Abstract

Recently, neural networks have gained prominence as potent tools in the realm of channel modeling in optical wireless communication (OWC) systems. However, existing channel networks tend to ignore random intensity noise and phase noise induced by optical sources under spontaneous and excited radiation, and likewise the shot noise of the devices. They could only characterize deterministic noise such as inter-symbol interference (ISI), signal-to-signal beat interference (SSBI), and nonlinearities. In this paper, a novel channel simulator framework to optical wireless communication systems is proposed, which can jointly learn the deterministic noise and random noise of the real channel simultaneously. The proposed approach is a comprehensive noise joint channel estimator (CNJCE) architecture. A comprehensive comparison is carried out including three classical deep learning algorithms, which are two tributaries heterogeneous neural network (TTHnet) with posteriori additive white Gaussian noise (AWGN), white Gaussian noise layer-based channel estimator (WGNCE) and conditional generative adversarial network (CGAN) based channel model. To represent both deterministic noise and random noise modeling capabilities, an overall estimation score is also proposed. The final experimental results show CNJCE achieved the highest average estimation scores of 79.86 and 97.52 in FSO and UVLC channels respectively. In contrast, TTHnet without AWGN or WGNCE only achieved 39.83 and 43.82. And CGAN based channel model completely lost signal characteristics, far worse than CNJCE. To our knowledge, this marks the first time that deterministic and random noise have been fully modeled in optical wireless communication, establishing a foundational framework for comprehensive end-to-end learning paradigms.