Abstract
The study considers the resource allocation (RAl) for Orthogonal Frequency Division Multiple Access (OFDMA) future cellular network (i.e., Cloud-RAN), where multiple mobile operators can distribute the Cloud-RAN infrastructure as well as network resources possessed by infrastructure providers. We have designed the resource allocation system by solving the dual-coupled problems at two distinct levels (i.e., Upper Level and Lower Level). The first level problem responsible for slicing the front haul capacity (Fcap) and computation of cloud resources for all operators (Op’s). This would indeed tend to increase the overall profits for each Op as well as infrastructure provider by accounting the numerical constraints on Fcap and computational resources. The study introduces a dual-level algorithmic approach to solve this two level RAl problem. At first-level, system considers both Ulevel and Llevel problems by relaxing discrete values with continuous ones. While in the second-level, we introduce two rounding methods to solve the optimal relaxed problems and attain a practical solution for the proposed problem. Finally, simulation results show that the designed algorithms efficiently perform the greedy approach to resource allocation and attain the discrete value very near to the total rate of upper bound acquired by solving resource allocation relaxed problems.
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Cisco Visual Networking Index: Global mobile data traffic forecast update, 2016–2021, Cisco, White Paper, February 2017
Cerwall, P., et al.: Ericsson mobility report, Ericsson, Stockholm, Sweden, Technical report, June 2017
China Mobile Research Institute, “C-RAN: The road towards green RAN,” China Mobile, White Paper (2011)
Checko, A., et al.: Cloud RAN for mobile networks - a technology overview. IEEE Commun. Surv. Tutor. 17(1), 405–426 (2015)
Wubben, D., et al.: Benefits and impact of cloud computing on 5G signal processing: flexible centralization through cloud-RAN. IEEE Signal Process. Mag. 31(6), 35–44 (2014)
Suryaprakash, V., Rost, P., Fettweis, G.: Are heterogeneous cloud-based radio access networks cost-effective? IEEE J. Sel. Areas Commun. 33(10), 2239–2251 (2015)
Costa-Perez, X., Swetina, J., Guo, T., Mahindra, R., Rangarajan, S.: Radio access network virtualization for future mobile carrier networks. IEEE Commun. Mag. 51(7), 27–35 (2013)
Liang, C., Yu, F.R.: Wireless network virtualization: a survey, some research issues, and challenges. IEEE Commun. Surv. Tutor. 17(1), 358–380 (2015)
Peng, M., Wang, C., Lau, V., Poor, H.V.: Fronthaul-constrained cloud radio access networks: insights and challenges. IEEE Wirel. Commun. 22(2), 152–160 (2015)
Luoto, P., Pirinen, P., Bennis, M., Samarakoon, S., Scott, S., Latva-Aho, M.: Co-primary multi-operator resource sharing for small cell networks. IEEE Trans. Wireless Commun. 14(6), 3120–3130 (2015)
Kokku, R., Mahindra, R., Zhang, H., Rangarajan, S.: NVS: a substrate for virtualizing wireless resources in cellular networks. IEEE/ACM Trans. Netw. 20(5), 1333–1346 (2012)
Kamel, M.I., Le, L.B., Girard, A.: LTE wireless network virtualization: dynamic slicing via flexible scheduling. In: Proceedings of the IEEE VTC Fall, pp. 1–5, September 2014
Reddy, C.V., Padmaja, K.V.: Leveraging communication performance for OFDMA using novel bit loading and allocation of power. In: International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp. 1606–1610 (2016)
Wang, X., et al.: Energy-efficient virtual base station formation in optical-access-enabled cloud-RAN. IEEE J. Sel. Areas Commun. 34(5), 1130–1139 (2016)
Luo, S., Zhang, R., Lim, T.J.: Downlink and uplink energy minimization through user association and beamforming in C-RAN. IEEE Trans. Wirel. Commun. 14(1), 494–508 (2015)
Shi, Y., Zhang, J., Letaief, K.B.: Group sparse beamforming for green cloud-RAN. IEEE Trans. Wirel. Commun. 13(5), 2809–2823 (2014)
Ha, V.N., Le, L.B., Ðào, N.-D.: Coordinated multipoint transmission design for cloud-RANs with limited fronthaul capacity constraints. IEEE Trans. Veh. Technol. 65(9), 7432–7447 (2015)
Park, S.-H., Simeone, O., Sahin, O., Shamai (Shitz), S.: Robust layered transmission and compression for distributed uplink reception in cloud radio access networks. IEEE Trans. Veh. Technol. 63(1), 204–216 (2014)
Liu, L., Bi, S., Zhang, R.: Joint power control and fronthaul rate allocation for throughput maximization in OFDMA-based cloud radio access network. IEEE Trans. Commun. 63(11), 4097–4110 (2015)
Rao, X., Lau, V.K.N.: Distributed fronthaul compression and joint signal recovery in cloud-RAN. IEEE Trans. Signal Process. 63(4), 1056–1065 (2015)
Werthmann, T., Grob-Lipski, H., Proebster, M.: Multiplexing gains achieved in pools of baseband computation units in 4G cellular networks. In: Proceedings of the IEEE 24th International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 3328–3333, September 2013
Necoara, I., Patrascu, A.: Iteration complexity analysis of dual first-order methods for conic convex programming. Optim. Methods Softw. 31(3), 645–678 (2016)
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Reddy, C.V., Padmaja, K.V. (2019). Relaxed Greedy-Based Approach for Enhancing of Resource Allocation for Future Cellular Network. In: Silhavy, R. (eds) Cybernetics and Algorithms in Intelligent Systems . CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-91192-2_36
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DOI: https://doi.org/10.1007/978-3-319-91192-2_36
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