Abstract
This paper addresses the self-interference (SI) cancellation at baseband for full-duplex MIMO communication systems in consideration of practical transmitter imperfections. In particular, we develop a subspace-based algorithm to jointly estimate the SI and intended channels and the nonlinear distortions. By exploiting the covariance and pseudo-covariance of the received signal, we can increase the dimension of the received signal subspace while keeping the dimension of the signal subspace constant, and hence, the proposed algorithm can be applied to most of full-duplex MIMO configurations with arbitrary numbers of transmit and receive antennas. The channel coefficients are estimated, up to an ambiguity term, without any knowledge of the intended signal. A joint detection and ambiguity identification scheme is proposed. Simulation results show that the proposed algorithm can properly estimate the channel with only one pilot symbol and offers superior SI cancellation performance.
Highlights
Half-duplex transmission is commonly used in the current communication systems by transmitting and receiving over orthogonal channels
The SI is first cancelled at the radio-frequency (RF) level, prior to the low-noise amplifier (LNA) and the analog-to-digital converter (ADC), to avoid overloading/saturation of these devices [1,2,3]
Noting that the intended signal is unknown, we propose to use a novel subspace method to efficiently estimate the different parameters
Summary
Half-duplex transmission is commonly used in the current communication systems by transmitting and receiving over orthogonal channels. The dimension of the signal subspace is 2NNt. the dimension of the signal subspace is 2NNt It follows that, to obtain a nondegenerate noise subspace, its dimension NrM − 2NtN should be larger than zero, and the number of receiving antennas should be larger than the number of transmitting antennas to make the subspace method work, and in [5], we developed the linear subspace algorithm for this setting. The use of the augmented received vector is usually referred as widely linear processing In this case, the augmented covariance matrix Ry of y has the following structure: Ry = HRuHH + σ 2I2MNr ,. This is a common property with other subspace-based estimators [17]
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