Interference alignment via Feasible Point Pursuit

Designing iterative algorithms for interference alignment (IA) is very useful for both practical and theoretical purposes. However, the existing works on iterative IA algorithms have not reported significant gains in terms of the DoF (Degrees of Freedom) over simple orthogonalization schemes. In this paper, we aim to design an iterative IA algorithm that can achieve high DoF. We recast the problem of designing linear transceivers for interference alignment as a non-convex quadratic feasibility problem, and apply an extension of the recently proposed Feasible Point Pursuit Successive Convex Approximation (FPP-SCA) algorithm [8] to solve it. Simulations suggest that the proposed algorithm can attain DoF very close to the known theoretical upper bound in certain cases, significantly outperforming existing approaches.