Cell Load-Aware Energy Saving Management in Self-Organizing Networks

Self-organizing networks are considered to be the next generation technology for network management and, therefore, also treated as one possible way to tackle economical and ecological challenges in the future. In this paper, we extend our framework for integrated self-organizing networks by the energy saving management self-organizing network use case. In contrast to coordination among multiple use cases, this approach targets at an integrated solution, where the coordination is inherent in the optimization enabling managing multiple use cases concurrently. Moreover, it considers dynamic traffic and interference situations, and predicts cell loads for various network configurations based on measurements of receiving and traffic conditions. Using the cell load as a proxy for evaluating network performance and base station energy consumption, we show that a joint optimization of cell individual offsets and antenna downtilts is superior to the adjustment of cell individual offsets only. Throughout a day, we observe remarkable improvements in user throughputs and reduction in network energy consumption further enhanced by switching off base stations.