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Wireless Networks
Erschienen in: Wireless Networks 4/2020

22.05.2019

Joint QoS-control and handover optimization in backhaul aware SDN-based LTE networks

verfasst von: Furqan Hameed Khan, Marius Portmann

Erschienen in: Wireless Networks | Ausgabe 4/2020

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Abstract

Future cellular networks will be dense and require key traffic management technologies for fine-grained network control. The problem gets more complicated in the presence of different network segments with bottleneck links limiting the desired quality of service (QoS) delivery to the last mile user. In this work, we first design a framework for software-defined cellular networks and then propose new mechanisms for management of QoS and non-QoS users traffic considering both access and backhaul networks, jointly. The overall SDN-LTE system and related approaches are developed and tested using network simulator in different network environments. Especially, when the users are non-uniformly distributed, the results shows that compared to other approaches, the proposed load distribution algorithm enables at least 6% and 23% increase in the average QoS user downlink throughput and the aggregate throughput of 40% users with lowest throughput (edge users), respectively. Also, the proposed system efficiently achieves desired QoS and handles the network congestion without incurring significant overhead.
Vorheriger Artikel Resource allocation in future HetRAT networks: a general framework
Nächster Artikel Study of single-cell massive MIMO systems with channel aging and prediction

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Fußnoten
1
\({\bar{\rho }}_{GBR} (t)\) is the average network load that reflects the channel metric.
 
2
e.g. eNodeB 1 in Scenario 1, and eNodeB 4 in Scenario 2 and 3.
 
3
Since multiple controllers will be distributed across the network infrastructure with each controller located close enough to the BS resulting in even lower latency.
 
Literatur
1.
Tall, A., Altman, Z., & Altman, E. (2015). Self-optimizing load balancing with backhaul-constrained radio access networks. IEEE Wireless Communication Letters, 4(6), 645.CrossRef
2.
Zaki, Y., Weerawardane, T., Hauth, S., Wallmeier, E., & Gorg, C. (2013). Intelligent traffic enforcement for LTE backhaul. In Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, IEEE PIMRC’13, 8–11 September 2013, London, UK.
3.
Philip, V. D., & Zeghlache, D. (2012). Demystifying link congestion in 4G-LTE backhaul using openflow. In 5th International Conference on New Technologies, Mobility and Security (NTMS), IEEE NTMS’12, 7–10 May 2012, Istanbul, Turkey.
4.
Kuklinski, S., Li, Y., & Dinh, K. T. (2014). Handover management in SDN-based mobile networks. IEEE Globecomm’14, in Globecomm 2014 Workshop, 8–12 December 2014 (pp. 194–200).
5.
Nguyen, V.-G., & Kim, Y. (2015). Proposal and evaluation of SDN-based mobile packet core networks. EURASIP Journal on Wireless Communications and Networks, 1, 1–18.
6.
Wang, D., Katranaras, E., Quddus, A., Sapountzis, N., Cominardi, L., Kuo, F.-C., et al. (2015). SDN-based joint backhaul and access design for efficient network layer operations. In IEEE European Conference on Networks and Communications, IEEE EuCNC (pp. 214–218).
7.
8.
Chaves, L. J., Garcia, I. C., & Madeira, E. R. M. (2016). OpenFlow-based mechanism for QoS in LTE backhaul networks. In 25th IEEE Symposium on Computers and Communication (ISCC), IEEE ISCC’16, 27–30 June 2016, Messina, Italy.
9.
Khan, F. H., & Portmann, M. (2017). Backhaul, QoS, and channel-aware load balancing optimization in SDN-based LTE networks. In 11th International Conference on Signal Processing and Communication Systems (ICSPCS’17), Gold Coast, 13–15 December 2017, Australia.
10.
Kim, H., d-Veciana, G., Yang, X., & Venkatachalam, M. (2012). Distributed alpha-optimal user association and cell load balancing in wireless networks. IEEE/ACM Transactions on Networking, 20(1), 177.CrossRef
11.
Wang, H., Ding, L., Wu, P., Pan, Z., Liu, N., & You, X. (2011). QoS-aware load balancing in 3GPP long term evolution multi-cell networks. In IEEE International Conference on Communications (ICC), IEEE ICC’11, 5–9 July 2011, Kyoto, Japan.
12.
Zhihang, L., Hao, W., Zhiwen, P., Nan, L., & Xiaohu, Y. (2013). QoS and channel state aware load balancing in 3GPP LTE multi-cell networks. In H. Mei (Ed.), Science China, Information Sciences. Science China Press and Springer, Berlin.
13.
Huang, M., Feng, S., & Chen, J. (2013). Dynamic association for load balancing in LTE multi-cell networks. In IEEE International Conference on Mechatronics Sciences, Electric Engineering and Computer (MEC), in IEEE MEC, 20–22 December 2013, Shenyang, China.
14.
Alexandris, K., Sapountzis, N., Nikaein, N., & Spyropoulos, T. (2016). Load-aware handover decision algorithm in next-generation HetNets. In Wireless Communication and Network Conference (WCNC’16), IEEE Wireless Communications and Networking Conference, 3–6 April 2016, Doha, Qatar.
15.
Son, K., Chong, S., & d-Veciana, G. (2009). Dynamic association for load balancing and interference avoidance in multi-cell networks. IEEE Transactions on Wireless Communications, 8(7), 3566–3575.CrossRef
16.
Wang, Z., Schoenen, R., Yanikomeroglu, H., & St-Hilaire, M. (2015). Load balancing in cellular networks with user-in-the-loop: A spatial traffic shaping approach. In IEEE ICC 2015, IEEE ICC’15.
17.
Li, Y., Li, M., Bin, C., Yong, W., & Liu, W. (2013). Dynamic optimization of handover parameters adjustment for conflict avoidance in long term evolution. IEEE International Conference on China Communications, 10(1), 56–71.
18.
Szilagyi, P., Vincze, Z., & Vulkan, C. (2012). Enhanced mobility load balancing optimisation in LTE. In Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, IEEE PIMRC’12, 9–12 September 2012, Sydney, Australia.
19.
Elgendi, I., Munasinghe, K. S., & Jamalipour, A. (2016). A three-tier SDN based distributed mobility management architecture for DenseNets. In 2016 IEEE ICC Mobile and Wireless Networking Symposium, Proceedings of IEEE ICC’16.
20.
Chou, G.-T., Liu, K.-H., & Su, S.-L. (2015). Load-based cell association for load balancing in heterogeneous cellular networks. In 2015 IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications—(PIMRC): Mobile and Wireless Networks, IEEE PIMRC’15, 30 August–02 September, Hong Kong, China (pp. 1681–1686).
21.
Li, Y., Cao, B., & Wang, C. (2016). Handover schemes in heterogeneous LTE networks: Challenges and opportunities. IEEE Wireless Communications, 23(2), 112–117.CrossRef
22.
Cao, B., He, F., Li, Y., Wang, C., & Lang, W. (2015). Software defined virtual wireless network: Framework and challenges. IEEE Networks, 29(4), 6–12.CrossRef
23.
Rony, R. I., Jain, A., L.-Aguilera, E., G.-Villegas, E.,& Demirkol, I. (2017). Joint access-backhaul perspective on mobility management in 5G networks. In IEEE Conference on Standards for Communications and Networking (CSCN), IEEE CSCN, 18–20 September 2017.
24.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–656.
25.
Jain, R., Chiu, D. M., & Hawe, W. (1984). A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. DEC Research Report TR-301.
26.
Bojovic, B., & Baldo, N. (2014). A new channel and QoS aware scheduler to enhance the capacity of voice over LTE systems. In 11th International Multi-Conference on Systems, Signals, and Devices (SSD’14), Castelldefels, 11–14 February 2014, Spain.
27.
28.
Hahn, S., Rose, D. M., & Kurner, T. (2014). Mobility load balancing a case study: Simplified vs. realistic scenarios. Euro-Cost.
29.
Kadatskaya, O., & Saburova, S. (2014) Research of requirements to QoS for voice over LTE. In First International Scientific-Practical Conference (pp. 135–139).
30.
31.
3GPP TR 36.881 v 14.0.0. Study on latency reduction techniques for LTE.
32.
Sieber, C., Durner, R., & Kellerer, W. (2017). How fast can you reconfigure your partially deployed SDN network? In Proceedings of International IFIP TC6 Networking Conference 2017 (pp. 1–9).
33.
He, K., Khalid, J., G.-Jacobson, A., Das, S., & Prakash, C. (2015). Measuring control plane latency in SDN-enabled switches. In ACM Symposium on SDN Research (SOSR), 17–18 June 2015, Santa Clara, CA, USA.
34.
He, K., Khalid, J., Das, S., Akella, A. Li, E. L., & Thottan, M. (2014). Mazu: Taming latency in software defined networks. In Wisconsin-Madison Technical Report.
35.
Chen, H., & Benson, T. (2017). Hermes: Providing tight control over high-performance SDN switches. In CoNext.
36.
Khalili, R., Despotovic, Z., & Hecker, A. (2018). Flow setup latency in SDN networks. IEEE Journal on Selected Areas in Communication (JSAC), 36, 2631.CrossRef
37.
Nguyen, T. D., Chiesa, M.,& Canini, M. (2017). Decentralized Consistent Updates in SDN. In Symposium of SDN Research (SOSR)’17, 3–4 April 2017, USA.
38.
Wu, K.-R., Liang, J.-M., Lee, S.-C., & Tseng, Y.-C. (2018). Efficient and consistent flow updates for software defined networks. IEEE Journal on Selected Areas in Communication, 36(3), 411.CrossRef
39.
Metadaten
Titel
Joint QoS-control and handover optimization in backhaul aware SDN-based LTE networks
verfasst von
Furqan Hameed Khan
Marius Portmann
Publikationsdatum
22.05.2019
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 4/2020
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-019-02021-7

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