Impact of service rates and base station switching granularity on energy consumption of cellular networks

作者：Lorincz Josip^*; Capone Antonio; Begusic Dinko

来源：EURASIP Journal on Wireless Communications and Networking, 2012, 2012(1): 342.

DOI：10.1186/1687-1499-2012-342

• Green
• Energy
• Wireless
• Mobile
• Cellular
• Management
• Switching
• Base station
• Power
• Network
• Optimization
• Rate
• UMTS

Over the last two decades we have witnessed significant growth of the cellular network energy consumption caused by a rapid increase in the number of mobile users and data volumes. This is contributed to by a tenfold increase of data rates every 5 years, and such a trend in up growth of energy consumption will continue with the introduction of a new throughput demanding services. Hence, development of new energy-saving techniques and estimation of the influence on energy savings when ensuring different service rates are the focus of this article. For the purpose of reducing cellular network energy consumption, this article proposes a new approach to energy-efficient management of network resources. The energy-efficient management is based on adaptive changes of on/off states of a complete base station (BS) site in accordance with the traffic pattern variations. Besides adaptations to the temporal traffic variations, the BS can adapt capacity to the spatial traffic variations through dynamic scaling of transmitted power according to capacity demand. We formulate the problem of energy-efficient management as a binary integer programming problem dedicated to energy consumption minimization of a complete network. Proposed methodology is simulated on a set of real size Universal Mobile Telecommunications System network instances consisting of different radio propagation environments. In addition, this article analyzes the influence on the energy savings potential of BSs switching granularity. Obtained results show that the proposed optimization approach offers significant reductions in the network energy consumption while preserving the most important QoS constraints like full area coverage and guaranteed service rates.

• 出版日期2012