A new lp norm algorithm for interference cancellation using spatial sparsity

In this paper, a new algorithm for interference cancellation, using spatial sparsity is proposed, which improves the conventional Minimum Variance Distortionless Variance (MVDR) Algorithm. This algorithm minimizes a sparsity based cost function for interference cancellation which was proposed earlier, but could not employ norm degrees lower than 0.8. The proposed algorithm in this paper is capable of using norm degrees down to zero value. The results show that using the proposed algorithm, some reduction in sidelobe levels, deeper nulls, and no sensitivity to the data length and less sensitivity to the Direction Of Arrival mismatch are achieved. Also, simulations show that the proposed algorithm works near optimum point and convergence for low norm degrees is guaranteed.