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

Abstract

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.

Life is Short: The Impact of Power States on Base Station Lifetime

“…We refer the reader to [29] for more details about the optimization model and the obtained results. For each BS, we collect the frequency and duration of each power state obtained from the solution of the problem.…”

“…We refer the reader to [29] for a comprehensive description. In brief, we consider a scenario with 33 UMTS macro BSs and a service area (SA) of 9.2 × 9.2 km 2 .…”

“…We consider an energy-aware algorithm and a realistic UMTS cellular deployment scenario, both obtained from [29]. We refer the reader to [29] for a comprehensive description.…”

“…We then compute the average saving in the network S(t). The saving is computed by adopting the power model presented in [29,33] for the UMTS case and LTE one, respectively. Moreover, we collect the time spent in each power state and the frequency of power transitions for each BS, and we compute the average acceleration factor in the network vs. time AF tot (t).…”

A Solar Energy Solution for Sustainable Third Generation Mobile Networks

“…The issue of BS switching can be treated as an optimization problem that reduces the number of active BSs, while addressing the access network's data traffic load. Various methods used to switch off a precise number of Node Bs in a universal mobile telecommunications system's (UMTS) cellular network during low data traffic periods, were discussed in [10][11][12][13]. The possibility of switching off several cells and Node Bs during low-traffic periods, while ensuring the quality of service constraints in terms of blocking probability and electromagnetic exposure limits, was discussed in [10].…”