DCO-OFDM Signals with Derated Power for Visible Light Communications Using an Optimized Adaptive Network-Based Fuzzy Inference System (ANFIS)

Abstract

Direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) signals used in visible light communications suffer from high peak-to-average-power ratio (PAPR) or cubic metric (CM). It strongly degrades the performance due to the great back-off necessary to avoid the clipping effect in the light-emitting diode. Thus, PAPR and CM reduction techniques become crucial to improve the system performance. In this paper, an adaptive network-based fuzzy inference system (ANFIS) is used to obtain efficient DCO-OFDM signals with a low power envelope profile. First, signals specially designed for DCO-OFDM with very low CM, as the ones obtained from the raw cubic metric (RCM)–active constellation extension method, are used to train the fuzzy systems in time and frequency domains. Second, after the off-line training, the ANFIS can generate a real-valued signal in a one-shot way with 8.9 dB of RCM reduction from the original real-valued signal, which involves a gain in the input power back off larger than 2.8 dB, an illumination-to-communication conversion efficiency gain of more than 35% and considerable improvements in bit error rate.