Re-Dimensioning Number of Active eNodeBs for Green LTE Networks Using Genetic Algorithms

With the tremendous growth of cellular networks, energy consumption reduction has become a critical requirement. Recent research has focused on the base station as the main constituent that drains the highest percentage of energy consumption within cellular infrastructures. In LTE, the predominance of data traffic might result in significant volatility in traffic loads across evolved Node Bs at different times of the day. Within such context, re-dimensioning the number of active evolved Node Bs to match current average network traffic loads can contribute immensely in the reduction of projected LTE energy drainage. Accordingly, this paper introduces a re-dimensioning problem formulation which determines the adequate evolved Node Bs activity configuration in LTE networks that corresponds to a given traffic load with a desired quality of service constraint. The postulated formulation is then solved by genetic algorithm. Performance of the proposed network re-dimensioning scheme is evaluated under different traffic loads and the results are compared against those achieved by a recently proposed dynamic energy efficient distance - aware base station switch on/off scheme. Simulation results have shown that the proposed re-dimensioning solution has the potential for significant reduction in energy requirements (represented in the number of active evolved Node Bs) in comparison to the dynamic distance - aware approach reported in literature. Moreover, sub-optimality of the devised genetic algorithm solution is displayed by exhibiting attained results against the optimal solution computed through exhaustive search.