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11 - Interference management, mobility management, and dynamic reconfiguration

Published online by Cambridge University Press:  05 June 2016

Michał Maternia
Affiliation:
Nokia
Ömer Bulakci
Affiliation:
Huawei
Emmanuel Ternon
Affiliation:
NTT DOCOMO
Andreas Klein
Affiliation:
University of Kaiserslautern
Tommy Svensson
Affiliation:
Chalmers University of Technology
Afif Osseiran
Affiliation:
Ericsson
Jose F. Monserrat
Affiliation:
Universitat Politècnica de València
Patrick Marsch
Affiliation:
Nokia
Mischa Dohler
Affiliation:
King's College London
Takehiro Nakamura
Affiliation:
NTT DoCoMo Inc.
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Summary

This chapter covers network-level solutions aiming at enhancing end-user experience and bringing down the Operational Expenditures (OPEX) of the 5G deployments.

After analyzing contemporary trends and predictions for 5G, it becomes evident that there are certain aspects of 5G deployments that have to be taken into consideration when designing future network-level solutions. These aspects are predominantly related to the network densification and increasing heterogeneity. While network densification will result in much smaller distances between the Base Stations (BSs) [1], the heterogeneity of 5G will manifest itself in multiple dimensions e.g. cell types or operating frequency. [2]. This complicated heterogeneous environment, apart from dimensioning and planning problems, brings also opportunities for an efficient mapping of users or services to optimal (from the overall system perspective) access technologies or BS types. Aspects related to efficient signaling exchange in this heterogeneous environment (also in scenarios with control/user plane decoupling) are related to a Lean System Control Plane design (cf. Chapter 2).

Based on the previously mentioned factors, it can be easily inferred that one of the most desired 5G features is broadly understood flexibility. The flexibility can be achieved in different ways, starting from a flexible allocation of radio resources in Time Division Duplexing (TDD) mode according to instantaneous traffic conditions, through an efficient exploitation of different Radio Access Technologies (RATs) or network layers, and finally as a freedom to dynamically reconfigure the network using BSs that are automatically configured and self-backhauled. Besides, the kind and extent of Moving Networks (MNs) expected to be introduced in 5G can be perceived as a dynamic reconfiguration enabler and an important part of the Dynamic Radio Access Network (RAN) described in Chapter 2.

The performance of the 5G system can be further enhanced by exploiting context information understood as “(…) any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and application themselves” [3]. Today, there are already many application domains and envisioned use cases where devices (such as sensors) or services (such as cloud services) communicate for unconscious support of people in their everyday life tasks.

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Publisher: Cambridge University Press
Print publication year: 2016

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References

[1] Bhushan, N., Junyi, Li, Malladi, D. et al., “Network densification: The dominant theme for wireless evolution into 5G,” IEEE Communication Magazine, vol. 52, no. 2, February 2014.Google Scholar
[2] ICT-317669 METIS project, “Final report on the METIS system concept and technology roadmap,” Deliverable D6.6, April 2015, www.metis2020.com/documents/deliverables/
[3] Dey, A. K., “Providing Architectural Support for Building Context-Aware Applications,” PhD Thesis, College of Computing, Georgia Institute of Technology, December 2000.
[4] Holsopple, J., Sudit, M., Nusinov, M., Liu, D. F. and Du, H.. Yang, S. J., “Enhancing situation awareness via automated situation assessment,” IEEE Communications Magazine, vol. 48, no. 3, pp. 146–152, March 2010.Google Scholar
[5] Janneteau, C., Simoes, J., Antoniou, J. et al., “Context-aware multiparty networking,” in ICT-Mobile Summit, Santander, April 2009, pp. 1–11.
[6] Bellavista, P., Corradi, A., and Giannelli, C., “Mobility-aware connectivity for seamless multimedia delivery in the heterogeneous wireless internet,” in IEEE Symposium on Computers and Communications, Santiago, July 2007.
[7] Antoniou, J., Pinto, F., Simoes, J., and Pitsillides, A., “Supporting context-aware multiparty sessions in heterogeneous mobile networks,” Mobile Network and Applications, vol. 15, no. 6, pp. 831–844, December 2010.Google Scholar
[8] Lungaro, P., Segall, Z., and Zander, J., “Predictive and context-aware multimedia content delivery for future cellular networks,” in IEEE Vehicular Technology Conference, Taipei, May 2010, pp. 1–5.
[9] Bellavista, P., Corradi, A., and Giannelli, C., “Mobility-aware management of internet connectivity in always best served wireless scenarios,” Mobile Networks and Applications, vol. 14, no. 1, pp. 18–34, February 2009.Google Scholar
[10] Makris, P., Skoutas, D. N., and Skianis, C., “A survey on context-aware mobile and wireless networking: On networking and computing environments’ integration,” IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 362–386, April 2012.Google Scholar
[11] Pantisano, F., Bennis, M., Saad, W., Valentin, S., and Debbah, M., “Matching with externalities for context-aware cell association in wireless small cell networks,” in IEEE Global Communications Conference, Atlanta, December 2013, pp. 4483–4488.
[12] Proebster, M., Kaschub, M., Werthmann, T., and Valentin, S., “Context-aware resource allocation to improve the quality of service of heterogeneous traffic,” in IEEE International Conference on Communications, Kyoto, June 2011, pp. 1–6.
[13] Tanghe, E., Joseph, W., Verloock, L., and Martens, L., “Evaluation of vehicle penetration loss at wireless communication frequencies,” IEEE Transactions on Vehicular Technology, vol. 57, pp. 2036–2041, July 2008.Google Scholar
[14] Sui, Y., Papadogiannis, A., and Svensson, T., “The potential of moving relays: A performance analysis,” in IEEE Vehicular Technology Conference, Yokohama, May 2012, pp. 1–5.
[15] Sui, Y., Papadogiannis, A., Yang, W., and Svensson, T., “Performance comparison of fixed and moving relays under co-channel interference,” in IEEE Global Communication Conference Workshops, Anaheim, December 2012, pp. 574–579.
[16] Ellenbeck, J., Hartmann, C., and Berlemann, L., “Decentralized inter-cell interference coordination by autonomous spectral reuse decisions,” in European Wireless Conference, Prague, June 2008, pp. 1–7.
[17] Garica, L., Costa, G. O, Cattoni, A., Pedersen, K., and Mogensen, P., “Self-organising coalitions for conflict evaluation and resolution in femtocells,” in IEEE Global Telecommunications Conference, Miami, December 2010, pp 1–6.
[18] Zhang, L., Yang, L., and Yang, T., “Cognitive interference management for LTE-A femtocells with distributed carrier selection,” in IEEE Vehicular Technology Conference Fall, Ottawa, September 2010, pp. 1–5.
[19] 3GPP TR 36.828, “Further enhancements to LTE Time Division Duplex (TDD) for Downlink-Uplink (DL-UL) interference management and traffic adaptation (Release 11),” Technical Report, TR 36.828, V11.0.0, Technical Specification Group Radio Access Network, June 2012.
[20] 3GPP TS 36.423, “X2 application protocol (X2AP) (Release 12),” Technical Specification, TS 36.423, V12.5.0, Technical Specification Group Radio Access Network, March 2015.
[21] Lahetkanges, E., Pajukoski, K., Vihriala, J. et al., “On the flexible 5G dense deployment air interface for mobility broadband,” in International Conference on 5G for Ubiquitous Connectivity, Akaslompolo, November 2014, pp. 57–61.
[22] ICT-317669 METIS project, “Final report on the network-level solutions,” Deliverable D4.3, March 2015, www.metis2020.com/documents/deliverables/
[23] Venkatasubramanian, V., Hesse, M., Marsch, P., and Maternia, M., “On the performance gain of flexible UL/DL TDD with centralized and decentralized resource allocation in dense 5G deployments,” in IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications, Washington, September 2014.
[24] ICT-317669 METIS project, “Proposed solutions for new radio access,” Deliverable D2.4, March 2015, www.metis2020.com/documents/deliverables/
[25] Sroka, P. and Kliks, A., “Distributed interference mitigation in two-tier wireless networks using correlated equilibrium and regret-matching learning,” in European Conference on Networks and Communications, Bologna, June 2014, pp. 1–5.
[26] Holakouei, R. and Marsch, P., “Proactive delay-minimizing scheduling for 5G Ultra Dense Deployments,” in IEEE Vehicular Technology Conference, Boston, September 2015.
[27] Abou-zeid, H., Hassanein, H. S., and Valentin, S., “Optimal predictive resource allocation: Exploiting mobility patterns and radio maps,” in IEEE Global Communications Conference, Atlanta, December 2013, pp. 4877–4882.
[28] Sui, Y., Vihriala, J., Papadogiannis, A., Sternad, M., Wei, Y., and Svensson, T., “Moving cells: A promising solution to boost performance for vehicular users,” IEEE Communications Magazine, vol. 51, no. 6, pp. 62–68, June 2013.Google Scholar
[29] Sui, Y., Guvenc, I., and Svensson, T. “Interference Management for Moving Networks in Ultra-Dense Urban Scenarios,” EURASIP Journal on Wireless Communications and Networking, Special Issue on 5G Wireless Mobile Technologies, April 2015.
[30] Jeong, M. R. and Miki, N., “A simple scheduling restriction scheme for interference coordinated networks,” IEICE Transactions on Communications, vol. E96-B, no. 6, pp. 1306–1317, June 2013.Google Scholar
[31] Sesia, S., Toufik, I., and Baker, M., LTE – The UMTS Long Term Evolution: From Theory to Practice, ed. West Sussex: John Wiley & Sons Ltd., 2011.
[32] Sternad, M., Grieger, M., Apelfrojd, R., Svensson, T., Aronsson, D., and Martinez, A. Belen, “Using ‘predictor antennas’ for long-range prediction of fast fading for moving relays,” in IEEE Wireless Communications and Networking Conference, Paris, April 2012, pp. 253–257.
[33] Jamaly, N., Apelfrojd, R., Martinez, A. Belen, Grieger, M., Svensson, T., Sternad, M., and Fettweis, G., “Analysis and measurement of multiple antenna systems for fading channel prediction in moving relays,” in European Conference on Antennas and Propagation, The Hague, April 2014.
[34] Thuy, D., Sternad, M., and Svensson, T., “Adaptive large MISO downlink with predictor antenna array for very fast moving vehicles,” in International Conference on Connected Vehicles and Exp, Las Vegas, December 2013, pp. 331–336.
[35] Thuy, D., Sternad, M., and Svensson, T., “Making 5G adaptive antennas work for very fast moving vehicles,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 2, 2015.Google Scholar
[36] Phan, V. V., Horneman, K., Yu, L., and Vihriala, J., “Providing enhanced cellular coverage in public transportation with smart relay systems,” in IEEE Vehicular Network Conference, Jersey City, December 2010, pp. 301–308.
[37] Fernandes, S. and Karmouch, A., “Vertical mobility management architectures in wireless networks: A comprehensive survey and future directions,” IEEE Communication Surveys Tutorials, vol. 14, no. 1, pp. 45–63, September 2012.Google Scholar
[38] Cananea, I., Mariz, D., Kelner, J., Sadok, D., and Fodor, G., “An on-line access selection algorithm for ABC networks supporting elastic services,” in IEEE Wireless Communications and Networking Conference, Las Vegas, April 2008, pp. 2033–2038.
[39] Fodor, G., Eriksson, A., and Tuoriniemi, A., “Providing QoS in always best connected networks,” IEEE Communications Magazine, vol. 41, no. 7, pp. 154–163, July 2003.Google Scholar
[40] Gustafson, E. and Jonsson, A., “Always best connected,” IEEE Wireless Communications Magazine, vol. 10, no. 1, pp. 49–55, February 2003.Google Scholar
[41] Yilmaz, O. N. C., Li, Z., Valkealahti, K., Uusitalo, M.A., Moisio, M., Lundén, P., and Wijting, C., “Smart mobility management for D2D communications in 5G networks,” in IEEE Wireless Communications and Networking Conference Workshops, Istanbul, April 2014, pp. 219–223.
[42] 3GPP TS 36.331, “Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification (Release 12),” Technical Specification, TS 36.331 V12.5.0, Technical Specification Group Radio Access Network, March 2015.
[43] Sui, Y., Ren, Z., Sun, W., Svensson, T., and Fertl, P., “Performance study of fixed and moving relays for vehicular users with multi-cell handover under co-channel interference,” in International Conference on Connected Vehicles and Expo, Las Vegas, December 2013, pp. 514–520.
[44] Next Generation Mobile Networks (NGMN) Alliance, “NGMN Use Cases related to Self Organising Network, Overall Description,” Technical Report, May 2007, www.ngmn.org/uploads/media/NGMN_Use_Cases_related_to_Self_Organising_Network__Overall_Description.pdf
[45] Jansen, T., Balan, I., Turk, J., Moerman, I., and Kürner, T., “Handover parameter optimization in LTE self-organizing networks,” in IEEE Vehicular Technology Conference, Ottawa, September 2010, pp. 1–5.
[46] Awada, A., Wegmann, B., Rose, D., Viering, I., and Klein, A., “Towards self-organizing mobility robustness optimization in Inter-RAT scenario,” in IEEE Vehicular Technology Conference, Budapest, May 2011, pp. 1–5.
[47] Legg, P., Hui, G., and Johansson, J., “A simulation study of LTE intra-frequency handover performance,” in IEEE Vehicular Technology Conference, September 2010, Ottawa, pp. 1–5.
[48] Luna-Ramirez, S., Ruiz, F., Toril, M., and Fernandez-Navarro, M., “Inter-system handover parameter auto-tuning in a joint-RRM scenario,” in IEEE Vehicular Technology Conference, Singapore, May 2008, pp. 2641–2645.
[49] Luna-Ramirez, S., Toril, M., Ruiz, F., and Fernandez-Navarro, M., “Adjustment of a Fuzzy Logic Controller for IS-HO parameters in a heterogeneous scenario,” in IEEE Mediterranean Electrotechnical Conference, Ajaccio, 2008, pp. 29–34.
[50] Nasri, R, Altman, Z., and Dubreil, H., “Fuzzy-Q-learning-based autonomic management of macro-diversity algorithm in UMTS networks,” Annals of Telecommunications, vol. 61, no. 9–10, 2006, pp. 1119–1135.Google Scholar
[51] Nawrocki, M. J., Aghvami, H., and Dohler, M., Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice. Chichester: John Wiley & Sons, 2006.
[52] Nasri, R., Altman, Z., and Dubreil, H., “Optimal tradeoff between RT and NRT services in 3G-CDMA networks using dynamic fuzzy Q-learning,” in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, 2006, pp. 1–5.
[53] Feng, Z., Liang, L., Tan, L., and Zhang, P., “Q-learning based heterogeneous network self-optimization for reconfigurable network with CPC assistance,” Science in China Series F: Information Sciences, vol. 52, no. 12, December 2009, pp. 2360–2368.Google Scholar
[54] Munoz, P., Barco, R., Bandera, I. de la, Toril, M., and Luna-Ramírez, S., “Optimization of a fuzzy logic controller for handover-based load balancing,” in IEEE Vehicular Technology Conference, Budapest, May 2011, pp. 1–5.
[55] Klein, A., “Context Awareness for Enhancing Heterogeneous Access Management and Self-Optimizing Networks,” PhD thesis at University of Kaiserslautern, ISBN 978-3-8439-2030-8, 2015.
[56] Webb, M. et al., “Smart 2020: Enabling the low carbon economy in the information age,” in IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, London, 2008.
[57] Evolved Universal Terrestrial Radio Access (E-UTRA); Study on energy saving enhancement for E-UTRAN, 3GPP TSG RAN, TR 36.887, V12.0.0, June 2014.
[58] NGMN-Alliance, “5G white paper – Executive version,” NGMN 5G Initiative, Technical Report, December 2014, www.ngmn.org/uploads/media/141222
[59] Ishii, H., Kishiyama, Y., and Takahashi, H., “A novel architecture for LTE-B: C-plane/U-plane split and Phantom Cell concept,” in IEEE Globecom Workshops, Anaheim 2012, pp. 624–630
[60] Views on Small Cell On/Off Mechanisms, 3GPP TSG RAN, R1-133456, August 2013.
[61] Ternon, E., Agyapong, P., Hu, L., and Dekorsy, A., “Database-aided energy savings in next generation dual connectivity heterogeneous networks,” in IEEE Wireless Communications and Networking Conference, Istanbul, 2014, pp. 2811–2816.
[62] Ternon, E., Agyapong, P., Hu, L., and Dekorsy, A., “Energy savings in heterogeneous networks with clustered small cell deployments,” in IEEE Wireless Communications Systems, Barcelona, 2014, pp. 126–130.
[63] Osseiran, A., Boccardi, F., Braun, V. et al., “Scenarios for 5G mobile and wireless communications: The vision of the METIS project,” IEEE Communications Magazine, vol. 52, no. 5, pp. 26–35, May 2014.Google Scholar
[64] Bulakci, O., Ren, Z., Zhou, C. et al., “Towards flexible network deployment in 5G: Nomadic node enhancement to heterogeneous networks,” in IEEE International Conference on Communications, London, June 2015.
[65] Bulakci, O., Ren, Z., Zhou, C. et al., “Dynamic nomadic node selection for performance enhancement in composite fading/shadowing environments,” in IEEE Vehicular Technology Conference, Seoul, 2014, pp. 1–5.
[66] Sui, Y., Papadogiannis, A., Yang, W., and Svensson, T., “The energy efficiency potential of moving and fixed relays for vehicular users,” in IEEE Vehicular Technology Conference, Las Vegas, 2013, pp. 1–7.
[67] Li, J., Björnson, E., Svensson, T., Eriksson, T., and Debbah, M., “Optimal design of energy-efficient HetNets: Joint precoding and load balancing,” in IEEE International Conference on Communications, London, June 2015.
[68] Li, J., Björnson, E., Svensson, T., Eriksson, T., and Debbah, M., “Joint precoding and load balancing optimization for energy-efficient heterogeneous networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 10, pp. 5810–5822, October 2015.Google Scholar

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