Abstract
Amplitude jitter is a common phenomenon in pulse amplitude modulation (PAM) visible light communication (VLC) systems, which greatly affects the system performance. In this paper, we propose a novel time-amplitude two-dimensional re-estimation based on density-based spatial clustering of applications with noise (DBSCAN) algorithm of machine learning (2DDB) to distinguish different signal levels with jitter. The Q factor of a PAM-8 VLC system applying 2DDB is improved by 1.6–3.2 dB through experimental demonstration. In addition, we also investigate the influence of amplitude jitter with different levels on PAM-8 systems, and analyze the range of parameters and applications of a DBSCAN algorithm. To the best of our knowledge, this is the first time that DBSCAN of machine learning is successfully applied to PAM VLC systems.
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