Abstract
A single antenna interference cancellation (SAIC) scheme is proposed. The scheme is motivated by the challenging asynchronous and non-stationary interference cancellation problem in wireless communications. Suppose the existence of the single-user region (SUR) of the desired signal, we propose to first recognize the SUR via a novel detection scheme. The SUR detection scheme proposed consists of the pseudo-observation matrix construction and the information theoretic criterion-based source number estimation. A basis set for the desired signal is then learnt over the SUR, via dictionary learning techniques. In the final signal recovery stage, a novel constrained sparse coding (CSC) process is proposed. The CSC reforms the conventional sparse coding (SC) via incorporating signal-specific constraints. Unlike existing SAIC algorithms, the proposed scheme is completely independent of a prior knowledge on the interferences. Numerical results are provided to demonstrate the effectiveness of the above proposed schemes under varied interference intensities and environmental noise levels. The proposed scheme outperformed competing SAIC schemes by about 5dB signal-to-interference-plus-noise ratio (SINR) improvement. The proposed CSC scheme outperformed the conventional SC by about 9dB SINR improvement, besides the superior recovery fidelity of signal property.
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