Intelligent optical transceiver technology based on federated learning traffic prediction

Abstract

With the development of optical network, modern optical network needs better performance. Because the traditional optical transceiver technology has a delay according to the flow switching transmission configuration, the delay optical network service still adopts the original configuration transmission, so a certain degree of frequency spectrum resources waste and high blocking rate will be caused. The above situation can be improved if the transmission configuration can be deployed in advance based on the predicted traffic. Federated learning is a scheme of distributed training model, which can train the traffic prediction model in distributed way under the premise of ensuring the privacy of client data, which is very suitable for the traffic prediction of optical network terminals. This paper proposes an intelligent optical transceiver technology based on federal learning traffic prediction, applies the federal learning on the traffic prediction of optical communication network terminal, distributed training traffic prediction model, and deploy the optical transceiver early transmission configuration such as modulation format and baud rate parameters, thus to weaken the delay of optical transceiver technology, reduce the network blocking rate and improve the transmission performance of optical network.