Enhanced Performance of a Cascaded Receiver Consisting of a DNN-Based Waveform-to-Symbol Converter and Modified NN-Based DD-LMS in CAP Underwater VLC System

Abstract

Underwater visible light communication (UVLC) based on LEDs has become a competitive candidate, which is able to provide high data rates, low latency and low cost for next-generation wireless communication technologies. However, it is still challenging to achieve high-speed communication because of bottleneck problems such as bandwidth limitation and linear and nonlinear distortions. Traditional Deep-learning Neural Network (DNN)-based waveform-to-symbol converter is verified to be an effective method to alleviate them, but impractical due to high complexity. To achieve a better tradeoff between communication performance and computation complexity, a cascaded receiver consisting of a DNN-based waveform-to-symbol converter and modified Neural Network (NN)-based decision-directed least mean square (DD-LMS) is then innovatively proposed. With fewer taps and nodes than the traditional converter, the front-stage converter could mitigate the majority of Inter-Symbol Interference (ISI) and signal nonlinear distortions. Then modified NN-based DD-LMS is cascaded to improve communication performance by reducing phase offset, making received constellation points more concentrated and closer to standard constellation points. Compared with the traditional converter, the cascaded receiver could achieve 89.6% of signal Vpp dynamic range with 12.4% of complexity in the 64APSK UVLC system. Moreover, the ratio of signal Vpp dynamic range and total trainable parameters is 1.24 × 10−1 mV, while that of the traditional converter is 1.95 × 10−2 mV. The cascaded receiver used in 64APSK UVLC systems is experimentally verified to achieve enhanced performance, thus as a promising scheme for future high-speed underwater VLC.