Fiber communication receiver models based on the multi-head attention mechanism

In this paper, an artificial-intelligence-based fiber communication receiver model is put forward. With the multi-head attention mechanism it contains, this model can extract crucial patterns and map the transmitted signals into the bit stream. Once appropriately trained, it can obtain the ability to restore the information from the signals whose transmission distances range from 0 to 100 km, signal-to-noise ratios range from 0 to 20 dB, modulation formats range from OOK to PAM4, and symbol rates range from 10 to 40 GBaud. The validity of the model is numerically demonstrated via MATLAB and Pytorch scenarios and compared with traditional communication receivers.