Network Efficiency Amendment Utilizing Cloud Radio Access Network In Mobile Communications

  • Abstract
  • Keywords
  • References
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  • Abstract

    Mobile data traffic is finding exponential growth currently in telecommunications industry. It has become important to concentrate on both spectral and energy efficiencies in utilizing cellular networks under green communication standpoint. Thus, for 5G the utmost priority is that to increase data traffic and reduce the total network energy ingesting by half. The proposed work is to design the Cloud Radio Access Network (C-RAN) with energy efficient, flexible and capacity-enhanced features by effectively bundling and establishing relation between BBU and RRU utilizing Catechistic technique. Mathematical results with realistic parameters prove that the projected optimization design clearly improve the energy efficiency of C-RAN’s compared to standard schemes.


  • Keywords

    Bundling up, Catechistic algorithm, Data sharing, Energy efficient, Mapping RRH and BBU

  • References

      [1] Online:

      [2] Hussein Taleb, Melhem El Helou, Kinda Khawam, Samer Lahoud, and Steven Martin, “Centralized and Distributed RRH Clustering in Cloud Radio Access Networks”, Jul 2017

      [3] F. Richter, A. J. Fehske, and G. P. Fettweis, “Energy efficiency aspects of base station deployment strategies for cellular networks,” in Proc. Int. IEEE Veh. Technol. Conf. (VTC), Sep. 2009, pp. 1–5.

      [4] B. Dai and W. Yu, “Energy efficiency of downlink transmission strategies for cloud radio access networks,” IEEE J. Sel. Area. Commun., vol. 34, no. 4, pp. 1037–1050, Apr. 2016.

      [5] P. Gandotra, R. K. Jha, and S. Jain, “Green communication in next generation cellular networks: A survey,” IEEE Access, vol. 5, pp. 11 727– 11 758, 2017.

      [6] J. Zuo, J. Zhang, C. Yuen, W. Jiang, and W. Luo, “Energy efficient user association for cloud radio access networks,” IEEE Access, vol. 4, pp.2429–2438, 2016.

      [7] R. Sun, M. Hong, and Z. Q. Luo, “Joint downlink base station association and power control for max-min fairness: Computation and complexity,” IEEE J. Sel. Areas Commun., vol. 33, no. 6, pp. 1040–1054, Jun. 2015.

      [8] K. G. Nguyen, Q. D. Vu, M. Juntti, and L. N. Tran, “Energy efficient preceding C-RAN downlink with compression at fronthaul,” in Proc.IEEE Int. Conf. Commun. (ICC), May 2017, pp. 1–6.

      [9] L. Liu andW. Yu, “Cross-layer design for downlink multihop cloud radio access networks with network coding,” IEEE Trans. Signal Process., vol. 65, no. 7, pp. 1728–1740, Apr. 2017.

      [10] S. H. Park, O. Simeone, O. Sahin, and S. Shamai, “Multihop backhaul compression for the uplink of cloud radio access networks,” IEEE Trans.Veh. Technol., vol. 65, no. 5, pp. 3185–3199, May 2016.

      [11] Y. Du and G. de Veciana, “Wireless Networks without Edges: Dynamic Radio Resource Clustering and User Scheduling,” in Proc. IEEE Conference on Computer Communications (INFOCOM), April 2014.

      [12] K. Sundaresan, M. Y. Arslan, S. Singh, S. Rangarajan, and S. V. Krishnamurthy, “FluidNet: A Flexible Cloud-based Radio Access Network for Small Cells,” in Proc. ACM International Conference on Mobile Computing & Networking, September 2013.

      [13] K. Wang, M. Zhao, and W. Zhou, “Traffic-aware Graph-based Dynamic Frequency Reuse for Heterogeneous Cloud-RAN,” in Proc. IEEE Global Communications Conference (GLOBECOM), December 2014.

      [14] M. Tao, E. Chen, H. Zhou, and W. Yu, “Content-centric sparse multicast beamforming for cache-enabled cloud RAN,” IEEE Trans. Wireless Commun., vol. 15, no. 9, pp. 6118–6131, Sep. 2016.

      [15] J. Yuan, Z. Li, W. Yu, and B. Li, “A cross-layer optimization framework for multihop multicast in wireless mesh networks,” IEEE J. Sel. Areas Commun., vol. 24, no. 11, pp. 2092–2103, Nov. 2006.

      [16] R. Ahlswede, N. Cai, S. Y. R. Li, and R. W. Yeung, “Network information flow,” IEEE Trans. Inf. Theory, vol. 46, no. 4, pp. 1204–1216, Jul. 2000.

      [17] Z. Li, B. Li, D. Jiang, and L. C. Lau, “On achieving optimal throughput with network coding,” in Proc. IEEE INFOCOM, vol. 3, Mar. 2005, pp.2184–2194 vol. 3.

      [18] S. H. Park, O. Simeone, and S. S. Shitz, “Joint optimization of cloud and edge processing for fog radio access networks,” IEEE Trans. Wireless Commun., vol. 15, no. 11, pp. 7621–7632, Nov. 2016.

      [19] Hussein Taleb, Melhem El Helou, Samer Lahoud, Kinda Khawam, and Steven Martin, “An Efficient Heuristic for Joint User Association and RRH Clustering in Cloud Radio Access Networks”, Jun 2018

      [20] Tung T. Vu, Duy T. Ngo, Minh N. Dao, Salman Durrani, Duy H. N. Nguyen and Richard H. Middleton, Energy Efficiency Maximization for Downlink Cloud Radio Access Networks with Data Sharing and Data Compression, May 2018

      [21] Yawen Chen,Xiangming Wen, Zhaoming Lu, Hua Shao, “Energy Efficient Clustering and Beamforming for Cloud Radio Access Networks”, Sep 2016

      [22] Lei Zhang, Atta ul Quddus, Efstathios Katranaras, Dirk Wubben, Yinan Qi, Rahim Tafazolli, “Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells under Cloud RAN”, Apr 2016

      [23] Binbin Dai, and Wei Yu, “Energy Efficiency of Downlink Transmission Strategies for Cloud Radio Access Networks”, Apr. 2016

      [24] Shun-Cheng Zhan, Dusit Niyato, “A Coalition Formation Game for Remote Radio Heads Cooperation in Cloud Radio Access Network”, Apr 2016

      [25] K. Boulos, M. E. Helou, and S. Lahoud, “RRH Clustering in Cloud Radio Access Networks,” in Applied Research in Computer Science and Engineering (ICAR), Oct 2015.




Article ID: 28439
DOI: 10.14419/ijet.v7i4.6.28439

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