Gergö Bognar, Stefan Baumgartner, Oliver Lang, Mario Huemer,
"Neural Network Optimal UW-OFDM"
: Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2021), IEEE, Seite(n) 389-394, 11-2021, ISBN: 978-1-6654-5828-3
Original Titel:
Neural Network Optimal UW-OFDM
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2021)
Original Kurzfassung:
We propose a novel end-to-end learning scheme for wireless communication systems employing the unique word (UW)-OFDM signaling scheme. The work is motivated by the recent advances of machine learning in channel equalization and data estimation. Our idea is to design a non-systematically
encoded UW-OFDM system optimal for neural network (NN) based estimators. To this order, we introduce model-based neural network architectures that optimize the transmitter and receiver sides, i.e. the UW-OFDM symbol generation and the NN data estimation together for minimal bit error ratio (BER). The proposed model is evaluated in a simulation environment, and compared with NN-based and traditional estimators.