skip to main content
10.1145/2507924.2507961acmconferencesArticle/Chapter ViewAbstractPublication PagesmswimConference Proceedingsconference-collections
research-article

Distributed base station activation for energy-efficient operation of cellular networks

Published: 03 November 2013 Publication History

Abstract

Dynamic base station activation (DBA) has recently emerged as a viable solution for reducing energy consumption in cellular networks. While most of the works on this topic focused on centralized decision making algorithms, in this paper we investigate distributive solutions. These solutions are particularly desirable due to importance of self-organization and self-optimization in future cellular networks. The goal of DBA is to achieve an optimal trade-off between network operator's revenue and operational cost while guaranteeing coverage for network users. The problem is posed as a network utility maximization aiming to find the optimal activation schedule of each base station. Using Lagrangian duality, the problem is decomposed into smaller subproblems, where each subproblem is solved locally at its associated base station. Controlled message passing among base stations ensures convergence to the global optimal solution. Moreover, this general solution is further extended to capture the combinatorial nature of DBA. Finally, numerical results are provided to demonstrate the behavior of our solution in terms of utility and cost trade-off and convergence in some example network scenarios.

References

[1]
Long term evolution of the 3gpp radio technology. http://www.3gpp.org/LTE/.
[2]
LTE-Advanced. http://www.3gpp.org/article/lte-advanced.
[3]
AT&T SXSW Press Release. http://www.att.com/Common/docs/SXSW_Network%20Fact_Sheet.doc, Mar. 2011
[4]
Cisco visual networking index: Global mobile data traffic forecast update. http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.pdf, Feb. 2013.
[5]
D. P. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.
[6]
D. P. Bertsekas and J. N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Prentice Hall Inc., 1989.
[7]
P. Bhat, S. Nagata, L. Campoy, I. Berberana, T. Derham, G. Liu, X. Shen, P. Zong, and J. Yang. LTE-Advanced: An Operator Perspective. IEEE Commun. Mag., 50(2):104--114, 2012.
[8]
S. Borst, M. Markakis, and I. Saniee. Distributed Power Allocation and User Assignment in OFDMA Cellular Networks. In In Proc. Allerton Conference on Communication, Control, and Computing, pages 1055--1063, Urbana, USA, Sep 2011.
[9]
M. H. Cheung, A. H. Mohsenian-Rad, V. W. Wong, and R. Schober. Utility-Optimal Random Access for Wireless Multimedia Sensor Networks. IEEE Commun. Lett., 1(4):340--343, 2012.
[10]
FCC Spectrum Policy Task Force. Report of the Spectrum Ffficiency Working. http://www.fcc.gov/sptf/reports.html, Nov. 2002.
[11]
R. Gandhi, S. Kuller, S. Parthasarathy, and A. Srinivisan. Dependent Rounding and its Applications to Approximation Algorithms. Journal of the ACM, 53(3):324--360, 2006.
[12]
J. Gong, S. Zhou, Z. Niu, and P. Yang. Traffic-Aware Base Station Sleeping in Dense Cellular Networks. In Proc. IEEE IWQoS, pages 1--2, Beijing, China, Jun 2010.
[13]
J. Huang, R. A. Berry, and M. L. Honig. Distributed Interference Compensation for Wireless Networks. IEEE J. Sel. Areas Commun., 24(5):1074--1084, 2006.
[14]
J. Huang, V. G. Subramanian, R. Agrawal, and R. A. Berry. Downlink Scheduling and Resource Allocation for OFDM Systems. IEEE Trans. Wireless Commun., 8(1):288--296, 2009.
[15]
X. Lin and N. B. Shroff. Utility Maximization for Communication Networks with Multipath Routing. IEEE Trans. Automat. Contr., 51(5):766--781, 2006.
[16]
D. Lopez-Perez, I. Guvenc, G. D. L. Roche, M. Kountouris, T. Q. S. Quek, and J. Zhang. ENHANCED INTERCELL INTERFERENCE COORDINATION CHALLENGES IN HETEROGENEOUS NETWORKS. IEEE Wireless Commun. Mag., 18(3):22--30, 2011.
[17]
M. A. Marsan and M. Meo. Energy Efficient Management of two Cellular Access Networks. ACM SIGMETRICS Performance Evaluation Review, 37(4):69--73, 2010.
[18]
M. A. Marsan and M. Meo. Green Wireless Networking: Three Questions. In Proc. IFIP Med-Hoc-Net, pages 41--44, Sicily, Italy, Jun 2011.
[19]
D. W. K. Ng and R. Schober. Resource Allocation and Scheduling in Multi-Cell OFDMA Systems with Decode-and-Forward relaying. IEEE Trans. Wireless Commun., 10(7):2246--2258, 2011.
[20]
E. Oh and B. Krishnamachari. Energy Savings Through Dynamic Base Station Switching in Cellular Wireless Access Networks. In Proc. IEEE GlobeCom, pages 1--5, Miami, USA, Dec 2010.
[21]
C. Peng, S.-B. Lee, S. Lu, H. Luo, and H. Li. Traffic-Driven Power Saving in Operational 3G Cellular Networks. In Proc. ACM MobiCom, pages 121--132, Las Vegas, USA, Sep 2011.
[22]
S. Shakkottai and R. Srikant. Network Optimization and Control. Foundations and Trends® in Networking, 2(3), 2007.
[23]
K. Son, H. Kim, Y. Yi, and B. Krishnamachari. Base Station Operation and User Association mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks. IEEE J. Sel. Areas Commun., 29(8):1525--1536, 2011.
[24]
B. Sriperumbudur and G. Lanckriet. On the convergence of the concave-convex procedure. In Proc. NIPS, pages 1759--1767, Vancouver, Canada, Dec 2009.
[25]
A. Yuille and A. Rangarajan. The Concave-Convex Procedure (CCCP). In Proc. NIPS, volume 2, pages 1033--1040, Vancouver, Canada, 2002.
[26]
S. Zhouy, J. Gongy, Z. Yangy, Z. Niuy, and P. Yang. Green Mobile Access Network with Dynamic Base Station Energy Saving. In ACM MobiCom posters, Beijing, China, Sep 2009.

Cited By

View all
  • (2019)Switching Constrained Max-Weight Scheduling for Wireless NetworksIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737401(2314-2322)Online publication date: Apr-2019
  • (2018)Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching CostsIEEE/ACM Transactions on Networking10.1109/TNET.2018.286987426:6(2501-2514)Online publication date: 1-Dec-2018
  • (2017)Augmenting max-weight with explicit learning for wireless scheduling with switching costsIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8056983(1-9)Online publication date: May-2017
  • Show More Cited By

Index Terms

  1. Distributed base station activation for energy-efficient operation of cellular networks

    Recommendations

    Reviews

    RuayShiung Chang

    Wireless communications are becoming ubiquitous. Wi-Fi and third-generation (3G) or fourth-generation (4G) connections are everywhere. For Wi-Fi, you need access points. For 3G or 4G, you need base stations for your cellular phones to connect. As the demand grows, so do the deployments of base stations. It is estimated that base stations account for 60 to 80 percent of the total network energy consumption. This paper proposes a distributed base station activation method for saving base station energies, and hence the operation costs. It uses a distributed algorithm based on Lagrangian dual decomposition, in which each base station computes its optimal activation probability through message exchange with its neighbors. Besides saving base station energies, there are two other things to consider. The first is user satisfaction about the service. The second is whether the frequent on-off will hurt the base station electronics, thus increasing the maintenance cost. For evaluating the quality of a particular active set of base stations with respect to network users' satisfaction, the paper models user satisfaction via the notion of utilities. A utility function is associated to each user, where the user utility is an increasing function of the amount of resources allocated to the user. However, response time is more important to users. The frequent on-off of base stations will increase response time since rebooting a base station takes time. The authors should also consider it in the modeling. Another thought is to design a multiple-power-state base station, not just on-off state. Then, depending on the traffic, the system could have more energy-efficient system control without jeopardizing performance. Finally, will frequent on-off reduce the lifetime of a base station__?__ Also, what is the best size of an area for this algorithm to work__?__ The simulation uses a square of 1,200 meters. Is it optimal__?__ This paper could be improved by including more useful and relevant citations. There is much more research available about base station power consumption than the authors cite. Online Computing Reviews Service

    Access critical reviews of Computing literature here

    Become a reviewer for Computing Reviews.

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    MSWiM '13: Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
    November 2013
    468 pages
    ISBN:9781450323536
    DOI:10.1145/2507924
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 November 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. cellular networks
    2. energy efficiency
    3. network utility maximization

    Qualifiers

    • Research-article

    Conference

    MSWiM '13
    Sponsor:

    Acceptance Rates

    MSWiM '13 Paper Acceptance Rate 42 of 184 submissions, 23%;
    Overall Acceptance Rate 398 of 1,577 submissions, 25%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2019)Switching Constrained Max-Weight Scheduling for Wireless NetworksIEEE INFOCOM 2019 - IEEE Conference on Computer Communications10.1109/INFOCOM.2019.8737401(2314-2322)Online publication date: Apr-2019
    • (2018)Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching CostsIEEE/ACM Transactions on Networking10.1109/TNET.2018.286987426:6(2501-2514)Online publication date: 1-Dec-2018
    • (2017)Augmenting max-weight with explicit learning for wireless scheduling with switching costsIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8056983(1-9)Online publication date: May-2017
    • (2017)Load-shared redundant interface for LTE access network2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)10.1109/BMSB.2017.7986193(1-5)Online publication date: Jun-2017
    • (2015)On Intelligent Base Station Activation for Next Generation Wireless NetworksProcedia Computer Science10.1016/j.procs.2015.08.31563(82-88)Online publication date: 2015
    • (2014)Online algorithms for energy cost minimization in cellular networks2014 IEEE 22nd International Symposium of Quality of Service (IWQoS)10.1109/IWQoS.2014.6914332(302-307)Online publication date: May-2014

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media