New machine learning approach could give a big boost to the efficiency of optical networks

New work leveraging machine learning could increase the efficiency of optical telecommunications networks. As our world becomes increasingly interconnected, fiber optic cables offer the ability to transmit more data over longer distances compared to traditional copper wires. Optical Transport Networks (OTNs) have emerged as a solution for packaging data in fiber optic cables, and improvements stand to make them more cost-effective.

A group of researchers from Universitat Politècnica de Catalunya in Barcelona and the telecom company Huawei have retooled an artificial intelligence technique used for chess and self-driving cars to make OTNs run more efficiently. They will present their research at the upcoming Optical Fiber Conference and Exposition, to be held 3-7 March in San Diego, California, USA.

OTNs require rules for how to divvy up the high amounts of traffic they manage and writing the rules for making those split-second decisions becomes very complex. If the network gives more space than needed for a voice call, for example, the unused space might have been better put to use ensuring that an end user streaming a video doesn’t get “still buffering” messages.

What OTNs need is a better traffic guard. ..Read More..

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