Joint Self-Interference Cancellation and Data Estimation for OFDM Based Full-Duplex Communication Systems

Description

In this work, we study joint self-interference cancellation (SIC) and data estimation in a full-duplex (FD) communication system utilizing neural networks (NNs). The proposed approaches are developed for an FD communication system with 5G conform OFDM transmit and receive signals and a data transmission over a multipath channel. We investigate three different options for conducting SIC and data estimation with NNs. First, we aim to train two NNs consecutively, the first one performing SIC in time domain and the second NN performing data estimation in frequency domain. Here, we follow a two-step approach where the NN conducting SIC is trained initially, and after freezing its trained parameters the NN for data estimation is optimized. In the second setup, both NNs are trained jointly in an end-to-end manner, while the basic structure of the first setup remains the same. In contrast to that, for the third scenario, we employ only one NN for both SIC and data estimation. We compare the aforementioned approaches in terms of achievable BER performance and computational complexity with a state-of-the-art model-based approach, where a polynomial SI canceller and a zero-forcing OFDM data estimator are applied.

Period	30 Oct 2023
Event title	Asilomar Conference on Signals, Systems and Computers (ACSSC 2023)
Event type	Conference
Location	United StatesShow on map