Fig. 1 Optimization strategy

Data-driven network management for 5G systems based on QoE criteriaCarolina Gijón-Martín1 (supervised by Matías Toril2 and Salvador Luna-Ramírez3)

Communications Engineering Department, University of Málaga (Spain)

e-mails: {cgm@ic.uma.es 1, mtoril@ic.uma.es2, sluna@ic.uma.es3}

WORK LINE 1: data-driven traffic steering for optimizing QoE in multi-tier networks [1]

Goal: optimize traffic sharing among carriers.

1) Activation of RSRQ-based inter-frequency handovers.

2) Self-tuning algorithm to adjust handover margins with

QoE criteria.

Method: indicator (∆𝑄𝑜𝐸𝑇(𝑖, 𝑗) ) derived from connection

traces as a driver.

Performance assessment: dynamic system-level simulator

emulating a live 2-tier LTE network with video, FTP, web

browsing and VoLTE services.

Results: proposed algorithm (QBHO+OE) outperforms

classical traffic steering algorithms in terms of QoE.

WORK LINE 2 (ongoing): dimensioning cell capacity in 5G systems with network slicing based on QoE criteria

Basic cell performance models in the absence of QoE measurements.

Models trained with few labeled cases of congestion using month-BH stats.

Prediction of cell performance independent of other cells.

Need for short-term prediction in systems with network slicing.

MOTIVATION & GOALS

1. LEGACY APPROACH

Fig. 2 Performance comparisonFig. 1 Optimization strategy

a) Proposed inter-frequency handover scheme b) Self-tuning algorithm

2. USE CASE

3. PROPOSED APPROACH

WHAT DOES 5G IMPLY?

New services and use cases

New functions and features (e.g., network slicing, network

virtualization, multi-connectivity)

High user expectations

Complex management Self-Organizing Networks (SON)

CURRENT SITUATION

• Need to adapt SON techniques to 5G features

• Need to change network management approach to a

user centric approach (Quality of Experience, QoE)

• Massive data (connection traces, PM, CM) stored but

not exploited

PHD GOAL

Develop self-planning and self-

optimization techniques for cellular

networks based on QoE criteria by

using big data analytics over traces

OTT service provider 1

-Time

- Services

- QoE requirements

OTT service provider 2

-Time

- Services

- QoE requirements

Virtual Network Operator

- Time

- Services

- QoE requirements

Traffic classification

(clustering)

Traffic forecasting

(time series analysis,

supervised learning)

QoE modeling

(MLR, supervised

learning)

Bottleneck detection

(with QoE constraints)

…

PMs

CMs

Traces

Purpose: classify encrypted connections in the radio access network per application

type (e.g., immersive video, IoT, app download…).

Methodology:

1) Feature selection/extraction of relevant traffic descriptors from connection traces.

2) Unsupervised learning (k-means, k-medoids, DBSCAN) over selected descriptors.

3) Check results against typical traffic mix reported by vendors/operators.

More slices

Mobile Network operator (MNO)[2]

Dynamic resource

allocation per slice

Classical dimensioning tool

Dynamic resource

reservation

Cell/slice classification

(clustering)

Proactive QoE-based dimensioning tool

REFERENCES[1] C. Gijón, et al. A data-driven traffic steering algorithm for optimizing user experience in multi-tier LTE networks, IEEE Transactions on Vehicular Technology, vol.68, nº10, pp.9414-9424, 2019.

[2] Da Silva, Icaro, et al. Impact of network slicing on 5G Radio Access Networks. En 2016 European conference on networks and communications (EuCNC), pp. 153-157, 2016.

Work funded by the Spanish Ministry of Science, Innovation and

Universities (RTI2018-099148-B-I00) and the Spanish Ministry of

Education, Culture and Sports (FPU grant FPU17/04286).

Purpose: forecast traffic a per-slice basis in the long-/short-term to avoid

future capacity problems.

Methodology: supervised learning (e.g., SVR, ANN, RF) over historical

performance data collected per slice.

QoE prediction

(time series analysis, supervised learning)

Fig. 3 Long-term cell traffic forecasting exampleFeature

selection/

extraction

(PCA, wrapper,

filtering)

1) Will the network experience capacity problems in the future with existing tenants?

2) Can the network accept new tenants?