Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation. (arXiv:2304.06987v1 [eess.SP])

In recent years, communication engineers put strong emphasis on artificial
neural network (ANN)-based algorithms with the aim of increasing the
flexibility and autonomy of the system and its components. In this context,
unsupervised training is of special interest as it enables adaptation without
the overhead of transmitting pilot symbols. In this work, we present a novel
ANN-based, unsupervised equalizer and its trainable field programmable gate
array (FPGA) implementation. We demonstrate that our custom loss function
allows the ANN to adapt …

aim algorithms ann artificial arxiv autonomy communication components context engineers fpga function implementation loss network neural network novel performance pilot training unsupervised work