Modulation, tracking and adaptive system design for robust optical wireless communications

Abstract

Optical wireless communication (OWC) has emerged as a promising technology for high-speed wireless connectivity, offering vast unregulated bandwidth in the optical spectrum and inherent security advantages. However, the hostile nature of optical wireless channels, particularly in turbid, turbulent underwater and foggy free-space optical (FSO) environments, presents significant challenges for reliable data transmission. This thesis investigates the development of multiple complementary approaches to achieve link robustness in OWC systems, addressing challenges ranging from turbulence-induced fading and beam misalignment to time varying channel conditions through comprehensive theoretical analysis and experimental validation.

Through experimental evaluation in a controlled channel emulator, an empirical investigation of frequency-based modulation schemes combined with polarisation division multiplexing (PDM) in underwater optical wireless communication (UOWC) channels is presented. The results demonstrate that frequency-shift keying with subcarrier intensity modulation (FSK-SIM) and frequency-shift chirp modulation (FSCM) provide inherent resilience to turbulence-induced received optical intensity fluctuations. In addition, the incorporation of PDM successfully approximately doubles the system throughput while maintaining the robustness of two frequencybased techniques.

Building upon the need for link reliability, a fine tracking system for maintaining optical alignment in dynamic OWC links is developed. The system applies a photodiode array architecture with algorithm by differential intensity measurements to detect beam displacement in realtime. Experimental validation in free-space optical (FSO) channels demonstrates successful link maintenance at misalignment speeds up to 17.4 mm/s with 1.1% outage probability. Realtime image transmission experiments further validate the system’s practical impact, reducing bit error rate (BER) from 0.305 without tracking to 2.29 × 10−3 with tracking enabled.

To simultaneously maximise data throughput and maintain link robustness under varying channel conditions, the investigation is expanded through the development of camera-based adaptive transmission strategies for both underwater and atmospheric channels. A k-nearest neighbour machine learning algorithm successfully classifies turbulence levels from captured images of backscattered light patterns with 99% accuracy, distinguishing between temperature-induced and bubble-induced turbulence. The system maintains reliable communication by dynamically adjusting modulation schemes based on the channel estimation, and achieves an average 3.51 Gbps throughout varying channel conditions.

The adaptive OWC is further developed and demonstrated through a channel state information (CSI) estimation architecture based on corner-cube retroreflector. The system exploits deterministic polarisation changes induced by retroreflectors to enable transmitter side channel monitoring. The bidirectional propagation path effectively doubles the channel’s impact on the retroreflected signal, enhancing sensing sensitivity. Experimental validation demonstrates 9% throughput improvement in turbulent underwater channels and 15% improvement in foggy FSO channels compared to the best performing fixed modulation schemes.