Skip to main content

Advertisement

SpringerLink
Log in

Energy Efficient Clustering and Beamforming for Cloud Radio Access Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Cloud radio access network (Cloud-RAN) is recognized as one of the key enabling techniques for 5G due to its advantages in flexibility. In addition, the greatly increased energy efficiency (EE, evaluated by bits/Hz/J) is listed as one main objective when designing 5G wireless network. In this paper, we concentrate on EE optimization through Cloud-RAN enabled flexible multicell cooperative transmission. A joint clustering and beamforming problem for EE optimization is formulated. Due to the combinatorial nature of the clustering process and the non-convexity of the energy efficient beamforming design, the problem is difficult to solve directly. Therefore, we propose a hierarchical iterative framework to solve the problem. The origin optimization problem is decoupled into two subproblems, i.e., energy efficient beamforming problem and energy efficient cluster forming problem. Coalition formation game theory and fractional programming are utilized to obtain the optimal network cluster partition and beamformers respectively. Simulation results demonstrate the superior performance of the proposed algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bhushan N, Li J, Malladi D, Gilmore R, Brenner D, Damnjanovic A, Sukhavasi R, Patel C, Geirhofer S (2014) Network densification: the dominant theme for wireless evolution into 5g. IEEE Commun Mag 52(2):82–89

    Article  Google Scholar 

  2. Zhang H, Chu X, Guo W, Wang S (2015) Coexistence of wi-fi and heterogeneous small cell networks sharing unlicensed spectrum. IEEE Commun Mag 53(3):158–164

    Article  Google Scholar 

  3. Chih-Lin I, Rowell C, Han S, Xu Z, Li G, Pan Z (2014) Toward green and soft: a 5g perspective. IEEE Commun Mag 52(2):66–73

    Article  Google Scholar 

  4. Soret B, Hua W, Pedersen KI, Rosa C (2013) Multicell cooperation for lte-advanced heterogeneous network scenarios. IEEE Wirel Commun 20(1):27–34

    Article  Google Scholar 

  5. Zhang H, Jiang C, Beaulieu NC, Chu X, Wang X, Quek TQS (2015) Resource allocation for cognitive small cell networks: A cooperative bargaining game theoretic approach. IEEE Trans Wirel Commun 14 (6):3481–3493

    Article  Google Scholar 

  6. Somekh O, Simeone O, Bar-Ness Y, Haimovich AM, Shamai S (2009) Cooperative multicell zero-forcing beamforming in cellular downlink channels. IEEE Trans Inf Theory 55(7):3206–3219

    Article  MathSciNet  Google Scholar 

  7. Zhang H, Jiang C, Cheng J, Leung V C M (2015) Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks. IEEE Wirel Commun 22(3):92–99

    Article  Google Scholar 

  8. Pennanen H, Tolli A, Kaleva J, Komulainen P, Latva-aho M (2016) Decentralized linear transceiver design and signaling strategies for sum power minimization in multi-cell mimo systems. IEEE Trans Signal Process 64(7):1729–1743

    Article  MathSciNet  Google Scholar 

  9. Pompili D, Hajisami A, Tran TX (2016) Elastic resource utilization framework for high capacity and energy efficiency in cloud ran. IEEE Commun Mag 54(1):26–32

    Article  Google Scholar 

  10. Zhang H, Jiang C, Beaulieu NC, Chu X, Wen X, Tao M (2014) Resource allocation in spectrum-sharing ofdma femtocells with heterogeneous services. IEEE Trans Commun 62(7):2366–2377

    Article  Google Scholar 

  11. Mahapatra R, Nijsure Y, Kaddoum G, Ul Hassan N, Chau Y (2016) Energy efficiency tradeoff mechanism towards wireless green communication: A survey. IEEE Commun Surv Tutorials 18(1):686–705

    Article  Google Scholar 

  12. Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong ACK, Zhang JC (2014) What will 5g be IEEE J Sel Areas Commun 32(6):1065–1082

    Article  Google Scholar 

  13. Heliot F, Imran MA, Tafazolli R (2011) Energy efficiency analysis of idealized coordinated multi-point communication system. In: 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), pages 1–5

  14. Lozano A, Heath RW, Andrews JG (2013) Fundamental limits of cooperation. IEEE Trans Inf Theory 59(9):5213–5226

    Article  MathSciNet  MATH  Google Scholar 

  15. Baracca P, Boccardi F, Braun V (2012) A dynamic joint clustering scheduling algorithm for downlink comp systems with limited csi. In: 2012 International Symposium on Wireless Communication Systems (ISWCS), pages 830–834

  16. Haijun Z, Hui L, Chunxiao J, Xiaoli C, Nallanathan A, Xiangming W (2015) A practical semidynamic clustering scheme using affinity propagation in cooperative picocells. IEEE Trans Veh Technol 64(9):4372–4377

    Article  Google Scholar 

  17. Saad W, Han Z, Debbah M, Are H, BaÅar T (2009) Coalitional game theory for communication networks. IEEE Signal Process Mag 26(5):77–97

    Article  Google Scholar 

  18. Zengfeng Z, Lingyang S, Zhu H, Saad W (2014) Coalitional games with overlapping coalitions for interference management in small cell networks. IEEE Trans Wirel Commun 13(5):2659–2669

    Article  Google Scholar 

  19. Zhongyuan Z, Mugen P, Zhiguo D, Chonggang W, Poor HV (2015) Cluster formation in cloud-radio access networks: Performance analysis and algorithms design. In: 2015 IEEE International Conference on Communications (ICC), pages 3903–3908

  20. He S, Huang Y, Yang L, Ottersten B, Hong W (2015) Energy efficient coordinated beamforming for multicell system: Duality-based algorithm design and massive mimo transition. IEEE Trans Commun 63(12):4920–4935

    Article  Google Scholar 

  21. Wei X, Yuke C, Hua Z, Ye Li G, Xiaohu Y (2015) Robust beamforming with partial channel state information for energy efficient networks. IEEE J Sel Areas Commun 33(12):2920–2935

    Article  Google Scholar 

  22. Nie W, Zheng FC, Wang X, Zhang W, Jin S (2016) User-centric cross-tier base station clustering and cooperation in heterogeneous networks: Rate improvement and energy saving. IEEE J Sel Areas Commun 34 (5):1192–1206

    Article  Google Scholar 

  23. Garcia V, Yiqing Z, Jinglin S (2014) Coordinated multipoint transmission in dense cellular networks with user-centric adaptive clustering. IEEE Trans Wirel Commun 13(8):4297–4308

    Article  Google Scholar 

  24. Dong L, Qian Z, Shengqian H, Chenyang Y, Gang W, Ming L (2014) Semi-dynamic cooperative cluster selection for downlink coordinated beamforming systems. In: 2014 IEEE Wireless Communications and Networking Conference (WCNC), pages 1194–1199

  25. Liu D, Han S, Yang C, Zhang Q (2015) Semi-dynamic user-specific clustering for downlink cloud radio access network. IEEE Trans Veh Technol PP(99):1–1

    Google Scholar 

  26. Ramamonjison R, Haghnegahdar A, Bhargava VK (2014) Joint optimization of clustering and cooperative beamforming in green cognitive wireless networks. IEEE Trans Wirel Commun 13(2):982–997

    Article  Google Scholar 

  27. Binbin D, Wei Y (2014) Sparse beamforming and user-centric clustering for downlink cloud radio access network. IEEE Access 2:1326–1339

    Article  Google Scholar 

  28. Yuanming S, Jun Z, Letaief KB (2014) Group sparse beamforming for green cloud-ran. IEEE Trans Wirel Commun 13(5):2809–2823

    Article  Google Scholar 

  29. Hong M, Sun R, Baligh H, Luo Z-Q (2013) Joint base station clustering and beamformer design for partial coordinated transmission in heterogeneous networks. IEEE J Sel Areas Commun 31(2):226–240

    Article  Google Scholar 

  30. Yoon M, Kim MS, Lee C (2016) A dynamic cell clustering algorithm for maximization of coordination gain in uplink coordinated system. IEEE Trans Veh Technol 65(3):1752–1760

    Article  Google Scholar 

  31. Rahman MMU, Ghauch H, Imtiaz S, Gross J Rrh clustering and transmit precoding for interference-limited 5g cran downlink. In: 2015 IEEE Globecom Workshops (GC Wkshps), pages 1–7. IEEE, p 2015

  32. Pantisano F, Bennis M, Saad W, Verdone R, Latva-aho M On the dynamic formation of cooperative multipoint transmissions in small cell networks. In: Globecom Workshops (GC Wkshps), 2012 IEEE, pages 1139–1144

  33. Qingjiang S, Razaviyayn M, Zhi-Quan L, Chen H (2011) An iteratively weighted mmse approach to distributed sum-utility maximization for a mimo interfering broadcast channel. IEEE Trans Signal Process 59 (9):4331–4340

    Article  MathSciNet  Google Scholar 

  34. Dinkelbach W (1967) On nonlinear fractional programming. Manag Sci 13(7):492–498

    Article  MathSciNet  MATH  Google Scholar 

  35. Jagannathan R (1966) On some properties of programming problems in parametric form pertaining to fractional programming. Manag Sci 12(7):609–615

    Article  MathSciNet  MATH  Google Scholar 

  36. Hong M, Sun R, Baligh H, Luo Z-Q (2013) Joint base station clustering and beamformer design for partial coordinated transmission in heterogeneous networks. IEEE J Sel Areas Commun 31(2):226–240

    Article  Google Scholar 

  37. Apt KR, Witzel A (2009) A generic approach to coalition formation. International Game Theory Review 11(03):347–367

    Article  MathSciNet  MATH  Google Scholar 

  38. Soret B, Pedersen KI, Jorgensen NTK, Fernandez-Lopez V (2015) Interference coordination for dense wireless networks. IEEE Commun Mag 53(1):102–109

    Article  Google Scholar 

  39. Apt KR, Radzik T (2006) Stable partitions in coalitional games. arXiv preprint cs/0605132

  40. Lo TKY (1999) Maximum ratio transmission. In: 1999 IEEE International Conference on Communications, 1999. ICC ’99, volume 2, pages 1310–1314 vol 2

  41. Ngo HQ, Larsson EG, Marzetta TL (2013) Energy and spectral efficiency of very large multiuser mimo systems. IEEE Trans Commun 61(4):1436–1449

    Article  Google Scholar 

Download references

Acknowledgment

This work is supported by the National Natural Science Foundation of China , No. 61271179, and the Beijing Municipal Science and Technology Commission research fund project “Research on 5G Network Architecture and Its Intelligent Management Technologies”, No. D151100000115002.

Author information

Authors and Affiliations

  1. Beijing Laboratory of Advanced Information Networks, Beijing, China

    Yawen Chen, Xiangming Wen, Zhaoming Lu & Hua Shao

  2. Beijing Advanced Innovation Center for Future Internet Technology, Beijing, China

    Yawen Chen, Xiangming Wen, Zhaoming Lu & Hua Shao

  3. Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing, China

    Yawen Chen, Xiangming Wen, Zhaoming Lu & Hua Shao

Authors
  1. Yawen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangming Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaoming Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yawen Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Wen, X., Lu, Z. et al. Energy Efficient Clustering and Beamforming for Cloud Radio Access Networks. Mobile Netw Appl 22, 589–601 (2017). https://doi.org/10.1007/s11036-016-0768-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-016-0768-7

Keywords

Search

Navigation