Energy efficiency analysis of a C-RAN with distance—Based power control

Reducing the global energy consumption is an important goal of fifth generation (5G) networks. The cloud-radio access network (C-RAN) along with power control mechanism can potentially offer the much sought after relief in energy consumption by enabling centralized processing. In this paper, a tunable downlink distance-based power control mechanism is considered and its effects on network-level coverage probability, along with energy efficiency (EE) of C-RAN are studied. Finding from analysis based on stochastic geometry reveals that distance-based power control can provide up to a seven-fold increase in the EE of C-RAN without power control. The significance of this finding lies in showing that by carefully tuning the transmit power, interference reduces and the achievable average rate improves, resulting in increased EE.

[1]  D. Stoyan,et al.  Stochastic Geometry and Its Applications , 1989 .

[2]  Henrik Lehrmann Christiansen,et al.  A framework for joint optical-wireless resource management in multi-RAT, heterogeneous mobile networks , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[3]  Marco Di Renzo,et al.  Average Rate of Downlink Heterogeneous Cellular Networks over Generalized Fading Channels: A Stochastic Geometry Approach , 2013, IEEE Transactions on Communications.

[4]  Abraham O. Fapojuwo,et al.  Analysis of load dependent energy efficiency of two-tier heterogeneous cellular networks , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[5]  Tony Q. S. Quek,et al.  Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks , 2012, IEEE Journal on Selected Areas in Communications.

[6]  Jeffrey G. Andrews,et al.  Stochastic geometry and random graphs for the analysis and design of wireless networks , 2009, IEEE Journal on Selected Areas in Communications.

[7]  Jeffrey G. Andrews,et al.  A Tractable Approach to Coverage and Rate in Cellular Networks , 2010, IEEE Transactions on Communications.

[8]  Zhengang Pan,et al.  Toward green and soft: a 5G perspective , 2014, IEEE Communications Magazine.

[9]  Fadhel M. Ghannouchi,et al.  Throughput reliability analysis of cloud-radio access networks , 2016, Wirel. Commun. Mob. Comput..

[10]  Hamed S. Al-Raweshidy,et al.  Component and parameterised power model for cloud radio access network , 2016, IET Commun..

[11]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[12]  Jeffrey G. Andrews,et al.  Fractional power control for decentralized wireless networks , 2007, IEEE Transactions on Wireless Communications.

[13]  Gerhard Fettweis,et al.  Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective? , 2015, IEEE Journal on Selected Areas in Communications.

[14]  Timothy X. Brown,et al.  Cellular performance bounds via shotgun cellular systems , 2000, IEEE Journal on Selected Areas in Communications.

[15]  C-ran the Road towards Green Ran , 2022 .

[16]  Ljupco Jorguseski,et al.  Energy Saving in Wireless Access Networks , 2010 .

[17]  François Baccelli,et al.  Stochastic geometry and wireless networks , 2009 .

[18]  Muhammad Ali Imran,et al.  How much energy is needed to run a wireless network? , 2011, IEEE Wireless Communications.