Multi-Objective Optimization in 5G Wireless Networks With Massive MIMO

摘要

The integration of massive multiple-input multiple-output systems in the fifth generation (5G) of wireless networks requires the simultaneous consideration of several conflicting objectives, in order to achieve optimal performance and operation. In this letter, we present a complete optimization framework, which is based on multi-objective evolutionary algorithms (MOEAs), namely, non-dominated sorting genetic algorithm-II and speed-constrained multi-objective particle swarm optimization. In addition, we use a decision maker for the selection of a solution vector that achieves the best compromise solution. The results illustrate that MOEAs are particularly promising techniques for solving such multi-objective problems in 5G networks.