Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.
Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications
