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Netmanias Interview with SK Telecom at MWC 2015: Big data-based intelligent operation platforms - Fast Data Platform
April 14, 2015 | By Dr. Michelle M. Do and Dr. Harrison J. Son (tech@netmanias.com)
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For the rest of the Q2 2015 issue of Korea Communication Review magazine please click here

 

 

SK Telecom at the Mobile World Congress (MWC) 2015 demonstrated its newest network operation platform for 5G called “Fast Data Platform”, using two big data-based intelligent operation platforms: T-Packet Analytics & Network Intelligence (T-PANI) and Analytics Platform for Intelligent Operation (APOLLO).
 
The company regards "Big Data + Real-time Analytics" as the two key words of Fast Data. The new platform not only efficiently processes “big data” constantly generated in the network, but also analyzes and optimizes them in real time, addressing service degradation experienced by users fast. So, it is expected to allow for user-level optimization. 

 

Conventional network operation and management

 

Figure 1. Conventional network operation and management platform - cell optimization based on O&M data 

 

Conventionally, network operation and management have been focused on optimizing performance of base stations. Network optimization has been performed at cell level, by using vendor-provided statistics of each cell. When O&M data is generated from base stations, a vendor-provided EMS collects it, processes it into statistics for individual cells, and sends them to the centralized Self-Organizing Network (cSON) server. Then the cSON server uses them in optimizing the network (EMS is not shown in the figure for simplified illustration).


Data process is delayed while O&M statistics is being generated. Because of such delay, the cSON server can perform network optimization not in real time, but only regularly or as scheduled. Optimization in the conventional way is performed at 'cell level', which makes 'user-level' QoE management difficult. Besides, operators cannot identify any QoE problems, which a user might be facing, in time until the user calls the operator's customer service complaining about it. So, technically real-time QoE management is also impossible, let alone user-level optimization.

 

SK Telecom has been operating cSON, since its commercialization in 2012. Mostly, cSON is used in neighbor optimization and power control, and other cSON features, though not very popular now, are expected to be used more often in the near future. 

 

Network operation and management for 5G: Fast Data Platform
 

Figure 2. Fast Data Platform: optimized customization based on real time analytics of big data 

 

In contrast to the conventional platform, Fast Data Platform allows for 'user and service-oriented' network operation and management, and network optimization as well. Network optimization using the new platform is three-fold, and includes: i) big data collection, ii) analysis of the data by T-PANI and APOLLO, and user/service-oriented optimization, and iii) enforcement of optimization through cSON.


Big data can be collected from UE, RAN (base stations), and core network. At UE, all events that are occurring at UE are collected through an application called "DIAG on Device (DoD)" installed on the UE. At base stations, cell-level data, signaling data exchanged between base station and UE, and instantaneous variations (e.g. radio measurements at UE) are collected. And finally at core network, data relating to user bearer/service are collected.


For example, RRC messages exchanged between base stations and UE alone are more than tens of TB every day. And this massive data have been left unused so far. The beauty of the new platform is that, it can analyze this big data to identify abnormality and problems, decide what to optimize (e.g. service, user, cell, etc.), and perform optimization on them. And then improvement measures based on the optimization results are enforced to base stations through cSON.
 
Even for users at the same cell, at the same location or route, optimization is customized for each user depending on their subscriber class, or the service subscribed. This means, network resources are more efficiently used, in a way that can improve QoE of more users.  

 

The optimization procedure by Fast Data Platform can be summarized as follows:

 

1) Big data collection 
The platform collects big data from UE, RAN, and core network. Just to name a few, big data includes:


•    Data collected from core network: bearer/service data

      -  Network performance info: link utilization, call drop ratio

      -  Call attempts, successful calls and usage index per application 
•    Data collected from RAN (base stations) 
      -  Base station/Cell information: eNB configuration info, Resource status info, Interference info, Handover report info, Mobility info, Fault status
      -  Signaling data exchanged between base station and UE: RRC messages for connection establishment and handover
      -  Radio measurements at UE: RSRP, RSRQ, CQI
•    Data collected from UE 
      -  Call events: out of service, dropped calls (HD voice) 
      -  Mobility events: handover failure, cell reselection failure 


At UE, 'DoD' app is installed, and it reports all call/mobility events occurring as well as quality of received radio signals at the UE. According to a presenter from SK Telecom at the event, as this application, running in the background, requires very little power, it has a minimal impact on service quality on the user side, and also is most effective in problem analytics. Because DoD became available only on the relatively recent models like Galaxy S5, G3, etc., DoD data has been gathered from not many users, yet. But, as more and more devices come with DoD these days, user-level optimization will become more convenient and efficient in years to come.

 

2) Real-time analytics and optimization:  T-PANI and APOLLO 
T-PANI and APOLLO analyze the collected big data in real time, identify problems to be handled, decides what to optimize, and finally perform optimization.

 

T-PANI: T-PANI manages networks, customers and services systematically. It measures Customer Experience Index (CEI), monitors app/service status, and analyzes CEI and app/service status for each service and region in real time. In case of CEI degradation or service failure, any service quality problem or system fault can be identified within less than one minute from an E2E perspective. T-PANI then provides CE and service status information to APOLLO so that it can perform user-level optimization. T-PANI consists of following modules: 


•    Network Topology: supports network operation
•    Application Service: responds to external threats through monitoring and analyzing service status
•    Customer Experience Management (CEM): analyzes CEI and manages quality of customer experience  

      -  CEI per service (e.g. Data, HD Voice, CSFB) 

      -  Nationwide CEI map

      -  Bad CE level in regions

      -  Customers with bad CE

 

Figure 3. T-PANI --> Screenshot of T-PANI's CM module

 

APOLLO: APOLLO collects and analyzes base station-originated raw big data in real time, and optimizes the performance of each base station (or cell) and user. It processes raw data to obtain statistics every 10 seconds, and automatically detects abnormality based on history log, allowing for real-time optimization as well as user-level optimization utilizing QoE information. And it, capable of predicting traffic variation and base station performance, helps to minimize degradation in base station and user performance. Followings are some features of APOLLO and their intended effects:  


•    real-time interference monitoring → automatic interference avoidance
•    detecting abnormality in fronthaul and radio unit → automatic recovery
•    real-time analytics of call flow and radio environment → optimization of call processing parameters

 

Figure 4. APOLLO --> Screenshot of APOLLO's display

 

3) Optimization Provisioning and Enforcement

Once APOLLO determines what to optimize through problem analysis, and completes optimization, improvement measures based on the optimization results are enforced at base stations through cSON. cSON offers the interfaces  between SK Telecom's Fast Data Platform and base stations. When optimization provisioning by APOLLO is completed, cSON enforces it into base stations to adjust the operation parameters running on the stations, so that the stations can operate with the newly adjusted parameters from then on.

 

 
       
   

The greatest strength of Fast Data Platform is that it can finally take advantage of quality measurement data provided by UE (DoD app. to be accurate), which has been unobtainable in the conventional network operation and management system.

 

To us, DoD application works as a radio network quality measurement tool added to UE, just like Speedtest, Chariot, XCAL, etc. installed on UE. It enables operators to collect i) quality of received radio signals, ii) quality of download speeds, and iii) quality of services (e.g. VoLTE) at individual UEs in real time.

 

The size of cells is getting smaller, and the number of base stations is increasing. As these trends continue, measuring the operation quality of increasing base stations using its current manpower and measurement equipment will impose a greater burden of operating cost on SK Telecom. Besides, as more and more small cells are working indoors, measuring radio quality indoors will be another challenge for operators.

 

Given the circumstance, DoD can be a great solution for this challenge. With DoD application, the company will be able to obtain radio network quality data from DoD UEs everywhere (Just imagine there are 20 million DoD UEs across the nation, all constantly collecting DoD data).

 

DoD application will help the company to not only improve users' QoE through user-level optimization, but also remotely detect any faults in the company's radio network in real time, by making the most of the data collected by UEs, allowing for precision management of the network.

 

The presenter from the company noted, "After the Fast Data Platform deployment in our network, time required for detecting and handling faults at cell sites has been drastically reduced."

 
       

 

 

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