Abstract
In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from 108 to 107 in multiplication and 106 to 108 in addition on the training phase.
Highlights
Published: 27 December 2021The application of machine learning technique in optical communication systems has been studied in many fields in recent years [1,2]
As a new method, the artificial neural network (ANN) has been of great interest on channel equalization in the field of wireless communication [8,9,10,11], which shows its advantage on the better bit error rate (BER)
Due to the influence of dispersion on the channel, the whole system, with the increase of transmission distance, dispersion will seriously affect the bit error rate, so this paper focuses on the long-distance fixed length channel characteristic learning
Summary
The application of machine learning technique in optical communication systems has been studied in many fields in recent years [1,2]. In the field of optical communication systems, many parts of the system, such as performance monitoring, fiber nonlinearity mitigation, carrier recovery, and equalization, have been optimized by machine learning and a neural network [3,4,5,6]. As a new method, the artificial neural network (ANN) has been of great interest on channel equalization in the field of wireless communication [8,9,10,11], which shows its advantage on the better bit error rate (BER). Since Deep learning relies on features of the data and situation, it cannot be efficiently trained under the changeable situation of long-distance communication
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