Adaptive Interference Cancellation Using Atomic Norm Minimization

The rapid increase in number of wireless devices in modern communication networks has significantly increased the number of interference sources, which severely impacts communication reliability. With the projected further increase in the number of wireless devices, adaptive interference cancellation is quickly becoming a necessity for modern networks. During interference cancellation, a digital beamformer adaptively adjusts its weight vector using an array processing algorithm, which in turn shapes the radiation pattern in a manner that minimizes interference power and maximizes the desired signal power. In this work, we propose two atomic norm minimization-based methods to design a weight vector that can be used to simultaneously cancel the interference and remove the impact of frequency offset in both noiseless and noisy frameworks. Numerical simulations indicate that our proposed methods significantly outperform the classic SMI technique.