Min-maxing interference alignment algorithm as a semidefinite programming problem

The main objective of this contribution is to develop a new interference alignment (IA) algorithm, which improves the sum-rate performance of multiuser MIMO communication systems. The recent iterative IA approaches cannot guarantee robust sum-rate performance in different K-user MIMO interference channels, especially at high SNR regime. In our proposed distributed optimization algorithm, each receiver maximizes the desired signal power while preserving the minimum interference leakage as a constraint. A convex relaxation has been applied to this optimization problem after reformulating it into semidefinite programming form. This algorithm provides orthogonal precoders and decoders, which is fairly simple in practical implementation. Simulation results of the proposed algorithm proffer significant sum-rate improvement in various interference channels compared to existing algorithms.