Abstract:
The rapid expansion and development of communication networks and systems
in the optical domain causes conventionally used methods in the transmission of
long haul networks, to enter the systems that cover shorter transmission distances
underneath 100 km. They can be used in various applications where high-speed
connectivity is needed like the inter and intra data center interliks, optical access
networks, indoor and in-building communication. There are many approaches
which can be used for short-reach communication, but they are subjected to
the complexity and high margins. Machine learning (ML) approaches provide
key solutions for numerous challenging situations due to their robust decision making, problem solving, and pattern recognition abilities. In the domain of
signal processing, ML techniques have been studied extensively in short-reach
optical communications and networks. We will apply deep learning approaches to
randomly generated dataset. A deep neural network (DNN), which includes the
transmitter, channel, and receiver for an optical fiber communication network is
to be modeled and implemented. Transceiver optimization can be implemented
in a single end-to-end operation by enabling this approach. The channel model
consists of different impairments like chromatic dispersion (CD), nonlinearity,
and attenuation. We expect to predict the symbol error rate (SER) of received
output in the existence of numerous channel impairments with minimum error
in short-reach optical fiber communication networks.
