- Open Access
Towards user-centric operation in 5G networks
© Monserrat et al. 2016
- Received: 4 June 2015
- Accepted: 20 December 2015
- Published: 7 January 2016
There are three pillars that characterize the new 5G revolution, namely, the use of heterogeneous wireless access technologies conforming an ultra-dense network, the software-driven flexibility of this network, and the simplified and user-centric operation and management of the system. This next-generation network operation and management shall be based on the usage of Big Data Analytics techniques to monitor the end-user quality of experience through direct measures of the network. This paper describes the Astellia approach towards this network revolution and presents some results on the performance of quality estimation techniques in current cellular networks. Thanks to the use of this approach, operators may fill the gap of knowledge between network key performance indicators and user experience. This way, they can operate in a proactive manner and have actual measurements of the users’ experience, which leads to a fairer judgement of the users’ complaints.
- Network management
The arrival of the 5G is expected by the year 2020. Meanwhile, various organizations and projects around the world are embarked on the process of describing the main characteristics based on the society to come in the next decade . However, there is consensus in the three main characteristics of the 5G . Firstly, 5G will be a heterogeneous network in which several radio interfaces could be applied to different use cases. This network is expected to be from three to five times more dense, in terms of number of access nodes, than current deployments, i.e., long-term evolution (LTE) Release 8, which poses severe challenges in the proper interference management . Secondly, 5G networks must be highly flexible and adapt to the dynamism of the traffic location and patterns. For this, radio access network (RAN) functionalities may run in large computer centers, able to dynamically assign more or less units of computation to the virtual cells distributed in the network. In this context, a virtual cell grabs the needed resources from a common pool and uses a set of remote radio head (RRH) units to configure the optimum cell to serve the user . As a result, 5G may rely on this new concept of cloud RAN that combines collaborative radio and the real-time cloud infrastructure with a centralized general-purpose processing solution . Finally, operation and management of this 5G network must be simplified, including a new approach in which all focus is on the user satisfaction . This requires advanced data analytics and the use of Big Data techniques to monitor the users’ quality of experience (QoE) by using network metrics that combine network and behavioral data .
The Nokia Siemens Networks 2013 Acquisition and Retention Study showed that in average almost 40 % of users are likely to churn within a year timeframe, being loyalty lower in mature markets . It is relevant to see that service quality is becoming one of the key drivers for such consideration , something that did not happen in the past, when motivation was mainly to replace the mobile device. In consequence, service operation and management needs to be redefined to put customers at the heart of daily operations. In this sense, operators need a complete and real-time picture of service behavior and performance for each individual customer, in order to propose customers appropriate and tailor-made contracts. This has proven efficient to increase significantly users’ satisfaction and subsequent fidelity.
Given the increasing complexity of mobile generation, the natural trend in system operation and management is to automate these processes, for which KPIs are the main inputs for decision-making. KPIs are aggregate metrics that measure the average behavior of the different network elements and help operators define and evaluate how successful the network is. KPIs, however, are not capturing the actual perception of customers, as evidenced by the fact that, in a recurrent way, operators see that network KPIs are very satisfying but customer service is facing an increasing number of subscriber complaints experiencing poor quality of experience. Moreover, KPIs are different for each radio access technology and cannot be combined in cases where the user is camping on different technologies along the session. Therefore, better models are needed to capture the relationship between QoE and measurable network service and context parameters, with a single measure of the user experience that is independent of the radio technology.
Big data and data analytics have an important role to play in this new paradigm of system operation and management . First, user location must be known in real time, which represents the analysis of enormous amount of data, including the receive power levels from several cells of millions of users and/or their GPS positions. Moreover, in order to derive the user QoE simultaneously, it is required to correlate this location with other context data, including bearer allocation, traffic pattern, day, hour, social behavior, contract details, etc.
A classical computational approach is unable to manage such a huge volume, velocity, and variety of data. Therefore, Big Data solutions together with Advanced Analytics must be put in place to transform these complex data into easily manageable data units.
Astellia together with the Universitat Politècnica de València has developed SatiX, a powerful customer-centric monitoring solution that correlates radio network metrics and end-user perception information. Customer experience metrics such as blocked calls, dropped calls, coverage issues, voice, and data quality are correlated with technical radio measurements such as received pilot power, signal-to-noise ratio (SNR), throughput, BLER, and duration of the connection. It is worth noting that the tool operates with temporal and hence anonymous identifiers. Therefore, there are not privacy issues in the management of such information. Of course, the operator can easily translate this temporal identifier to obtain the actual identity of the customer, being these data subject to applicable regulations in terms of data security and privacy management.
The result is an advanced customer satisfaction index that not only provides objective customer perception indicators but also identifies radio parameters to adjust network conditions and enhance customer experience.
At user level a series of magnitudes extracted from call traces are aggregated in order to calculate different metrics, e.g., the experimented average SNR in a dedicated channel or the voice call drop rate. These metrics are the starting point of the SatiX process calculation.
3.1 Calculation of metrics
List of metrics used in UMTS
Mean value of the best server cell
From −109 to −70 dBm
Mean value of the best server cell
From −17 to −6 dB
Number of calls that end in another technology
From 0 to 100 %
Percentage of drops in relation with the total calls in a day
From 0 to 10 %
Number of total blocks in a day
From 0 to 10
Considering all the session duration
From 50 kbps to 2 Mbps
Considering all the session duration
From 8 to 250 kbps
Average data connection reliability given by the data drop rate and data block rate
(%Data Blocks) + (%Data Drops) (1−(%Data Blocks))
Level 5 if −85 ≤ RSCP
Level 4 if −93 ≤ RSCP < −85 dBm
Level 3 if −100 ≤ RSCP < −93 dBm
Level 2 if −109 ≤ RSCP < −100 dBm
Level 1 if RSCP < −109 dBm
3.2 Calculation of attributes
List of attributes used in UMTS
Reflects the ability of maintaining a successful connected voice call
Means the ability of successfully initiating a voice call while being in a coverage zone
User coverage per cell and day
Average voice quality in voice calls
Quality experimented in a data calls
As an example, the level of the coverage attribute is calculated as the average of RSCP and Ec/N0 metrics, whereas data quality takes into account, throughput in both directions, reliability and handover rate. These attributes are to be properly weighted in an offline calibration process.
3.3 SatiX results
The optimum weights for the components of the SatiX attributes were calculated based on customer’s surveys on a measurement campaign performed during 3 months, with more than 2000 phone surveys. With the collected data, we minimized the mean squared error of the difference between the SatiX value and the actual surveys, provided that the summation of weights equals 1, and being all weights from 0 to 1. This convex problem was solved using the interior point method .
A different solution that permits a non-linear combination of attributes consists in defining a neural network that uses attributes as inputs and provides SatiX as output. Some trials have shown that a three hidden layer neural network performs well.
Once the calculations are finalized, there are available data about metrics, attributes and SatiX for each user, which is complemented with other available data from other sources, like zones or administrative/political levels linked to each cell in order to obtain results associated to geographical data. Users’ expectations are also taken into account to bias the obtained technical SatiX value. The source information to derive these rules is multiple, ranging from the application layer information, the tariff, users mobility, and the variability of the calculated metrics. As outcome, different types of results are generated: dissatisfaction rankings, SatiX/attributes/metrics maps at cell and administrative area level, temporal evolution graphs showing how satisfaction, or metrics evolved after a certain update in the network, etc.
The calculation of correlation between SatiX results and the polls is made using three different methods. The first one is based on setting a level from which it is considered that the survey has been positive or negative. For this method, there is a correlation of 77 or 78 % when neglecting promoters and detractors. The second method uses a scale from 1 to 5 and assumes that both surveys and SatiX fit if difference is 0 or 1. In this case, correlation is 73 %. If we extract the detractors and promoters, the level of success increases considerably up to 83 %. The last correlation method takes into account a scale from 1 to 10 assuming a hit if the level is the same or differs only in 1 level. In this case, the success rate is 60 %, being 72 % without detractors and promoters.
This paper has described a new approach for the definition of KPI, which fits better with the future needs of the 5G in terms of network operation and maintenance.
In the future, some aspects need to be revisited. In particular, the generation of SatiX results through the aggregation of information contained in data records should be studied carefully from the averaging perspective. Depending on the aggregation level, the averaging should be different. This could lead to heavy queries with very long execution times, which requires the new approaches of Big Data.
Finally, current approach is still vendor-specific, since different vendors have intrinsic differences when comparing their metrics. This could generate differences in the SatiX calculations depending on the specific hardware, which should be avoided. To solve this esthetical problem, a separate calibration for each vendor shall be done.
The authors would like to thank the funding received from the Ministerio de Industria, Energía y Turismo TSI-100102-2013-106 funds.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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