Energy-efficient strategies with base station power management for green wireless networks
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In this thesis, our objective is to improve the energy efficiency and load balance for wireless networks. We first study the relationships between the base station (BS) on/off operation and traffic distribution. A cooperative power saving method called clustering BS-off (CBSO) scheme is proposed. Instead of adopting a unified and consistent BS-off scheme in the whole network, the proposed centralized and distributed CBSO schemes can adaptively group BSs in several clusters based on the traffic fluctuations with space and time. Second, to further improve the network load balance and energy efficiency in distributed manner, we propose a power efficient self-organized virtual small networking (VSN) protocol. A heuristic firefly algorithm is applied to arrange the BSs' operation in small groups based on the traffic level. By jointly considering the load balance, the effectiveness of the proposed algorithm is demonstrated based on the average and min-max traffic levels of BSs' groups. Finally, the importance of detailed BS operation between active and sleep modes is considered. The operating procedure of femtocell base station, i.e., HeNB, is modeled as an MAP/PH/1/k queueing system. Such queueing analysis particularly focuses on the HeNB vacation process with user priorities. The HeNB's power on/off scheme is modeled as alternative service and vacation periods. The hybrid access is regarded as high and low priority users in the queuing system. We further propose the adaptive service rate and vacation length (ASV) method, so that the HeNB can work in a more energy-efficient way while satisfying QoS requirements such as blocking probability and users waiting time. Simulation results show the effectiveness of the proposed strategies and the overall network energy efficiency can be improved significantly.