Full-duplex Transceiver Design: A GMM Clustering Approach

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital domain channel estimations and SIC. The frame structure of the designed FDT contains two successive phases: labelling phase and data transmission phase. In particular, the designed FDT performs cluster labelling in the labelling phase and performs GMM clustering based on expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the labelling symbol design is optimized for the overhead reduction and clustering accuracy improving. Finally, simulation results show that, the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge, meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) channel estimator.