5G mobile access targets unprecedented performance, not only in terms of higher data rates per user and lower latency, but also in terms of network intelligence and capillarity. To achieve this, 5G networks will resort to solutions as small cell deployment, multipoint coordination (CoMP, ICIC) and centralized radio access network (C-RAN) with baseband units (BBUs) hotelling. As adopting such techniques requires a high-capacity low-latency access/aggregation network to support backhaul, radio coordination and fronthaul (i.e., digitized baseband signal) traffic, optical access/aggregation networks based on wavelength division multiplexing (WDM) are considered as an outstanding candidate for 5G-transport. By physically separating BBUs from the corresponding cell sites, BBU hotelling promises substantial savings in terms of cost and power consumption. However, this requires to insert additional high bit-rate traffic, i.e., the fronthaul, which also has very strict latency requirements. Therefore, a tradeoff between the number of BBU-hotels (BBU consolidation), the fronthaul latency and network-capacity utilization arises. We introduce the novel BBU-placement optimization problem for C-RAN deployment over a WDM aggregation network and formalize it by integer linear programming. Thus, we evaluate the impact of 1) jointly supporting converged fixed and mobile traffic, 2) different fronthaul-transport options (namely, OTN and Overlay) and 3) joint optimization of BBU and electronic switches placement, on the amount of BBU consolidation achievable on the aggregation network.
Carrying out self-diagnosis of telecommunication networks requires an understanding of the phenomenon of fault propagation on these networks. This understanding makes it possible to acquire relevant knowledge in order to automatically solve the problem of reverse fault propagation. Two main types of methods can be used to understand fault propagation in order to guess or approximate as much as possible the root causes of observed alarms. Expert systems formulate laws or rules that best describe the phenomenon. Artificial intelligence methods consider that a phenomenon is understood if it can be reproduced by modeling. We propose in this paper, a generic probabilistic modeling method which facilitates fault propagation modeling on large-scale telecommunication networks. A Bayesian network (BN) model of fault propagation on GPON-FTTH (Gigabit-capable Passive Optical Network-Fiber To The Home) access network is designed according to the generic model. GPON-FTTH network skills are used to build structure and approximatively determine parameters of the BN model so-called expert BN model of the GPON-FTTH network. This BN model is confronted with reality by carrying out self-diagnosis of real malfunctions encountered on a commercial GPON-FTTH network. Obtained self-diagnosis results are very satisfying and we show how and why these results of the probabilistic model are more consistent with 1 the behaviour of the GPON-FTTH network, and more reasonable on a representative sample of diagnosis cases, than a rule-based expert system. With the main goal to improve diagnostic performances of the BN model, we study and apply EM (Expectation Maximization) algorithm in order to automatically fine-tune parameters of the BN model from real data generated by a commercial GPON-FTTH network. We show that the new BN model with optimized parameters reasonably improves self-diagnosis previously carried out by the expert Bayesian network model of the GPON-FTTH access network.
Abstract-In this paper we study different options for the survivability implementation in MPLS over Optical Transport Networks (OTN) in terms of network resource usage and configuration cost. We investigate two approaches to the survivability deployment: single layer and multilayer survivability and present various methods for spare capacity allocation (SCA) to reroute disrupted traffic.The comparative analysis shows the influence of the offered traffic granularity and the physical network structure on the survivability cost: for high bandwidth LSPs, close to the optical channel capacity, the multilayer survivability outperforms the single layer one, whereas for low bandwidth LSPs the single layer survivability is more cost-efficient. On the other hand, sparse networks of low connectivity parameter use more wavelengths for optical path routing and increase the configuration cost, as compared with dense networks. We demonstrate that by mapping efficiently the spare capacity of the MPLS layer onto the resources of the optical layer one can achieve up to 22% savings in the total configuration cost and up to 37% in the optical layer cost. Further savings (up to 9 %) in the wavelength use can be obtained with the integrated approach to network configuration over the sequential one, however, at the increase in the optimization problem complexity. These results are based on a cost model with different cost variations, and were obtained for networks targeted to a nationwide coverage. Index Terms-MPLS over OTN, multilayer network design, multilayer routing, spare capacity allocation, survivability design.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.