Genetic max-SINR algorithm for interference alignment

In this paper, we propose interference alignment (IA) algorithms inspired by Genetic Algorithm (GA). By simulations for (2 × 2, 1)³ system, we observe that the existing max-SINR (MS) algorithm converges to different sumrates for different initializations of precoders. And the initializations for which sumrate is good, cannot be found trivially using channel state information. Also, in the case of limited feedback (LFB) of precoders, the sumrates can be achieved greater than that can be achieved using conventional chordal distance, if the precoder is selected properly along with receiver combining matrix. Therefore, in this paper, two algorithms are proposed inspired by GA: first, to make the max-SINR robust to initializations: MS-GA, and second, to achieve better sumrates in case of limited feedback: MS-GA-LFB. These optimal sumrates are obtained at the cost of increased computation complexity which is proportional to the population size chosen in the Genetic Algorithm. The simulation results show that the sum rates of the proposed algorithms match with that obtained using brute force approach to find the good initialization.