SLM-based PAPR reduction for improved performance of DCO-OFDM LiFi using blind estimation for healthcare monitoring system

Abstract

The necessity for reliable healthcare monitoring following the COVID-19 pandemic has highlighted the limitations of RF-based devices in medical settings. Visible light communication (VLC), which provides inherent security and is resistant to RF interference, is a good alternative. This work proposes a VLC system using DC-biased optical (DCO) orthogonal frequency division multiplexing (OFDM) for resilient, high-speed biomedical data transmission through indoor optical fading channel. In this system, data is modulated using quadrature amplitude modulation (QAM) with order 4 and 16. Four equalization methods; block-type, comb-type, superimposed training (ST), and blind channel estimation (CE); are implemented across three patient positioning scenarios. We integrate blind channel estimation and SLM-based PAPR reduction for a dynamic healthcare LiFi scenario. A comprehensive comparative analysis of CE techniques is conducted under realistic patient positioning (LOS/NLOS) conditions. An analysis of the trade-off between spectral efficiency and energy-to-noise ratio is examined in this context. Simulation results reveal a high Peak-to-Average Power Ratio (PAPR), reaching 15 dB with block-type CE. To mitigate this, Selected Mapping (SLM) is applied with three complex phase sequences, and three real sequences, achieving up to 4 dB PAPR reduction with no Bit Error Rate (BER) degradation. At 28 dB SNR, BER values were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for blind, block-type, comb-type, and ST CE, respectively. Spectral efficiency declines with increased multipath, yet blind CE maintains the highest performance, reaching 0.9 bits/s/Hz with 20 multipath components. Additionally, complex phase vectors in SLM provide an extra 1 dB PAPR gain over real-valued versions.