Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communications
arXiv:2402.15288v11 citationsh-index: 72024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN)
Originality Synthesis-oriented
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This work addresses the need for efficient signal processing in optical communications, but it is incremental as it applies an existing ANN method to a specific hardware implementation.
The authors tackled the problem of equalization in optical communications by implementing an artificial neural network-based equalizer on an FPGA, achieving real-time performance for a 30 GBd PAM2 system.
In this work, we present a high-throughput field programmable gate array (FPGA) demonstrator of an artificial neural network (ANN)-based equalizer. The equalization is performed and illustrated in real-time for a 30 GBd, two-level pulse amplitude modulation (PAM2) optical communication system.