A Joint Statistical Moment-Based Blind Channel Estimation and Modified TL-RCT Companding Approach for DCO-OFDM in VLC-Driven IoT Applications

Visible light communication (VLC) has emerged as a promising technology for high speed, energy efficient data transmission in Internet of Things (IoT) applications. However, direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM), which is extensively adopted in VLC systems, suffers from a high peak to average power ratio (PAPR) and imperfect channel state information, thereby limiting overall system performance. This paper proposes a novel joint framework that integrates a statistical moment based blind channel estimation method with a modified two level rooting companding transform (TL-RCT) to improve the performance of DCO-OFDM systems in VLC environments. Initially, a TL-RCT scheme using two companding parameters is introduced to satisfy system requirements. However, it results in an increase in average signal power. To overcome this limitation, a modified TL-RCT scheme is developed to attain effective companding while maintaining constant average signal power. Besides, a statistical moment based blind channel estimation technique is applied, exploiting first order statistical properties of the received signal to precisely estimate the channel without the use of pilot symbols. The proposed framework concurrently enables significant PAPR reduction and blind channel estimation while preserving signal integrity. Simulation data exhibit substantial enhancements in PAPR reduction, BER performance, and net gain. Specifically, the modified TL-RCT scheme achieves a PAPR reduction of 8.7 dB and a net gain enhancement of 8.5 dB compared to existing companding algorithms. These results indicate that the proposed scheme is highly suitable for energy efficient and interference resilient VLC enabled IoT communication systems.