Energy-Efficient Wireless Cellular Communications through Network Resource Dynamic Adaptation

Energy-Efficient Wireless Cellular Communications through Network Resource Dynamic Adaptation

Josip Lorincz (FESB, University of Split, Split, Croatia)
DOI: 10.4018/jbdcn.2013040102
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Abstract

Cellular networks represent one of the major energy consumers of communication networks and their contribution to the global carbon footprint and energy consumption continuously and rapidly increases. Improving energy efficiency of the cellular access networks become an important requirement and has recently gained considerable attention of the research community and operators. In this paper, improving cellular networks energy efficiency through dynamic adaptation of network resources is presented with foundations which justify practical realization of such approach. Paper gives insight into how the traffic pattern variations and transmitted power scaling influence on the instantaneous power consumption of the base stations. Also, impact of the base stations Tx power on two prominent energy efficiency metrics of the cellular access network is discussed. Results of a proposed optimization approach which is based on dynamic adaptation of the base stations on/off activity and the transmitted power in accordance with the spatial and temporal variations of traffic are presented. According to obtained results, dynamic adaptation of network resources can offer significant monthly energy savings on the level of complete cellular access network.
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As part of the ICT (Information and Communications Technology) sector, mobile radio networks contribute a rather small portion of global greenhouse gas (GHG) emissions (0.2%) (Richter, 2009; Vadgama, 2009). However, energy consumed by this sector is not negligible and contributes 15–20% of the energy consumption of the entire ICT sector (Fettweis, 2008). According to estimations, the radio access part of a cellular network, more specifically the base station (BS), is a major energy consumer (Chen, 2010). The share of the BSs’ energy consumption in the total cellular network energy consumption is between 55 and 80% (Ha, 2011; Guo, 2011; Wu 2012). With rising energy prices, BSs, as the most significant energy consumer in wide area cellular networks, contribute up to 50% of the total operational expenditures (OPEX) of an operator (Correia, 2010). Hence, improving the energy efficiency of cellular access networks has been an important economic issue since reducing energy consumption translates to lower operator OPEX.

A comprehensive survey of techniques dedicated to energy savings in cellular networks is provided in Hasan (2012). Authors explore some research issues and challenges and suggest techniques to enable an energy efficient cellular network. In particular, standardization of energy efficiency metrics, improvements in power amplifier technology, development of power saving protocols such as sleep modes, energy-aware cooperative BS power management with adaptive adjustment of BS Tx power known as “Cell zooming” are discussed. Furthermore, techniques such as: spectrum sensing, energy-aware MAC and routing, efficient resource management, cross-layer optimization and renewable energy sources are also presented.

Extensive overview of relevant challenges in the area of green cellular networks and solutions considering: relay techniques, heterogeneous cells, operator’s cooperation, power and granularity control, etc., have been discussed in Oh (2011). In addition, using real data traces from part of the real urban area network, authors emphasize promising potential in terms of power saving that one can expect by turning off BSs during low traffic periods.

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless data networks are presented in Meshkati (2007). Authors have introduced a number of non-cooperative and distributed power control games in which each user seeks to maximize its own utility while satisfying its QoS requirements in multiple access networks. The impact of advanced signal processing on energy efficiency and network capacity is demonstrated and the tradeoffs among throughput, delay, network capacity and energy efficiency are discussed.

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