Akademik Veri Yönetim Sistemi
WIRELESS PERSONAL COMMUNICATIONS, cilt.141, sa.1, ss.217-247, 2025 (SCI-Expanded, Scopus)
Efficient energy usage is a critical concern in cellular communication systems, as in many other fields. However, it is known that energy-saving measures applied in cellular communication systems may have a negative impact on the area throughput, which is of vital importance and considered unacceptable to decrease. Energy efficiency and spectral efficiency are two essential performance metrics for modern wireless communication systems, particularly with the rollout of 5G networks and the increasing demand for high-speed data services. However, these metrics are often conflicting, and improving one may negatively impact the other, leading to a challenging optimization problem. Therefore, in this paper, the solution to this problem is focused on the optimization of energy and spectral efficiencies in massive Multi-Input Multi-Output (MIMO) systems, which are known for being successful in these two efficiencies. For these optimization processes, a new intelligent optimization algorithm is proposed to determine the optimal configurations among various parameter variations, specifically by varying the number of users (up to 70), the number of active antennas (up to 100), and the transmission power (up to 200 mW) in massive MIMO systems. The proposed modified multi-objective artificial bee colony algorithm demonstrated significant success in optimizing energy efficiency and spectral efficiency trade-off. It has been demonstrated superior convergence efficiency to optimal results after low number of iterations for the trade-off problem. The proposed algorithm has achieved a higher success rate than the multi-objective genetic algorithm, multi-objective bat algorithm, multi-objective particle swarm optimization, multi-objective differential evolution algorithm, multi-objective sperm fertilization procedure optimization algorithm and multi-objective artificial bee colony algorithms by performing Pareto optimal front estimation with an inverted generational distance value of 0.503. The results demonstrate that the optimal conditions for cells in massive MIMO systems (transmission power, number of users, and number of antennas) can be determined in terms of energy efficiency and spectral efficiency.