Energy Cooperation Among Sustainable Base Stations in Multi-Operator Cellular Networks

Energy Harvesting technology contributes significantly to green cellular networking by ensuring self-sustainability and extinguishing environmental hazards. Due the imbalance between harvested energy traffic load of base stations (BSs), cooperation has become a crucial requirement. However, decision optimal among BSs in multi-operator network is challenging task due consideration various factors, such as cost, loss energy, future information load, BSs, etc. The two conflicting objectives are minimizing buying cost while transferring through power links. In this work, we present an framework, formulated multi-objective linear programming (MOLP) problem which brings trade-off above-mentioned considering at time slots. For prediction develop Deep Q-Learning-based method that intelligently increases measurement accuracy continuous exploration exploitation. results simulation experiments carried out MATLAB depict proposed framework outperforms state-of-the-art works terms performance, energy-loss reduction.