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A TLBO-Based Energy Efficient Base Station Switch off and User Subcarrier Allocation Algorithm for OFDMA Cellular Networks


Affiliations
1 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Iran, Islamic Republic of
     

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Downlink of a cellular network with orthogonal frequency-division multiple access (OFDMA) is considered. Joint base station switch OFF and user subcarrier-allocation with guaranteed user quality of service, is shown to be a promising approach for reducing network’s total power consumption. However, solving the aforementioned mix-integer and nonlinear optimization problem requires robust and powerful optimization techniques. In this paper, teaching-learning based optimization algorithm has been adopted to lower cellular network’s total power consumption. The results show that the proposed technique is able to reduce network’s total power consumption by determining a near optimum set of base stations to be switched OFF and near optimum subcarrier-user assignments. It is shown that the proposed scheme is superior to existing base station switch OFF schemes. Robustness of the proposed TLBO-based technique is verified.

Keywords

Energy Efficiency, Green Cellular Network, Base Station Switching, Optimization, TLBO Algorithm.
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  • A. Abrol and R.K. Jha, “Power Optimization in 5G Networks: A Step towards Green Communication”, IEEE Access, Vol. 4, pp. 1355-1374, 2016.

  • Z. Hasan, H. Boostanimehr and V. Bhargava, “Green Cellular Networks: A Survey, Some Research Issues and Challenges”, IEEE Communications Surveys and Tutorials, Vol. 13, No. 4, pp. 524-540, 2011.

  • R. Mahapatra et al., “Energy Efficiency Trade-off Mechanism towards Wireless Green Communication: A Survey”, IEEE Transactions on Wireless Communications, Vol. 18, No. 1, pp. 686-705, 2016.

  • O. Arnold et al., “Power Consumption Modeling of Different Base station Types in Heterogeneous Cellular Networks”, Proceedings of Future Network and Mobile Summit, pp. 1-8, 2010.

  • A. Fehske, F. Richter and G. Fettweis, “Energy Efficiency Improvements through Micro Sites in Cellular Mobile Radio Networks”, Proceedings of IEEE GLOBECOM Workshop, pp 1-5, 2009.

  • L. Correia et al., “Challenges and Enabling Technologies for Energy Aware Mobile Radio Networks”, IEEE Communications Magazine, Vol. 48, No. 11, pp. 66-72, 2010.

  • C. Murthy and C. Kavitha, “A Survey of Green Base stations in Cellular Networks”, International Journals of Computer Networks and Wireless Communications, Vol. 2, No. 2, pp. 232–236, 2012.

  • P. Belotti et al., “Mixed-integer Nonlinear Optimization”, http://homepages.cae.wisc.edu/~linderot/papers/Belotti-Et-Al-12-TR.pdf.

  • D. Ngo and T. Le-Ngoc, “Joint Sub Channel Assignment and Power Allocation for OFDMA Femtocell Networks”, IEEE Transactions on Wireless Communications, Vol. 13, No. 1, pp. 342-355, 2014.

  • C. Chang et al., “On Optimal Cell Activation for Coverage Preservation in Green Cellular Networks”, IEEE Transactions on Mobile Computing, Vol. 13, No. 11, pp. 2580-2591, 2014.

  • Y. Chen, W. Sheen and L. Wang, “Optimization of Cyclic-Delay Diversity Aided Frequency-Selective Scheduling in OFDMA Downlink Systems”, IEEE Transactions on Vehicular Technology, Vol. 63, No. 4, pp. 1645-1659, 2014.

  • Y. Chiu et al., “GGRA: a Feasible Resource-Allocation Scheme by Optimization Technique for IEEE 802.16 Uplink Systems”, IEEE Transactions on Vehicular Technology, Vol. 59, No. 3, pp. 1393-1401, 2010.

  • E. Danish et al., “Content-aware Resource Allocation in OFDM Systems for Energy Efficient Video Transmission”, IEEE Transactions on Consumer Electronics, Vol. 60, No. 3, pp. 320-328, 2014.

  • R. Elliott and W. Krzymien, “Downlink Scheduling via Genetic Algorithms for Multiuser Single-Carrier and Multicarrier MIMO Systems with Dirty Paper Coding”, IEEE Transactions on Vehicular Technology, Vol. 58, No. 7, pp. 3247-3262, 2009.

  • J. Zhang et al., “Evolutionary-Algorithm-Assisted Joint Channel Estimation and Turbo Multiuser Detection/Decoding for OFDM/SDMA”, IEEE Transactions on Vehicular Technology, Vol. 63, No. 3, pp. 1204-1222, 2014.

  • H. Lang, S. Lin and W. Fang, “Subcarrier Pairing and Power Allocation with Interference Management in Cognitive Relay Networks Based on Genetic Algorithms”, IEEE Transactions on Vehicular Technology, Vol. 65, No. 9, pp. 7051-7063, 2015.

  • N. Sharma and A.S. Madhukumar, “Genetic Algorithm Aided Proportional Fair Resource Allocation in Multicast OFDM Systems”, IEEE Transactions on Broadcasting, Vol. 61, No. 1, pp. 16-29, 2015.

  • R. Andreotti et al., “Resource Allocation via Max-Min Good Put Optimization for BIC-OFDMA Systems”, IEEE Transactions on Communications, Vol. 64, No. 6, pp. 2412-2426, 2016.

  • K. Bagadi and S. Das, “Neural Network-based Adaptive Multiuser Detection Schemes in SDMA-OFDM System for Wireless Application”, Neural Computing and Applications, Vol. 23, No. 3-4, pp. 1071-1082, 2013.

  • D. Astely et al., “A Future Radio Access Framework”, IEEE Journal on Selected Areas in Communications, Vol. 24, No. 3, pp. 693-706, 2006.

  • Rysavy Research, “Transition to 4G: 3GPP broadband evolution to IMT advanced”, Available at: https://www.ic.gc.ca/eic/site/smt-gst.nsf/vwapj/dgso-001-10-3GAmericas-atatchment.pdf/$FILE/dgso-001-10-3GAmericas-atatchment.pdf.

  • W. Rhee and J. Cioffi, “Increase in Capacity of Multiuser OFDM System using Dynamic Subchannel Allocation”, Proceedings of IEEE 51st Vehicular Technology Conference, pp. 1085-1089, 2000.

  • F. Shams, G. Bacci and M. Luise, “A Survey on Resource Allocation Techniques in OFDM (A) Networks”, Computer Networks, Vol. 65, pp. 129-150, 2014.

  • R. Bolla et al., “Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-aware Fixed Network Infrastructures”, IEEE Communications Surveys and Tutorials, Vol. 13, No. 2, pp. 223-244, 2011.

  • T. Han and N. Ansari, “On Greening Cellular Networks via Multicell Cooperation”, IEEE Wireless Communications, Vol. 20, No. 1, pp. 82-89, 2011.

  • B. Badic et al., “Energy Efficient Radio Access Architectures for Green Radio: Large Versus Small Cell Size Deployment”, Proceedings of IEEE 70th International Conference on Vehicular Technology, pp. 1-5, 2009.

  • T. Le and M. Nakhai, “Possible Power-Saving Gains by Dividing a Cell into Tiers of Smaller Cells”, Electronics Letters, Vol. 46, No. 16, pp. 1163-1165, 2010.

  • B. Badic et al., “Effect of the Base station Antenna Beam Tilting on Energy Consumption in Cellular Networks”, Proceedings of IEEE 72nd International Conference on Vehicular Technology, pp. 1-5, 2010.

  • F. Cao and Z. Fan, “The Tradeoff between Energy Efficiency and System Performance of Femtocell Deployment”, Proceedings of IEEE 7th International Symposium on Wireless Communication Systems, pp. 315-319, 2010.

  • T. Tamadoni, M. Eslami, Sh. Jam and D. Davarpanah, “A Novel Antenna Allocation Technique for Green Single Cell MIMO and MIMO-CoMP Downlink Transmission”, AEU-International Journal of Electronics and Communications, Vol. 80, pp. 59-66, 2017.

  • M.A. Marsan et al., “Optimal Energy Savings in Cellular Access Networks”, Proceedings of IEEE International Conference on Communications, pp. 1-5, 2009.

  • E. Oh and B. Krishnamachari, “Energy Savings through Dynamic Base station Switching in Cellular Wireless Access Networks”, Proceedings of IEEE International Conference on Global Telecommunications, pp. 1-5, 2010.

  • A. Hajijamali Arani et al., “Distributed ON/OFF Switching and Dynamic Channel Allocation: Decreasing Complexity and Improving Energy Efficiency”, Transactions on Emerging Telecommunications Technologies, Vol. 28, No. 12, pp. 1-12, 2015.

  • K. Samdanis et al., “Self-organized Energy Efficient Cellular Networks”, Proceedings of IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications, pp. 1665-1670, 2010.

  • A. Bousia et al., “Green Distance-Aware Base Station Sleeping Algorithm in LTE Advanced”, Proceedings of IEEE International Conference on Communications, pp. 1347-1351, 2012.

  • C. Xiong et al., “Energy-Efficient Resource Allocation in OFDMA Networks”, IEEE Transactions on Communications, Vol. 60, No. 12, pp. 3767-3778, 2015.

  • S. Elayoubi, L. Saker and T. Chahed, “Optimal Control for Base station Sleep Mode in Energy Efficient Radio Access Networks”, Proceedings of IEEE Conference on INFOCOM, pp. 106-110, 2011.

  • Z. Niu et al., “Cell Zooming for Cost-efficient Green Cellular Networks”, IEEE Communications Magazine, Vol. 48, No. 11, pp. 74-79, 2010.

  • S. Bhaumik et al., “Breathe to Stay Cool: Adjusting Cell sizes to reduce Energy Consumption”, Proceedings of 1st ACM SIGCOMM Workshop on Green Networking, pp. 41-46, 2010.

  • G. Koutitas, “Green Network Planning of Single Frequency Networks”, IEEE Transactions on Broadcasting, Vol. 56, No. 4, pp. 541-550, 2010.

  • H. Ghazzai et al., “Optimized Smart Grid Energy Procurement for LTE Networks using Evolutionary Algorithms”, Transactions on Vehicular Technology, Vol. 63, No. 9, pp. 4508-4519, 2014.

  • L. Xiang et al., “Adaptive Traffic Load-Balancing for Green Cellular Networks”, Proceedings of IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications, pp. 41-45, 2011.

  • D. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison Wesley Publisher, 1989.

  • M. Clerc, “Particle Swarm Optimization”, Available at: https://kamenpenkov.files.wordpress.com/2016/01/pso-m-clerc-2006.pdf

  • D. Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization”, Technical Report, Department of Computer Engineering, Erciyes University, 2005.

  • M. Dorigo and C. Blum, “Ant Colony Optimization: A Survey”, Theoretical Computer Science, Vol. 344, No. 2-3, pp. 243-278, 2011.

  • K.S. Lee and Z.W. Geem, “A New Meta-Heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice”, Computer Methods in Applied Mechanics and Engineering, Vol. 194, No. 36-38, pp. 3902-3933, 2005.

  • A. Ahrari and A.A. Atai, “Grenade Explosion Method a Novel Tool for Optimization of Multimodal Functions”, Applied Soft Computing, Vol. 10, No. 4, pp. 1132-1140, 2010.

  • R.V. Rao, V.D. Kalyankar and G. Waghmare, “Parameters Optimization of Selected Casting Processes using Teaching Learning based Optimization Algorithm”, Applied Mathematical Modelling, Vol. 38, No. 23, pp. 5592-5608, 2014.

  • M.H. Khooban, “Design an Intelligent Proportional-Derivative Feedback Linearization Control for Nonholonomic-Wheeled Mobile Robot”, Journal of Intelligent & Fuzzy Systems, Vol. 26, No. 4, pp. 1833-1843, 2014.

  • R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching-Learning-based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problem”, Computer-Aided Design, Vol. 43, No. 3, pp. 303-315, 2011.


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  • A TLBO-Based Energy Efficient Base Station Switch off and User Subcarrier Allocation Algorithm for OFDMA Cellular Networks

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Authors

Danial Davarpanah
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Iran, Islamic Republic of
Mohammad Reza Zamani
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Iran, Islamic Republic of
Mohsen Eslami
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Iran, Islamic Republic of
Taher Niknam
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Iran, Islamic Republic of

Abstract


Downlink of a cellular network with orthogonal frequency-division multiple access (OFDMA) is considered. Joint base station switch OFF and user subcarrier-allocation with guaranteed user quality of service, is shown to be a promising approach for reducing network’s total power consumption. However, solving the aforementioned mix-integer and nonlinear optimization problem requires robust and powerful optimization techniques. In this paper, teaching-learning based optimization algorithm has been adopted to lower cellular network’s total power consumption. The results show that the proposed technique is able to reduce network’s total power consumption by determining a near optimum set of base stations to be switched OFF and near optimum subcarrier-user assignments. It is shown that the proposed scheme is superior to existing base station switch OFF schemes. Robustness of the proposed TLBO-based technique is verified.

Keywords


Energy Efficiency, Green Cellular Network, Base Station Switching, Optimization, TLBO Algorithm.

References