Joint optimization for base station density and user association in energy-efficient cellular networks

In the research of green networks, considering the base station (BS) density from the perspective of energy efficiency is very meaningful for both network deployment and BS sleeping based power saving. In this paper, we optimize the BS density for energy efficiency in cellular networks by the stochastic geometry theory and optimize the user association matrix by the Quantum Particle Swarm Optimization (QPSO). On one hand, we model the distribution of base stations and user equipment (UE) as spatial Poisson point process (PPP). Based on such model, we derive the closed-form expressions of the average achievable data rate, the network energy consumption and the network energy efficiency with respect to the network load. Then, we optimize the BS density for network energy efficiency maximization by adopting the Newton iteration method. On the other hand, we build a user association matrix to present the connection state between BSs and UEs, and then optimize it by QPSO. Our study reveals that we can improve the network energy efficiency by switching on/off proportion of the BSs according to the network load. The simulation results validate the theoretical analysis, and show that when the right amount of BSs is deployed according to the network load, the network energy efficiency can be maximized and the maximum energy efficiency is a fixed value once the network parameters are given.

[1]  Bhaskar Krishnamachari,et al.  Energy Savings through Dynamic Base Station Switching in Cellular Wireless Access Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[2]  Jeffrey G. Andrews,et al.  Load-Aware Modeling and Analysis of Heterogeneous Cellular Networks , 2012, IEEE Transactions on Wireless Communications.

[3]  L. Chiaraviglio,et al.  Optimal Energy Savings in Cellular Access Networks , 2009, 2009 IEEE International Conference on Communications Workshops.

[4]  Tijani Chahed,et al.  Minimizing Energy Consumption via Sleep Mode in Green Base Station , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[5]  Marco Ajmone Marsan,et al.  Energy-efficient management of UMTS access networks , 2009, 2009 21st International Teletraffic Congress.

[6]  Gerhard Fettweis,et al.  Energy consumption analysis of wireless networks using stochastic deployment models , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[7]  Frederic Paik Schoenberg,et al.  The distribution of Voronoi cells generated by Southern California earthquake epicenters , 2007 .

[8]  P. Deuflhard.  Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms , 2011 .

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

[10]  Biljana Badic,et al.  Energy Efficient Radio Access Architectures for Green Radio: Large versus Small Cell Size Deployment , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[11]  Dan Keun Sung,et al.  The Effects of Cell Size on Energy Saving, System Capacity, and Per-Energy Capacity , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[12]  Tiankui Zhang,et al.  Multi-relay selection scheme based on quantum particle swarm optimization in relay networks , 2012, The 15th International Symposium on Wireless Personal Multimedia Communications.

[13]  Gerhard Fettweis,et al.  Power consumption modeling of different base station types in heterogeneous cellular networks , 2010, 2010 Future Network & Mobile Summit.

[14]  Zhisheng Niu,et al.  Optimal Combination of Base Station Densities for Energy-Efficient Two-Tier Heterogeneous Cellular Networks , 2013, IEEE Transactions on Wireless Communications.

[15]  Daniel Crespo,et al.  Domain-size distribution in a Poisson-Voronoi nucleation and growth transformation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.