdoor applications, including eHealth, smart home, and smart IoT systems. OWC technologies provide a secure and low-interference communication channel for IoT devices using visible light. In OWC-enabled edge computing, data processing is performed in nodes, reducing communication overhead and improving system scalability. Nevertheless, user mobility remains a major challenge for OWC systems, as time-varying optical channels significantly degrade signal processing performance. Furthermore, reliable signal detection under mobility is critical for improving the signal-to-noise ratio. To overcome these challenges, this paper proposes a deep learning-based LED detection scheme for a mobility-aware MIMO-OFDM system. Deep learning techniques are also utilized to identify OFDM frame boundaries and decode the transmitted data, replacing traditional signal processing approaches. Experimental results demonstrate that the proposed method enables long-range MIMO-OFDM communication over distances of up to 22 m while maintaining a low error rate at a receiver speed of 3 m/s.