부산대학교 도서관

Inspired by the big data processing capability of the machine learning, we propose a user selection algorithm based on modified population-based incremental learning (MPBIL) for multi-user massive MIMO systems with massive connectivity. With the objective of enhancing the algorithm efficiency, the proposed algorithm evolves the population exploiting both the superior individuals and the best individual. In further, we design the orthogonal permutation to increase individual diversity and avoid the overfit of the classical population-based incremental learning. Simulation results demonstrate that the performance of the proposed algorithm is far better than the classical greedy based user selection method while maintaining low complexity, especially for the large number of MU-MIMO users and candidates.