Optimizing Energy Efficiency of MIMO Using Quantum Genetic Algorithm

We introduce a novel quantum genetic algorithm (QGA) that selects the optimum extreme (minimum or maximum) value of an unconstrained goal function with very low computational complexity. The quality of the initial candidate solutions of the classical genetic algorithm (CGA) has a strong influence on the speed of convergence to the best optimum result. To boost the quality of the initial selected random candidate solutions, we merge the CGA with a quantum extreme value searching algorithm (QEVSA). We exploited the proposed QGA as an embedded computational infrastructure for the uplink multiple-input multiple-output (MIMO) system. The algorithm maximizes the energy efficiency of the uplink MIMO system. Simulation results show that the suggested QGA successfully achieves maximum energy efficiency by determining the best transmit power of the active users.