Optimizing Spectrum-Energy Efficiency in Downlink Cellular Networks

The popularity of smart mobile devices has brought significant growth of data services for mobile service providers. Mobile users of data services are charged based on the amount of data used. Raising served data amount seemingly increases the profit; energy consumption rises correspondingly. Besides, spectral resources are licensed and limited for mobile operators to allocate. Increasing data services over the spectrum for the profit does not count the cost of energy. To assess the profitability, considered is the revenue-to-cost ratio. Optimizing the ratio is an economic incentive for mobile operators. Revenue is regarded as efficiency in spectrum use, the cost as energy consumption; therefore we interpret the revenue-to-cost ratio as spectrum-energy efficiency. In this paper, we study the spectrum-energy efficiency optimization problem where BSs are with the ability to perform cell zooming, sleep mode, and user migration. We formulate the problem into an integer linear program which is solvable by CPLEX to maximize spectrum-energy efficiency; meanwhile traffic demands by associated users in multicell/multiuser networks are met. To avoid high computation time, a heuristic algorithm is proposed to efficiently solve the formulated problem. Numerical analysis through case studies demonstrates energy consumption and efficiency improvements, and comparisons between near-optimal solutions against optimality.

[1]  G. Plitsis.  Coverage prediction of new elements of systems beyond 3G: the IEEE 802.16 system as a case study , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[2]  W. Afric,et al.  WiMAX on 3.5 GHz Cell Size Calculation , 2006, Proceedings ELMAR 2006.

[3]  J.T. Louhi,et al.  Energy efficiency of modern cellular base stations , 2007, INTELEC 07 - 29th International Telecommunications Energy Conference.

[4]  Yiwei Thomas Hou,et al.  On optimal throughput-energy curve for multi-hop wireless networks , 2011, 2011 Proceedings IEEE INFOCOM.

[5]  Preben E. Mogensen,et al.  Fixed Frequency Reuse for LTE-Advanced Systems in Local Area Scenarios , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[6]  Mario Pickavet,et al.  Energy efficiency in communications , 2010 .

[7]  Muhammad Ali Imran,et al.  On the Energy Efficiency-Spectral Efficiency Trade-Off in the Uplink of CoMP System , 2012, IEEE Transactions on Wireless Communications.

[8]  Zhisheng Niu,et al.  Cell zooming for cost-efficient green cellular networks , 2010, IEEE Communications Magazine.

[9]  Gerhard Fettweis,et al.  Energy Efficiency Aspects of Base Station Deployment Strategies for Cellular Networks , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[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]  Marceau Coupechoux,et al.  Limiting Power Transmission of Green Cellular Networks: Impact on Coverage and Capacity , 2010, IEEE International Conference on Communications.

[12]  Ieee Microwave Theory,et al.  IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions , 2007 .

[13]  Gerhard Fettweis,et al.  The global footprint of mobile communications: The ecological and economic perspective , 2011, IEEE Communications Magazine.

[14]  O. Yemets,et al.  Solving optimization problems with linear-fractional objective functions and additional constraints on arrangements , 2006 .

[15]  Zhang Chao,et al.  Green Mobile Access Network with Dynamic Base Station Energy Saving , 2009 .

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

[17]  Albrecht J. Fehske,et al.  Energy efficiency gains in interference-limited heterogeneous cellular mobile radio networks with random micro site deployment , 2011, 34th IEEE Sarnoff Symposium.

[18]  Liesbet Van der Perre,et al.  Challenges and enabling technologies for energy aware mobile radio networks , 2010, IEEE Communications Magazine.

[19]  Eiko Seidel,et al.  Heterogeneous LTE Networks and Inter-Cell Interference Coordination , 2011 .

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

[21]  Young-il Kim,et al.  Performance evaluation of multi-hop relay system with deployment scenarios , 2008, MILCOM 2008 - 2008 IEEE Military Communications Conference.

[22]  P. Demeester,et al.  Business scenarios for a WiMAX deployment in Belgium , 2007, 2007 IEEE Mobile WiMAX Symposium.

[23]  Sourjya Bhaumik,et al.  Breathe to stay cool: adjusting cell sizes to reduce energy consumption , 2010, Green Networking '10.

[24]  Masood Maqbool,et al.  Comparison of Various Frequency Reuse Patterns for WiMAX Networks with Adaptive Beamforming , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

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

[26]  Zhisheng Niu,et al.  Toward dynamic energy-efficient operation of cellular network infrastructure , 2011, IEEE Communications Magazine.

[27]  Holger Claussen,et al.  Leveraging advances in mobile broadband technology to improve environmental sustainability , 2009 .

[28]  Adam Wolisz,et al.  Primary user behavior in cellular networks and implications for dynamic spectrum access , 2009, IEEE Communications Magazine.

[29]  M. Cetron,et al.  Energy efficiency enhancements in radio access networks , 2004 .

[30]  Cong Xiong,et al.  Energy- and Spectral-Efficiency Tradeoff in Downlink OFDMA Networks , 2011, IEEE Transactions on Wireless Communications.