Revenue Analytics for Long Term Evolution (LTE) - Technical White Paper October, 2012
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Revenue Analytics for Long
Term Evolution (LTE)
Technical White Paper
October, 2012
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cVidya Confidential Proprietary
This document and the information contained in it is CONFIDENTIAL INFORMATION of cVidya,
and shall not be used, published, disclosed, or disseminated outside cVidya in whole or in part
without cVidya's consent. This document contains trade secrets of cVidya. Reverse
engineering of any or all of the information in this document is prohibited. The copyright
notice does not imply publication of the document.
Documented Releases
Revision Number Revision Description Revision Date
1.0 Initial release Date–month‐year
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Contents
1 Revenue Management challenges .................................................................. 7
1.1 Service Configuration ................................................................................................. 7
1.2 Usage ......................................................................................................................... 8
1.3 Billing......................................................................................................................... 9
2 Revenue management solution .................................................................... 10
2.1 Revenue Assurance .................................................................................................. 10
2.2 Fraud Management .................................................................................................. 11
2.3 Revenue Management Control Points ...................................................................... 12
3 About cVidya ................................................................................................ 13
Table of Figures
Figure 1 – LTE Phase 1 .................................................................................................. 6
Figure 2 – LTE Phase 2 & 3 ............................................................................................ 6
Figure 3 – LTE Charging................................................................................................. 8
Figure 4 – LTE Control Points ...................................................................................... 12
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The introduction of LTE
The access technology called LTE (Long Term Evolution) is quickly becoming the network
technology of choice for 4G deployments around the world as the consumer demand for
higher capacity mobile broad band services continues to rise. LTE is becoming the technology
of choice because it provides cost‐effective, highly responsive and very fast mobile data
services.
The 4G LTE technology revolutionizes mobile network architecture and the services offered for
mobile users. LTE lays the foundations for an all‐IP environment enabling unified internet
based interactions of User’s Equipment (UE) with a growing number of high bandwidth
offerings. Traditional circuit based voice services can be fully assimilated with the packet data
infrastructure thus simplifying access technologies and providing richer integrated voice and
data services.
LTE technology brings changes to the Radio Access Network (RAN) as well as to the network
core, moving it from the dual circuit and packet cores architecture, to a unified Evolved Packet
Core (CPE) that serves voice, media and data. LTE provides access to emerging IP Multimedia
Subsystem (IMS) networks, which will eventually replace the traditional circuit based mobile
voice networks of today with rich multimedia services.
In most cases, 3G operators are deploying LTE in a phased process (see figures below):
Phase 1
Deploy LTE along with the existing 3.xG network; gradually move the traditional 3G data
core (BSC, SGSN, GGSN) to the Evolved Packet Core (EPC)
Phase 2
Deploy IP Multimedia Subsystem (IMS) along with the existing 3.xG circuit based voice
network; gradually move the traditional 3G Voice core (BSC, MSC, HLR) to IMS with EPC
Phase 3
Full EPC + IMS network and service environments
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3.x G Network Circuit Core
SMSC Voice & Messaging
MSC
SMSC
BTS BSC/
RNC Intelligent
HLR
NodeB Network
Radio Access Network
SGSN GGSN
Packet Core
Internet
4G ‐ LTE Network
MME HSS
e‐NodeB
S‐GW P‐GW
Radio Access Network
Evolved Packet Core RCRF
SPR
Figure 1 – LTE Phase 1
4G ‐ LTE Network
IP Multimedia
Subsystem
Application
Servers Voice &
Messaging
HSS CSCF
MME
MGW
e‐NodeB
S‐GW
P‐GW
Radio Access Network
Evolved Packet Core
RCRF
SPR
Internet
Figure 2 – LTE Phase 2 & 3
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1 Revenue Management challenges
The transition to LTE affects service types, utilization patterns, and also alters the services’ and
customers’ information. Furthermore, traffic patterns, especially for data, are changed
significantly. LTE enables operators to move from traditional flat charge based data usage to
more advanced group bundled & quality dependant charges, thus increasing revenues and
differentiation. On the other hand, as with the internet, the all‐IP LTE technology increases the
level of vulnerability to fraudsters and hackers.
cVidya’s MoneyMap and FraudView solutions address the challenges arising from the
introduction of LTE and IMS in two ways. Firstly, from the overall perspective of Revenue
Assurance and Fraud management, they address the new configuration, usage patterns and
vulnerabilities which LTE brings with it, and secondly, they continue to address the relevant
traditional RA and Fraud issues from the pre‐LTE era.
The following paragraphs outline some of the major Revenue Assurance and Fraud
Management challenges that LTE raises.
1.1 Service Configuration
Customers and Service information in 3G networks is usually stored by and managed across
the Home Location Register (HLR) and the Prepaid Intelligent Network platforms. Some
aspects of 3G data usage are managed by Radius/AAA platforms.
In LTE networks the HLR that manages customers’ service information, is replaced by the
Home Subscribers System (HSS), which is a combination of the HLR and AAA, and responsible
for managing the overall data services for the customers. Furthermore, LTE supports Quality of
Service (QoS) dependent charging, thus the level of quality of service (bandwidth, guarantied
bit rate, etc.) depends on parameters provisioned for each customer. The LTE QoS parameters
per customers are stored by the Service Profile Register (SPR) platform. The QoS is actually
managed by the Policy Charge Rules Function (PCRF) and the Policy Charge Enforcement
Function (PCEF) controlling the Packet Data Network Gateway (P‐GW). It is imperative that the
SPR be synchronized with the HLR and HSS (see figure 3).
During phase 1 of the LTE deployment, the operators are managing customers’ information
concurrently across the HLR, HSS and SPR. Customers having 3G data are managed on the HLR
while those moved to LTE reside on the HSS and SPR, while their voice services are still
managed by the HLR. It is of the utmost importance that the data integrity across the HLR, HSS
and SPR for all the registered customers and their services, be maintained. To maintain data
integrity, it is essential that duplications and discrepancies be resolved during the overall
transit period from phase 1 to 3. It is important to note that LTE service and topology
attributes, as well as customers’ information, impact the information handled by the CRM,
Billing and ERP (accounting & logistics) systems.
Prepaid customers on 3G networks are also managed by a Prepaid Intelligent Network (IN)
platform. Revenue management for 3G maintains integrity by avoiding duplications and
discrepancies between the HLR and the IN platforms. The transition to LTE introduces the On‐
Line Charging (OCG) platform which receives usage information, coupled with utilized QoS
from the PCEF. For data usage, the OCG initially interacts with the Prepaid IN platform, to
determine available credit and report utilized credit. Eventually (phase 3), the Prepaid IN
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platform will be completely assimilated into the OCG. The PCEF also reports usage of postpaid
customers (and visitors) to the Off‐Line Charging system (retail billing).
During phase 1, the OCG functionality can be carried out by the existing prepaid platforms (IN
and Prepaid charging gateway). On the other hand, the new OCG can interact with the existing
prepaid platforms. Eventually, all related prepaid charging should be assimilated in the OCG.
Revenue Management should check the integrity of the OCG vs. the IN Prepaid platform,
including prepaid customers information and credit balances. The information residing in the
OCG should be checked against that in the HSS. It is essential to maintain data integrity and
avoid duplications and discrepancies during the overall transit period from phase 1 to 3.
4G ‐ LTE Network
MME HSS
On‐Line Off‐Line
Charging Charging
e‐NodeB
S‐GW P‐GW
Radio Access Network RCEF
Evolved Packet Core RCRF
SPR
Figure 3 – LTE Charging
During phases 2 & 3, the customer’ information residing in the HLR should be transferred and
assimilated in the HSS. The transit period should be monitored by Revenue Management in
order to maintain data integrity and avoid duplicates and discrepant configuration records.
The customers’ service information residing in the HSS, SPR and OCG should also be checked
on a periodic basis in order to detect Policy Violating service attributes (e.g. customers having
service attributes they are not entitled to), and service provisioning misprocesses and
latencies. The SPR may be co‐located with the HSS or separately deployed by other parties (i.e.
MVNOs, Wholesale operators).
Data residing in the HSS, SPR and OCG may also be exposed to either internal or external
(hacking/backdoor) fraudulent activities, aimed at affecting charging or damaging operations.
Data integrity controls are therefore necessary on a periodic basis, including strict monitoring
of any illegal or policy violating activity (e.g. fraudulent data manipulation) performed on the
above service platforms.
1.2 Usage
Moving from flat based data usage to QoS/bundled data usage increases the significance of
usage data records generated by the LTE network elements (S‐GW, P‐GW, PCEF and PCRF).
Most of these usage records are “partial” in nature, requiring the Revenue Management
systems (RA and Fraud Management) to process/aggregate them into “completed” usage
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records (e.g. a record addressing an overall data session). The sheer number of those “partial”
records requires a capable platform having the ability to collect and process all records.
As with 3G networks and services, Revenue Management should check the completeness and
integrity of usage records generation (by the relevant network elements), collection (by the
data collectors) and processing, in order to detect lost or misprocessed billable events. The
Fraud Management solution should detect suspicious traffic patterns, risky services,
contract/registration violating usage and more.
LTE is an emerging service and in order to manage the technical and service environments and
be able to plan ahead, it is important to generate and track usage figures and patterns. The
Revenue Management solution should provide the means for traffic and utilizations trends
analysis and reports, providing the relevant indications as to suspicious/important deviations.
1.3 Billing
LTE facilitates offerings of an increased number of data service types and service derivatives.
Furthermore, LTE supports QoS/data bundling dependent charging instead of the flat based
charging common to 3G data services. QoS offerings may change dynamically between, and
even during, data or media sessions, making the charging process more complex. The increase
in the number of service attributes associated with registered customers (post and pre paid),
enhances the complexity posed on either prepaid (on‐line charging) or postpaid (off‐line
charging). Enhanced service offerings may enable a customer to choose which services are
prepaid and which are postpaid. It is therefore clear that we must validate the integrity of
customers’ information (static and dynamic) in both on‐line and off‐line charging systems.
From a charging integrity perspective, the handling of dynamically allocated QoS/data bundled
service parameters extends the complexity of both on‐line and off‐line charging, thus
increasing the probability of errors and misprocessing. It is essential to impose charging
integrity controls to check for undercharges, overcharges and miss rates. These integrity
checks should be carried out for both on‐line and off‐line charging (note: the solution can be
implemented across “chosen” traffic/service samples).
As with 3G networks, a Revenue Management system should check that all billable usage
events are processed and that all the invoices are handled and delivered in the right manner.
The bills generated should be examined periodically in order to detect Policy Violating charges.
Data residing in the on‐line and off‐line charging platform may also be exposed to either
internal or external (hacking/backdoor) fraudulent activities that are aimed at affecting
charging or damaging operations. Data integrity controls are therefore necessary on a periodic
basis, including strict monitoring of any illegal or policy violating data modification to either of
these platforms.
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2 Revenue management solution
The tables below map the relevant Revenue Management process required to address major
revenue leakages and fraudulent activities associated with LTE.
The tables address only the threats posed by LTE. Classical 3G revenue threats are still
relevant, but are not addressed below. The following Revenue Affecting Threats are divided
into Revenue Assurance and Fraud Management threats.
2.1 Revenue Assurance
Threat Control Solution Platform
Integrity of HSS configuration Compare CRM/Billing vs. HSS MoneyMap
Compare HSS vs. HLR
Compare HSS vs. IN prepaid
Integrity of SPR configuration Compare CRM/Billing vs. SPR MoneyMap
Compare SPR vs. HLR
Compare SPR vs. IN prepaid
Integrity of OCG configuration Compare CRM/Billing vs. OCG MoneyMap
Compare OCG vs. IN prepaid (incl. remaining
credit)
Completeness of HSS Latencies in CRM/Billing vs. HSS MoneyMap
configuration
Completeness of SPR Latencies in CRM/Billing vs. HSS MoneyMap
configuration
Completeness of OCG Latencies in CRM/Billing vs. HSS MoneyMap
configuration
Service Policies ‐ HSS/SPR/OCG Assure CRM/Billing vs. Service Policies MoneyMap
Generated CDRs integrity Count & aggregated payloads of CDRs PCEF vs. MoneyMap
OCG
Count & aggregated payloads of CDRs PCEF vs.
Off‐line Billing
Usage deviations Trend analysis of OCG CDRs MoneyMap
Trend analysis of Off‐line Billing CDRs
On‐line rating integrity Accuracy of OCG processing (sample) MoneyMap/RBV
Off‐line rating integrity Accuracy of Off‐Line Billing (sample) MoneyMap/RBV
Invoice integrity Accuracy of invoices for LTE customers MoneyMap/RBV
LTE billing integrity Completeness of inputs to Off‐Line billing vs. MoneyMap
rated CDRs
Billing Service Policies by OCG rated records vs. Service Policies MoneyMap
customer type Off‐Line rated records vs. Service Policies
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2.2 Fraud Management
Threat Control Solution Platform
Manipulation of HSS data Look for fraud characteristics:
MoneyMap +
HSS vs. CRM/Bill
FraudView
HSS vs. Service Policies
Manipulation of SPR data Look for fraud characteristics: MoneyMap +
SPR vs. CRM/Bill FraudView
SPR vs. Service Policies
Manipulation of OCG data Look for fraud characteristics: MoneyMap +
OCG vs. CRM/Bill FraudView
OCG vs. Service Policies
OCG vs. IN
Unauthorized usage Usage CDRs by blocked/non registered users FraudView
Illegal usage Usage CDRs for non authorized services FraudView
Illegal bandwidth utilization Usage CDRs with QoS exceeding registered FraudView
services
Large utilized bandwidth (risk of MoneyMap +
piggybacking) FraudView
Service Policies violating usage Usage superseding service policies FraudView
Suspicious usage Large usage counts FraudView
Large usage payloads
Large usage during irregular hours/time
bands
“Hot Listed” usage
“Behavior” violating usage (large deviations
from average usage)
Suspicious service configuration Look for “non authorized”/suspicious access FraudView/Internal
(CRM/Billing/HSS/SPR/OCG) or service configuration fraud
Service violating configuration Look for “non authorized”/suspicious access FraudView / Internal
(CRM/Billing/HSS/SPR/OCG) or service configuration fraud
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2.3 Revenue Management Control Points
Figure 4 below, outlines the major data sources (or control points) required for both Revenue
Assurance and Fraud Management
Business Support Systems
Conf. Conf. Conf.
Service
Billing
Configuration CRM
Portal System
CDRs
Conf.
Peering & IP Multimedia
Interconnect
CDRs Billing Subsystem
CDRs
Conf.
Application
CDRs Servers
CSCF
Conf.
HSS
MGW
CDRs
MME
Conf. Conf.
CDRs
On‐Line Off‐Line
e‐NodeB Charging Charging
S‐GW P‐GW CDRs
Radio Access Network RCEF
CDRs
Evolved Packet Core PCRF
Conf.
Conf. Represents Subscriber or Service information SPR
Conf.
CDRs Represents Usage Records
Figure 4 – LTE Control Points
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3 About cVidya
cVidya Networks is a global leader of Revenue Analytics solutions for telecom, media and
entertainment service providers. Innovative cVidya solutions serve to maximize margins,
improve customer experience and optimize ecosystem relationships by encompassing Revenue
Assurance, Fraud Management, Operational Risk Management & Compliance, Sales
Performance Management and Pricing Analytics.
The cVidya experts and consultants have established a stellar track record by achieving rapid
ROI and lower TCO for over 150 customers. Operating regional offices worldwide, cVidya has
partnered with leading vendors to implement an impressive base of operational solutions.
cVidya’s customers include British Telecom, Telefonica Group, Vodafone Group, AT&T, O2,
MTN and Swisscom. Follow us on Facebook, Linkedin, Twitter or visit us at www.cvidya.com
and YouTube.
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