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Abstract
The massive Multiple-Input Multiple-Output (MIMO) is key enabling technology for the 5G and 6G cellular technologies, which allows dramatically improving the energy efficiency of the network, as well as increasing the transmission bit rate. In this paper. We developed a new quantum genetic algorithm for handling non-constrained objective functions. The study aims to compare the performance of the classical genetic algorithm and its newly extended quantum version in minimizing the overall transmit power of the downlink massive MIMO system. In terms of the total number of performed generations and total transmit power, a simulation environment was used to demonstrate the efficiency of the quantum genetic strategy versus the classical one.
