Radio Resource Management Techniques for 5G Verticals
S.M. Ahsan Kazmi
Institute of Information Security and Cyber Physical Systems, Innopolis University, Innopolis, Russia
Search for more papers by this authorTri Nguyen Dang
Department of Computer Science and Engineering, Kyung Hee University, Seoul, Korea
Search for more papers by this authorNguyen H. Tran
School of Computer Science, The University of Sydney, Sydney, NSW, Australia
Search for more papers by this authorMehdi Bennis
Department of Communications, University of Oulu, Oulu, Finland
Search for more papers by this authorChoong Seon Hong
Department of Computer Science and Engineering, Kyung Hee University, Seoul, Korea
Search for more papers by this authorS.M. Ahsan Kazmi
Institute of Information Security and Cyber Physical Systems, Innopolis University, Innopolis, Russia
Search for more papers by this authorTri Nguyen Dang
Department of Computer Science and Engineering, Kyung Hee University, Seoul, Korea
Search for more papers by this authorNguyen H. Tran
School of Computer Science, The University of Sydney, Sydney, NSW, Australia
Search for more papers by this authorMehdi Bennis
Department of Communications, University of Oulu, Oulu, Finland
Search for more papers by this authorChoong Seon Hong
Department of Computer Science and Engineering, Kyung Hee University, Seoul, Korea
Search for more papers by this authorSummary
A cellular communication network can broadly be categorized into two parts, the core network and the radio access network (RAN). This chapter focuses on the latter part of cellular communication network, i.e. RAN. It briefly presents 5G specifications along with the enabling technologies that will support bringing the 5G networks to fruition. Then the chapter discusses different types of resources available for the RAN of future cellular systems that need to be managed for effective operations. It also discusses network slicing which can be considered as a promising scheme to meet the heterogeneous requirements produced by various 5G verticals. The chapter further provides a use-case that focuses on one of the most important requirements of virtual reality (VR), i.e. offloading computation task. Offloading intensive tasks to more resourceful devices such as clouds or mobile edge computing servers increases the computational capacity of VR devices.
References
-
Hong, C.S., Ahsan Kazmi, S.M., Moon, S. et al. (2016). SDN based wireless heterogeneous network management. AETA 2015: Recent Advances in Electrical Engineering and Related Sciences, pp. 3–12. Springer.
10.1007/978-3-319-27247-4_1 Google Scholar
- Raza, H. A brief survey of radio access network backhaul evolution: part i. IEEE Communications Magazine 49 (6): 164–171. https://doi.org/10.1109/MCOM.2011.5784002.
- Osseiran, A. (2014). Mobile and wireless communications system for 2020 and beyond (5G). https://www.metis2020.com/wp-content/uploads/presentations/ITU-R-2020-VisionWS.pdf.
- Andrews, J.G., Buzzi, S., Choi, W. et al. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications 32 (6): 1065–1082.
-
Ahsan Kazmi, S.M., Tran, N.H., Ho, T.M. et al. (2016). Decentralized spectrum allocation in D2D underlying cellular networks. 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–6. IEEE.
10.1109/APNOMS.2016.7737199 Google Scholar
- Kazmi, S.M.A., Tran, N.H., Saad, W. et al. (2017). Mode selection and resource allocation in device-to-device communications: a matching game approach. IEEE Transactions on Mobile Computing 16 (11): 3126–3141.
- Ullah, S., Thar, K., and Hong, C.S. (2017). Cache decision for scalable video streaming in information centric networks. Multimedia Tools and Applications 76 (20): 21519–21546.
-
Ahsan Kazmi, S.M., Tran, N.H., Ho, T.M. et al. (2015). Resource management in dense heterogeneous networks. Network Operations and Management Symposium (APNOMS), 2015 17th Asia-Pacific, pp. 440–443. IEEE.
10.1109/APNOMS.2015.7275383 Google Scholar
- Olwal, T.O., Djouani, K., and Kurien, A.M. (2016). A survey of resource management toward 5G radio access networks. IEEE Communications Surveys Tutorials 18 (3): 1656–1686. https://doi.org/10.1109/COMST.2016.2550765.
- Rappaport, T.S., Xing, Y., MacCartney, G.R. et al. Overview of millimeter wave communications for fifth-generation (5G) wireless networkswith a focus on propagation models. IEEE Transactions on Antennas and Propagation 65 (12): 6213–6230. https://doi.org/10.1109/TAP.2017.2734243.
- Ho, T.M., Tran, N.H., Ahsan Kazmi, S.M. et al. (2016). Distributed resource allocation for interference management and QoS guarantee in underlay cognitive femtocell networks. 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4. IEEE.
- Ahsan Kazmi, S.M., Tran, N.H., Saad, W. et al. (2016). Optimized resource management in heterogeneous wireless networks. IEEE Communications Letters 20 (7): 1397–1400. https://doi.org/10.1109/LCOMM.2016.2527653.
- Adedoyin, M.A. and Falowo, O.E. (2017). QoS-based radio resource management for 5G ultra-dense heterogeneous networks. 2017 European Conference on Networks and Communications (EuCNC), pp. 1–6. doi: 10.1109/EuCNC.2017.7980721.
- Visual Networking Index Cisco (2016). Global mobile data traffic forecast update, 2015–2020 White Paper. Document ID 958959758.
- Li, L., Zhao, G., and Blum, R.S. (2018). A survey of caching techniques in cellular networks: research issues and challenges in content placement and delivery strategies. IEEE Communications Surveys Tutorials 20 (3): 1710–1732. https://doi.org/10.1109/COMST.2018.2820021.
- Yang, C., Yao, Y., Chen, Z., and Xia, B. (2016). Analysis on cache-enabled wireless heterogeneous networks. IEEE Transactions on Wireless Communications 15 (1): 131–145. https://doi.org/10.1109/TWC.2015.2468220.
- Ji, J., Zhu, K., Ran, W. et al. (2018). Energy efficient caching in backhaul-aware cellular networks with dynamic content popularity. Wireless Communications and Mobile Computing 2018: 1–12.
- Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing 8 (4): 14–23. https://doi.org/10.1109/MPRV.2009.82.
- Cisco (2015). Fog computing and the Internet of Things: Extend the cloud to where the things are. White Paper.
- Beck, M.T., Werner, M., Feld, S. et al. (2014). Mobile edge computing: A taxonomy. Proceedings of the Sixth International Conference on Advances in Future Internet, pp. 48–55. Citeseer.
- Mao, Y., Zhang, J., and Letaief, K.B. (2016). Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications 34 (12): 3590–3605. https://doi.org/10.1109/JSAC.2016.2611964.
- Messous, M., Sedjelmaci, H., Houari, N. et al. (2017). Computation offloading game for an UAV network in mobile edge computing. 2017 IEEE International Conference on Communications (ICC), pp. 1–6. doi: 10.1109/ICC.2017.7996483.
- Kazmi, S.M.A., Tran, N.H., Ho, T.M., and Hong, C.S. (2018). Hierarchical matching game for service selection and resource purchasing in wireless network virtualization. IEEE Communications Letters 22 (1): 121–124.
- Ahsan Kazmi, S.M. and Hong, C.S. (2017). A matching game approach for resource allocation in wireless network virtualization. Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, p. 113. ACM.
-
Kim, D.H., Kazmi, S.M., and Hong, C.S. (2018). Cooperative slice allocation for virtualized wireless network: A matching game approach. Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication, p. 94. ACM.
10.1145/3164541.3164565 Google Scholar
- Sciancalepore, V., Zanzi, L., Costa-Perez, X. et al. (2018). ONETS: Online network slice broker from theory to practice. arXiv preprint arXiv:1801.03484.
- Richart, M., Baliosian, J., Serrat, J., and Gorricho, J.-L. (2016). Resource slicing in virtual wireless networks: a survey. IEEE Transactions on Network and Service Management 13 (3): 462–476.
- Foukas, X., Patounas, G., Elmokashfi, A., and Marina, M.K. (2017). Network slicing in 5G: survey and challenges. IEEE Communications Magazine 55 (5): 94–100.
- Zhang, H., Liu, N., Chu, X. et al. (2017). Network slicing based 5G and future mobile networks: mobility, resource management, and challenges. IEEE Communications Magazine 55 (8): 138–145.
- Ordonez-Lucena, J., Ameigeiras, P., Lopez, D. et al. (2017). Network slicing for 5G with SDN/NFV: concepts, architectures and challenges. arXiv preprint arXiv:1703.04676.
-
Ho, T.M., Tran, N.H., Ahsan Kazmi, S.M. et al. (2018). Wireless network virtualization with non-orthogonal multiple access. NOMS 2018–2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–9. IEEE.
10.1109/NOMS.2018.8406264 Google Scholar
- Vo, P.L., Nguyen, M.N.H., Le, T.A., and Tran, N.H. (2018). Slicing the edge: Resource allocation for RAN network slicing. IEEE Wireless Communications Letters 7 (6): 970–973.
- Bastug, E., Bennis, M., Médard, M., and Debbah, M. (2017). Toward interconnected virtual reality: opportunities, challenges, and enablers. IEEE Communications Magazine 55 (6): 110–117.
- Boyd, S., Parikh, N., Chu, E. et al. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3 (1): 1–122. https://doi.org/10.1561/2200000016.
Further Reading
- Lin, T., Ma, S., and Zhang, S. (2015). On the global linear convergence of the ADMM with multiblock variables. SIAM Journal on Optimization 25 (3): 1478–1497.
- Manzoor, A., Tran, N.H., Saad, W. et al. (2019). Ruin theory for dynamic spectrum allocation in LTE-U networks. IEEE Communications Letters 23 (2): 366–369.
- Nishihara, R., Lessard, L., Recht, B. et al. (2015). A general analysis of the convergence of ADMM. arXiv preprint arXiv:1502.02009.
- Shen, C., Chang, T.-H., Wang, K.-Y. et al. (2012). Distributed robust multicell coordinated beamforming with imperfect CSI: an ADMM approach. IEEE Transactions on Signal Processing 60 (6): 2988–3003.
- Wang, C., Liang, C., Richard Yu, F. et al. (2017). Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Transactions on Wireless Communications 16 (8): 4924–4938.