Exploiting Gaussian Mixture Model Clustering for Full-Duplex Transceiver Design

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 coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a 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 estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. 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) or least mean square (LMS) channel estimator.