Backhaul Transport Network Research Articles

Designing of the Submarine Cable Backhaul Optical Transport Network and the Prediction of OSNR using Artificial Neural Network

In the era of the fast exponential increase of internet traffic, thereby, widespread deployment of IP over Optical Transport Network (OTN) necessitates the designing of a robust and stable backhaul network, so that the Services/Clients don’t experience any blackouts and outages from the International Bandwidth. Because of being a terrestrial backhaul transport network, the performance parameter is mostly the Optical Signal to Noise Ratio(OSNR). In this paper, in the 1st phase, motivated by a live real network, it’s been fully designed the Submarine Cable Backhaul Transport Network from Cable Landing Station(CLS) to Destination with working, protection, and restoration path prioritizing the fidelity of the network. In the 2nd phase, collecting the real-time OSNR time series data from the live circuit, an Artificial Neural Network (ANN) Model has been proposed to predict the OSNR to monitor the performance quality. The Model ANN result shows the network capability for better OSNR assessment and forecasting.

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.

Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.

Experimental demonstration of a distributed ring-based EPON architecture

We experimentally demonstrate error free performance of a simple ring-based Ethernet Passive Optical Network (EPON) architecture that supports truly shared LAN capability among users as well as a fully distributed dynamic bandwidth allocation DBA scheme. The proposed distributed architecture can also be utilized as a packet-based mobile backhaul transport network to backhaul emerging mobile services across the PON-based fiber infrastructure. Utilizing PON access infrastructure as a mobile backhaul enables a seamless migration path to fixed-mobile convergence solutions.