Abstract
In this paper we consider a cloud radio access network (C-RAN) where the remote radio heads (RRHs) are separated from the baseband signal processing servers, named baseband units (BBUs). The latter forms a centralized pool of high-performance data center resources. To benefit from network function virtualization, we consider virtualized BBU (V-BBU) resources where the BBU functionality and services have been virtualized in the form of virtual network functions. All RRHs in the C-RAN form a single cluster. Each RRH of such a cluster may accommodate random or quasi-random traffic. That means that new calls in a RRH can be generated by an infinite number of mobile users (random traffic) or by a finite number of mobile users (quasi-random traffic). An arriving call requires a radio resource unit from the serving RRH and a computational resource unit from the V-BBU. If these resource units are available, then the call is accepted and remains in the system for a generally distributed service time. Otherwise, the call is blocked and lost. In order to analyze this C-RAN we model it as a loss system and study two cases: (i) all RRHs accommodate quasi-random traffic and (ii) some RRHs accommodate random traffic and the rest accommodate quasi-random traffic. In both cases, we show that the steady state probabilities have a product form solution and propose convolution algorithms for the accurate determination of the main teletraffic performance measure which is congestion probability. The accuracy of these algorithms is verified via simulation.
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References
Checko, A., et al. (2015). Cloud RAN for mobile networks—A technology overview. IEEE Communications Surveys and Tutorials, 17(1), 405–426. 1st Quart.
Kazi, B., & Wainer, G. (2019). Next generation wireless cellular networks: Ultra dense multi-tier and multi-cell cooperation perspective. Wireless Networks, 25(4), 2041–2064.
CPRI Consortium. (2015). CPRI specification V7.0 common public radio interface (CPRI); interface specification.
Mukhlif, F., Noordin, K., Mansoor, A., & Kasirun, Z. (2019). Green transmission of C-RAN based on SWIPT in 5G: A review. Wireless Networks, 25(5), 2621–2649.
Network Function Virtualisation (NFV). (2014). Management and orchestration, document ETSI GS NFV-MAN 001 (V1.1.1), December 2014.
Chen, T., Matinmikko, M., Chen, X., Zhou, X., & Ahokangas, P. (2015). Software defined mobile networks: Concept, survey, and research directions. IEEE Communications Magazine, 53(11), 126–133.
Stasiak, M., Glabowski, M., Wisniewski, A., & Zwierzykowski, P. (2011). Modeling and dimensioning of mobile networks. Hoboken, NJ: Wiley.
Glabowski, M., Hanczewski, M., & Stasiak, M. (2016). Modelling load balancing mechanisms in self-optimizing 4G mobile networks with elastic and adaptive traffic. IEICE Transactions on Communications, E99–B(8), 1718–1726.
Casares-Giner, V., Martinez-Bauset, J., & Ge, X. (2018). Performance model for two-tier mobile wireless networks with macrocells and small cells. Wireless Networks, 24(4), 1327–1342.
Vassilakis, V., Moscholios, I., & Logothetis, M. (2018). Quality of service differentiation in heterogeneous CDMA networks. Wireless Networks, 24(4), 1279–1295.
Moscholios, I., & Logothetis, M. (2019). Efficient multirate teletraffic loss models beyond Erlang. Hoboken: Wiley.
Wang, L., & Zhou, S. (2017). On the fronthaul statistical multiplexing gain. IEEE Commun. Letters, 21(5), 1099–1102.
Larsen, L., Checko, A., & Christiansen, H. (2019). A survey of the functional splits proposed for 5G mobile crosshaul networks. IEEE Commun. Surveys Tuts, 21(1), 146–172. 1st Quart.
Dai, B., & Yu, W. (2016). Energy efficiency of downlink transmission strategies for cloud radio access networks. IEEE Journal on Selected Areas in Communications, 34(4), 1037–1050.
Li, Y., Jiang, T., Luo, K., & Mao, S. (2017). Green heterogeneous cloud radio access networks: Potential techniques, performance trade-offs, and challenges. IEEE Communications Magazine, 55(11), 33–39.
Ahmad, I., et al. (2018). Overview of 5G security challenges and solutions. IEEE Communications Standards Magazine, 2(1), 36–43.
Liu, J., Zhou, S., Gong, J., Niu, Z., & Xu, S. (2014). On the statistical multiplexing gain of virtual base station pools. In Proceedings of IEEE Globecom (pp. 2283–2288), Austin, TX, USA.
Avramova, A., Christiansen, H., & Iversen, V. (2015). Cell deployment optimization for cloud radio access network using teletraffic theory. In: Proceedings of advanced international conference on telecommunications (AICT), Brussels, Belgium.
Checko, A., Avramova, A., Burger, M., & Christiansen, H. (2016). Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings. Journal of Communications and Networks, 18(2), 162–172.
Iversen, V., Benetis, V., & Hansen, P. (2004). Performance of hierarchical cellular networks with overlapping cells. In Proceedings of EuroNGI workshop, Wadern, Germany.
Moscholios, I., Vassilakis, V., Logothetis, M., & Boucouvalas, A. (2017). State-dependent bandwidth sharing policies for wireless multirate loss networks. IEEE Transactions on Wireless Communications, 16(8), 5481–5497.
Liu, J., Zhou, S., Gong, J., Niu, Z., & Xu, S. (2016). Statistical multiplexing gain analysis of heterogeneous virtual base station pools in cloud radio access networks. IEEE Transactions on Wireless Communications, 15(8), 5681–5694.
Han, C., Wang, W., Wang, Y., & Zhang, Z. (2017). Computational resource constrained multi-cell joint processing in cloud radio access networks. In Proceedings of IEEE ICC, Paris, France.
Wang, K., & Yang, K. (2016). Power-minimization computing resource allocation in mobile cloud-radio access network. In Proceedings of international conference on computer and information technology (CIT), Nadi, Fiji.
Ramakrishnan, S., Kar, S., & Selvamuthu, D. (2019). Analysis of energy efficiency in cloud based heterogeneous RAN with large-scale antenna systems. Computer Networks, 149, 265–276.
Fakhri, Z., Khan, M., Sabir, F., & Al-Raweshidy, H. (2018). A resource allocation mechanism for cloud radio access network based on cell differentiation and integration concept. IEEE Transactions on Network Science and Engineering, 5(4), 261–275.
Ben Ali, K., & Zarai, F. (2019). Adaptive radio resource management scheme in 5G networks support for IoT applications. In Proceedings of international conference on internet of things: Systems, management and security (IOTSMS), Granada, Spain.
Simscript III. Retrieved February, 2020 from http://www.simscript.com/.
Moscholios, I., & Logothetis, M. (2010). The Erlang multirate loss model with batched Poisson arrival processes under the bandwidth reservation policy. Computer Communications, 33(supplement 1), S167–S179.
Moscholios, I., Vardakas, J., Logothetis, M., & Boucouvalas, A. (2013). Congestion probabilities in a batched Poisson multirate loss model supporting elastic and adaptive traffic. Annals of Telecommunications, 68(5), 327–344.
Ezhilchelvan, P., & Mitrani, I. (2017). Multi-class resource sharing with batch arrivals and complete blocking. In Proceedings of international conference on quantitative evaluation of systems (QEST), Lecture Notes in Computer Science, (Vol. 10503), Springer.
Moscholios, I., Vassilakis, V., & Sarigiannidis, P. (2018). Performance modelling of a multirate loss system with batched Poisson arrivals under a probabilistic threshold policy. IET Networks, 7(4), 242–247.
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Chousainov, IA., Moscholios, I., Kaloxylos, A. et al. Performance evaluation of a C-RAN supporting a mixture of random and quasi-random traffic. Wireless Netw 26, 3953–3965 (2020). https://doi.org/10.1007/s11276-020-02301-7
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DOI: https://doi.org/10.1007/s11276-020-02301-7