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In laser-based free-space optical communication, besides OAM beams,Hermite-Gaussian (HG) modes or HG-mode coherently composed states (HG-MCCS) canalso be adopted as the information carrier to extend the channel capacity withthe spatial pattern based encoding and decoding link. The light field ofHG-MCCS is mainly determined by three independent parameters, including indexesof HG modes, relative initial phases between two eigenmodes, and scalecoefficients of the eigenmodes, which can obtain a large number of effectivecoding modes at a low mode order. The beam intensity distributions of theHG-MCCSs have obvious distinguishable spatial characteristics and can keeppropagation invariance, which are convenient to be decoded by the convolutionalneural network (CNN) based image recognition method. We experimentally utilizeHG-MCCS to realize a communication link including encoding, transmission underatmospheric turbulence (AT), and decoding based on CNN. With the index order ofeigenmodes within six, 125 HG-MCCS are generated and used for informationencoding, and the average recognition accuracy reached 99.5% for non-ATconditions. For the 125-level color images transmission, the error rate of thesystem is less than 1.8% even under the weak AT condition. Our work provides auseful basis for the future combination of dense data communication andartificial intelligence technology.

Hermite-Gaussian-mode coherently composed states and deep learning based free-space optical communication link