Adaptive Interference Cancellation Using Atomic Norm Minimization and Denoising

The rapid increase in the number of wireless devices in modern communication networks has significantly increased the number of interference sources, which severely impacts communication reliability. Adaptive interference cancellation is rapidly becoming a necessity for modern wireless networks. For interference cancellation, a digital beamformer adaptively adjusts its weight vector using an array processing algorithm. This, in turn, shapes the radiation pattern in a manner that minimizes interference power and maximizes the desired signal power. In this letter, we propose two atomic-norm-minimization-based methods to design a weight vector that can be used to cancel interference. We present numerical and experimental studies and compare with the minimum variance distortionless response beamformer, which outputs the highest possible signal-to-interference-plus-noise ratio (SINR). We show that our approach is robust to signal corruptions arising from carrier frequency offsets. Uniquely, our algorithm extracts the offset frequencies, thus enabling interference cancellation with maximum SINR.