Machine Learning Approach to Predict the DC Bias for Adaptive OFDM Transmission in Indoor Li-Fi Applications

Multilevel quadrature amplitude modulation (M-QAM) combined with DC-bias in optical orthogonal frequency division multiplexing (DCO-OFDM) offers a spectrally efficient solution and adaptive transmission rates for indoor light-fidelity (Li-Fi) systems. However, a significant challenge posed by the DCO-OFDM scheme is the additional power of the DC bias required to ensure that the amplitudes of the transmitted signals are nonnegative. These biased signals are clipped according to optical power constraints, imposing clipping noise that affects the transmission bit error rate (BER). This performance degradation is conditioned by the adjustments made to the DC bias, which requires continuous modification to support adaptive transmission. Therefore, simultaneously addressing DC bias optimization and clipping mitigation is essential to provide reliable and power-efficient transmissions. This paper proposes a machine learning (ML) approach to predict the optimum DC bias based on the statistical properties of the OFDM signal and system characteristics. A robust ML regressor selection process using LazyPredict algorithm (LPA) was employed to identify the optimal regressors for developing the predictive model. The model demonstrated significant prediction accuracy for DC bias across a wide range of transmission settings. In particular, the models built on variants of gradient boosting regressor (GBR) and support vector regressor (SVR) demonstrated superior performance, with R-squared evaluation scores of 0.9792 and 0.9225, respectively, for two different sets of features. Furthermore, the BER performance of our adaptive DC bias approach was compared to a fixed DC bias in adaptive DCO-OFDM transmission, demonstrating the superiority of our approach in effectively mitigating clipping noise at high transmission rates while maintaining power efficiency at lower rates. These results provide a promising solution for the future practical deployment of Li-Fi systems in indoor applications.