Self-interference cancellation for full-duplex MIMO transceivers

Full-duplex operation requires effective self-interference (SI) cancellation that in turn needs reliable SI channel estimation. In this paper, we develop two estimation algorithms suitable for a 2-stage SI cancellation structure. By exploiting the sparsity of the SI channel, we first derive a compressed sensing-based SI channel estimation algorithm to be used in the first SI cancellation stage at radio-frequency (RF) to reduce the SI. We then develop a subspace-based algorithm to jointly estimate the residual SI channel, the intended channel and the transmitter nonlinearities for the second SI cancellation stage at baseband. Including the intended received signal in the estimation process is the main advantage of the proposed algorithm as compared to previous works that assume it as additive noise. Simulation results show that the proposed algorithms outperform the least-square (LS) algorithm and offer higher signal-to-residual-interference-and-noise ratio (SINR) over a large received signal-to-noise ratio (SNR) range.