Optimization of base station sleeping strategy and resource allocation in wireless cellular networks using artificial Protozoa Optimizer

In recent years, achieving energy efficiency (EE) has become a critical focus due to the rising demand for environmentally friendly communication systems. This study presents an integrated framework for optimizing base station (BS) deactivation, resource allocation, and renewable energy usage to enhance network efficiency while reducing carbon emissions. A mixed-integer programming model is employed to capture the interdependencies between these factors, and the Artificial Protozoa Optimizer (APO) algorithm is proposed to determine optimal BS deactivation strategies. Additionally, the Lagrange duality method is utilized to optimize power distribution, subcarrier allocation, and energy procurement. Extensive simulations demonstrate that the proposed framework reduces carbon emissions by up to 40 % (achieving as low as 1. 8 kg/hour) while simultaneously improving network profit by up to 21 % compared to equal-power allocation baselines. The framework maintains robust performance under realistic inter-cell interference, preserves quality-of-service (coverage >99 % even with 60 % BS sleep ratio), and scales efficiently to dense user loads (up to 50 users per cell). Sensitivity analysis further reveals that the environmental and economic gains are attainable under practical policy regimes, including moderate carbon pricing (≥0. 02 /kg CO₂) and renewable energy subsidies. These findings validate the efficiency, practicality, and policy-awareness of the proposed optimization framework, establishing its potential as a comprehensive solution for sustainable wireless networks. • Increases HC by 14. 2 % and 9. 57 %, respectively, reducing annual operating costs by 58 % and 11. 86 %. • The robust model achieves a 12. 4 % higher HC and a 23. 7 % lower cost. • It yields a 9. 6 % HC gain and an 11. 9 % cost saving. • Delivering 41. 75 MW HC at an annual cost of 2. 72 M.