A digital subspace-based self-interference cancellation in full-duplex MIMO transceivers

This paper addresses the problem of digital self-interference (SI) cancellation in full-duplex systems. Under practical transmitter imperfections, the received SI is affected by transmitter nonlinearities and propagation channel, which need to be estimated in order to cancel the SI. The proposed estimation method is based on subspace decomposition. The major detriment of subspace technique is the need of oversampling or multisensor receiver to obtain a nondegerate noise subspace. We modify the traditional subspace techniques by exploiting the covariance and the pseudo-covariance of the received signal. This enables us to increase the dimension of the received signal without resulting to oversampling or multisensor receiver. The different parameters are estimated, up to an ambiguity term, without any knowledge of the intended signal. We develop a joint detection and ambiguity identification procedure that requires a considerably smaller number of pilots than standard training-based methods. Simulation results show that the proposed algorithm can properly estimate the SI channel coefficients and the nonlinear parameters without any pilot symbol from the intended transmitter.