SON (Self Organizing Networks) An analysis of its benefits in the mobile domain

School of Computing, Telecommunications and Networks Faculty of Technology, Engineering and the Environment

SON (Self Organising Networks) An analysis of its benefits in the mobile domain Sumit Kumar Student No. – STU 28913 November, 2015

Masters Final Project Dissertation

Supervisor : Mr. Shane McMordie

ABSTRACT Mobile Telecommunications Networks over the time has grown in complexity and has reached the stage at which automations are now indispensable for running a modern day mobile network. Crucial areas of network such as design, planning, and optimization need a real time automation solution.

This is why the SON technology was developed. SON is a dynamic plug and play system of hardware and software bundle which when connected to the telecommunications network would automatically configure, optimize and heal the network.

As with every new technology, there is always a degree of scepticism that comes along and it is quite important to validate its functioning before we accept it. The aim of this project is to analyse the three key features of SON i.e.

Load Balancing / ANR on 3G network, RET feature analysis on LTE network and GSM spectrum Management on 2G network. It is intended to implement these features on a live network and see the overall gain in the network performance. A critical analysis is done to see the magnitude of impact due to SON. It is also intended to find out areas where SON did not work well and had issues with its implementation, working and accuracy.

In this project the main functionality of SON was closely reviewed when it was implemented in an urban cluster. The KPI gains were measured in the area of capacity, coverage, load balancing and quality improvements. The final result not only gauged the magnitude of performance improvement but also highlighted the complexity and drawbacks of the SON solution.

Some remarkable improvements in the network quality were observed especially in the 3G and 2G network but there were couple of areas where SON had issues and had failed to achieve what expected. These were noted as learnings and areas of future improvement.

Declaration I hereby declare that this dissertation project is my own work and effort, and that all sources cited or quoted are indicated and acknowledged by means of a comprehensive list of references.

November, 2015

Sumit Kumar

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Contents 1

INTRODUCTION ..........................................................................................................................5 1.1 Motivation ........................................................................................................................... 5 1.2 Objective and scope ........................................................................................................... 6 2 Literature Review .........................................................................................................................8 3 Project Design – SON implementation ..................................................................................... 22 3.1 CCLB and CANR trial on 3G RNC ................................................................................... 22 3.2 SON RET test for LTE ...................................................................................................... 28 3.3 GSM Spectrum Management using SON ........................................................................ 31 4 Results – SON Performance ..................................................................................................... 32 4.1 CCLB and CANR on 3G RNC - Results ........................................................................... 32 4.1.1 KPI Charts .................................................................................................................... 35 4.1.2 Capacity Enhancements............................................................................................... 38 4.1.3 Drive Test Plot comparison .......................................................................................... 40 4.2 SON RET feature trial on LTE network ............................................................................ 45 4.2.1 Volte UE performance result ........................................................................................ 45 4.2.2 FTP UE performance result .......................................................................................... 48 4.2.3 SON Load balancing feature trial ................................................................................. 51 4.2.3.1 KPI Performance Phase 1 Result ......................................................................... 51 4.2.3.2 KPI Performance Phase 2 Result .......................................................................... 53 4.2.3.3 KPI Performance Phase 3 Result .......................................................................... 54 4.2.4 New site introduction .................................................................................................... 57 4.3 GSM Spectrum Management using SON ........................................................................ 59 4.3.1 KPI Performance Analysis ............................................................................................ 60 4.3.2 Drive test KPI Plots Comparison .................................................................................. 60 5 Learnings and Recommendations ............................................................................................ 61 5.1 SON PCI problems ........................................................................................................... 61 5.2 Multivendor Issues observed ............................................................................................ 63 6 Conclusion................................................................................................................................. 64 References ....................................................................................................................................... 65 Abbreviation ...................................................................................................................................... 66 Bibliography ...................................................................................................................................... 68

Chapter 1 1 1.1

INTRODUCTION Motivation Imagine a mobile communication network which has three different

technologies and multiple vendor equipment supporting various services. An LTE RAN from Ericsson overlaid on Second and Third generation RAN nodes supplied by Huawei. An access transmission network provided by Aviat and a backbone SDH microwave ring by Ericsson and part of it on DWDM optical fibre by Huawei, part of the access transmission on TDM and part on ATM/IP. This is no imagination; this is a reality of today’s complex networks, at least where I work, the network is similar in complexity.

In such a complex metro network, the traffic pattern is highly dynamic, the operational issues are unforeseen and it is not only difficult to optimize but it is also difficult to maintain service quality levels to match the customers’ expectation. Unknown events such as a mass gathering by a church or a site going down due to power can result in loading the nearby neighbouring sites and thereby degrading network quality of both voice and data. Just to manage these issues which directly impacts the customer perception and the revenue needs a lot of expertize on different network elements, manpower and other resources. This is a complex affair, to catch issues in close to real time and execute a remedy. Traditional way of managing network quality seems to have no future.

Self-configuring, self-healing and self-optimizing functions are now more than necessary to manage a complex mobile network; it is indispensable in this age. This is where SON comes to rescue. SON promises to help improve the network quality by centralizing the functionality and optimizing multiple network elements and domains in a highly coordinated approach.

SON boasts of an autonomous operation where no manual intervention is required. SON functionalities are too many as shown in the table below:-

Self-Planning

Self-Deployment

Self-Optimization

Self-Healing

Planning the location of a new Hardware installation base station

Support for a centralized Hardware capacity expansion optimization entity / replacement

Planning the radio parameters TX link setup of a new base station

Neighbor list optimization

Automatic software download to the NodeB

Planning the transport parameters of a new base Authentication of the node Interference control Automatic NEM upgrade station Planning data alignment for all Setup secure tunnel to O&M Handover parameters Cell / service outage detection neighbor nodes gateways optimization Automatic performance QoS-related parameters Automatic inventory update management data optimization consolidation Automatic software download Information correlation for Load balancing to the NodeB fault management Common channels Self-Test of the system Cell outage compensation optimization Radio parameters Configuration of home NodeB Compensation for outage of optimization for Home base station higher level NEs eNodeBs Transport parameters Fast recovery of unstable optimization NEM systems Routing optimization Mitigation of unit outage Energy saving SON in core networks

Fig 1 above: Table of SON functionalities SON Functionalities:Juan Ramiro (ed.), Khalid Hamied (ed.), (2012) Self-organizing networks: selfplanning, self-optimization and self-healing for GSM, UMTS and LTE, TK5103.2.S46 2011, John Wiley & Sons Ltd.

This project is focused on few of the key functionalities and will view the performance of the network before and after the SON deployment. As with every major technology introduction, the benefits and drawbacks are not as simple and clear as they first appear. Many drivers are pushing the network operators to introduce SON in their networks and there is no doubt that this is the future but before accepting a new technology which is going to change the way networks are managed and the lives of the engineers who have been managing them, a little more detailed investigation is required.

1.2

Objective and scope

The project is comprised of two main areas. First, a thorough research is conducted on the features and offerings of a Self-Organizing Network. This research will be based on 3GPP explanations of the concept and architecture. The source of information is various articles and white papers written on the subject. Second area is the implementation of a SON quality

improvement feature with four critical optimization functions on a small cluster of a live multi technology network.

Measurements of quality samples were taken before and after the SON feature implementation and the performance was recorded and reviewed separately. Based on the results, an analysis was carried out on the benefits and drawbacks of the SON solution. The analysis also threw some light on the traditional way of optimizing mobile networks and the benefits an automated system brings.

The research was finally concluded with a short survey done on the engineers working on traditional optimization and those working with a SON solution. The benefits and complexities of the system were analysed and reported with a final conclusion and the prospect of future research.

The objective of this project is as follows:1. To have an overview of the key offerings of a SON solution.

2. To analyse the drivers and architecture of SON in multi technology environment.

3. To analyse the working of key features such as Intra frequency Load balancing, RET optimization (open loop and closed loop) ANR optimization and GSM spectrum management.

3. To measure the Baseline KPI of a cluster before SON activation and measure the gain after activation.

4. To assess the complexity of a SON solution and its return on investment 5. To comment on the benefits and drawbacks to adopt this technology.

6. To recommend the adoption of SON, its benefits and challenges, to the network operators.

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Chapter 2 2 Literature Review In the year 2006 a group of telecommunication operators and vendors formed the NGMN (Next Generation Mobile Network) Alliance with a main objective to provide guidance to the mobile broadband technical standards developed for the LTE. The group closely analysed the major work functions and tasks performed by an optimization and network improvement engineering team. Based on their analysis, they released the first white paper detailing the enhanced efficiency to be incorporated in the use cases for the NGMN SON operation.

The top ten use cases are listed below.

1 2 3 4 5 6 7 8 9 10

Plug & Play Installation Automatic Neighbor Relation configuration OSS Integration Handover Optimization Minimization of Drive Tests Cell Outage Compensation Load Balancing Energy Savings Interaction home/macro BTS QoS Optimization

The NGMN guidelines have emphasized the interoperability between the OSS, BSS & EMS between multivendor equipment and the ability for the integration with a 3rd party open interface software. Before we understand, try and test a SON, we need to also look at how the network is managed in the absence of SON. Traditional RRM (Radio Resource Management) has all the features which can be used to improve a network quality. The load based handover, capacity management, Half Rate trigger threshold (for 2G), power control etc. are all present in the traditional RRM. The key difference in a traditional RRM and a SON is that RRM is governed by a set of parameters (system level) and a SON collects real time data from various interfaces (measurement stats, KPI

and traffic) and changes and adjusts the parameters dynamically in real time.

This automation makes it quicker and improves the quality for that environment at that time. It also reduces any possible human error.

This section will briefly touch upon the key features of a SON in a multi technology environment. Out of the listed key features, the most important ones will be tested in a live network and their results will be analysed in the subsequent sections.

(Self-Optimizing Networks- Removing Risk and Adding Value, Tim McElligott, Frost & Sullivan, February 2014, http://www.frost.com/sublib/display-report.do?id=D532-00-B2-00-00 – Accessed on May 2015.)

SON Architecture in Multi technology environment

There are three different SON architectures that have been defined by the 3GPP.

Fig 2 above: SON architecture (Østerbø, O. and Grøndalen, O. (2012), ‘Benefits of Self-Organizing Networks (SON) for Mobile Operators’, Journal of Computer Networks and Communications, Vol. 2012, Article ID 862527. Available at www.hindawi.com/journals/jcnc/2012/862527/.)

In the Centralized SON architecture, the algorithms are deployed in the control system of the network, typically at the OMC which in turn gives access to the configuration management, performance management and planning database etc.

Advantages of this architecture are mainly:1. Flexibility – to manage sophisticated optimization strategies.

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2. Ease of deployment – since the function is located in OMC and few other locations.

3. Diversity – ability to align with multivendor strategy and equipment 4. Independence – ability to have 3rd party supplier provide this on multivendor network.

Disadvantages are:1. Capacity constraints – required massive bandwidths to exchange data between different elements.

2. Delay in response – Due to the data capture and reporting dependence on CM & PM database / statistics the changes cannot be made in real time.

3. Redundancy – It is prone to single point of failure.

Remedy:1. Redundancy – More reliable infrastructure to be used and the system to be configured with auto update with the last saved configuration data.

In the Distributed SON architecture, the algorithms are deployed in the NodeB/RNC/Base station controller network elements.

(Nokia Networks White Paper, Product code C401-011915-WP-201508-1-EN, Nokia Solutions & Networks, http://networks.nokia.com/file/34496/lte-m-optimizing-lte-forthe-internet-of-things)

Advantages:1. Real time execution – Functions monitors traffic pattern and are able to make changes in the system in real time.

2. Less Capacity constraint – low requirement of huge bandwidth because the functions sits in the controller.

3. Redundancy – No risk of single point of failure.

Disadvantages:-

1. Small scale – Optimization is on few network elements and is mainly local.

2. Isolated strategy – Not too flexible in cross correlation of parameters as this required a lot of data interchange. One strategy cannot be replicated throughout and coordination between other SON functions is a problem.

The Hybrid SON architecture is a combination of two stage control architecture. The algorithms are located at the OMC or at the server connected to the northbound interface of the OMC. It manages the centralized as well as the distributed algorithms.

Advantages:1. Real time actions – Hybrid architecture allows real time actions Disadvantages:1. Coordination – Complex coordination between other SON functions is a problem.

In our analysis on the live network in the next section, we will be using a distributed SON architecture.

Self-Planning The process to identify a new site location, its hardware configuration, transmission backhaul capacity, radio frequency, codes and neighbour relations based on various inputs from planning tool, projections and strategy is called network planning. Self-planning is nothing but doing this whole thing automatically in order to reduce the cost and effort. There are couple of requirements for the self-planning to work properly:a. Path-loss propagation and other input data should be accurate. It can be improved by model tuning or live data recordings. b. Mathematically compute and arrive at most optimal parameter settings in the given environment Page 11

c. Activate network elements and implement parameter changes automatically and for different vendor equipment.

Typical constraints for a multi technology self-planning:a. Dual or triple band antenna sharing- The only flexibility for different technology to use the same antenna would be to have their own electrical tilts. Any change in orientation or height would impact all the technologies. So it needs to be ensured that one technology is not favoured over the performance of other. b. Traffic sharing between collocated sites- overlaid IRAT in the same location would share the traffic within their respective coverage footprint. Proper projection and strategy of the operator needs to be defined to offload or balance one particular IRAT.

Fig 3 above: Self-planning and integration process

Although Self-planning slightly overlaps self-optimization, but it should be noted that planning more to do with a new node integration and optimization is to do with the quality improvement of the existing nodes.

The major soft parameters requiring automatic self-planning are as below:1. 2/2.5 G – GSM – BCCH, TCH, BSIC, MAIO, HSN, TXPWR, Handover margin etc.

2. 3G – UMTS – CPICH, PSC, CE, IRAT Neighbour relation, SHO etc.

3. 4G – LTE – PCIs, DL/UL assigned RBs etc.

Automated Site selection and GSM Spectrum Management In GSM/Time Division Multiple Access (TDMA) networks, RF planning is generally executed before Frequency Planning. Its mission is to improve the RF environment, as defined by KPIs such as coverage and quality (i.e. overlap between sectors), to make channel planning easier.

Bin Cost Function Component for GSM For each individual bin, the format of the cost function is given as:

Cb = f(tb)[fCOV(Pb) + fINT(Pb)], Typical GSM RSL thresholds for different environments Environment

RSL threshold [dBm]

Dense urban

−66

Urban

−70

Suburban

−76

Rural

−84

where tb is the amount of traffic served in bin b and f(tb) is a scaling factor associated with that traffic, and is used to provide weighting for the cost function, fCOV(Pb) is the function designed to penalize lack of coverage within the bin, fINT(Pb) is the function designed to penalize the interference within the bin and Pb is the vector of received signals at that bin from all Neighboring sectors.

Penalty for Lack of Coverage To establish and maintain a call within any given geographic location, the received signal level on the DL needs to be above a certain threshold. It is usually assumed that density of man-made structures and their impact on Page 13

the signal attenuation is highly correlated with the morphology classification.

Since Received Signal Level (RSL) predictions are typically computed outdoors, the coverage thresholds for the received signal are morphology dependent as well in order to take into account typical indoor penetration Interference Penalty Each sector in a cellular system should cover only its intended service area. Coverage problems arise when the actual coverage footprint is smaller than what was intended by design. On the other hand, if the actual coverage area exceeds the desired one, intra system interference between sectors may occur. To maintain quality within a bin, the potential Carrier to Interference ratio (C/I) in the bin needs to be sufficiently high, where C denotes received signal level from the observed serving cell and I denotes interference (the sum of received signals from other cells).

Typically, signals from several cells capable of servicing the call can be found within each bin. During the planning process, it is usually assumed that the principal serving cell is the one with the highest RSL. The rest of the cells represent interferers and their signal degrades the quality of service.

Potential C/I (as opposed to actual C/I) does not take into account a specific channel plan, the assumption being that any potential interferer can be an actual one. Some typical values of the RSL thresholds for different environmental types are presented in Table above.

In general, the RF planning algorithm assumes that a bin meets the coverage requirements if the RSL of the strongest server is above the threshold value specified for the bin’s morphology.

GSM Sector Cost Function Component In addition to coverage and quality, another important factor in GSM cell planning is capacity. Each base station has a certain number of TRXs capable of serving a predetermined amount of traffic (users). The sites in the network should be planned in such a way that two main capacity objectives are met with minimum hardware resources. Firstly, no users in the network

should be blocked. Secondly, the load between the base stations needs to be as uniformly distributed as possible, thus achieving what is typically referred to as balanced load. For each individual sector, the format of the sector component of the cost function is given as: C(s) Max ((E(s) - EMax (s))/(EMax (s)),0)) Where Cs is the sector cost designed to penalize for exceeding capacity at sector s, E(s) is the traffic served by sector s and EMAX(s) is the maximum traffic for sector s. Maximum traffic depends on number of TRXs assigned to sectors.

(Zhang Congling & Xie Guozhu, 2003, Maximizing Spectral Effeciency: Huawei Technologies)

Automated Neighbour planning in LTE In GSM & UMTS, the handover neighbours can be defined from one cell to the other:a) Intra cells – Within the same cell b) Inter cell – with other cells c) IRAT – Inter radio access technology i.e. GSM to 3G and vice versa d) External – cells managed by two different BSC/RNC

Between LTE and UMTS the handovers are of the type:a) White list – cells considered for reselection b) Black list – cells that are barred from reselection

Generally speaking the handover event happens whenever a set of criteria are fulfilled, such as:a) Server signal becomes weaker than Neighbours’ b) Server signal quality deteriorates and a neighbour with acceptable signal strength exists c) An IRAT neighbour becomes better that an absolute threshold Page 15

d) Server cell becomes worse than an absolute threshold for signal level or quality or interference.

It is not very difficult to plan for a neighbour when the network exists and a new site is added to the existing networks. The measurement report from the mobile station received by the controller can easily help in deciding a neighbour relation to be added or not. The difficulty is when the network is new and no measurement reports are fed to define a relation. SON’s automatic neighbour planning feature can help.

Based on the coverage prediction an interference matrix is created and couple of things are considered before automatically adding a neighbour. Minimum signal strength overlap with the neighbours, Maximum number of neighbours per cell, maximum distance between neighbours are few of the important checks which when done, the ones passed are added automatically to the neighbour list.

Fig 4 above: shows the Neighbour signal measurement by mobile

Automated Site Planning in LTE Planning of LTE PCI is similar to the planning of 3G scrambling code.

The LTE PCI, similar to the 3G SC is used to identify the different cells in the vicinity. PCI determines the sequence to generate the DL RS which the UE

needs in order to demodulate the received signal. PCI is also used in bit scrambling which is carried out prior to modulation which must be different for neighbouring cells to ensure interference randomization and full processing gain of the channel coding. PCI is also used to select the hopping pattern of the sequence that is used to generate the uplink RS and the mapping of resource elements to certain channels.

The NGMN alliance has included the SON use case for automatic planning of PCI. The automatic planning of PCI has many advantages over the traditional planning. It replaces the complex manual process and helps avoid human errors. The SON automatic configuration of physical cell identity aims at configuring the PCI for a newly introduced cell automatically fulfilling the requirements of collision free configuration.

Fig 5 above: SON PCI configuration

There are different methods used for this automation, one method allows NodeBs to automatically configure their PCI’s and the other uses centralized control. The first method where the NodeBs automatically configures its PCI is shown in the fig above where a new cell randomly chooses a temporary PCI and obtains the neighbours’ PCI and finally adjusts is PCI accordingly to avoid collusion and confusion. The diagram represents such a case where a new cell is introduced in the centre of the cluster of cells and the newly introduced cell adjusts its own PCI. Another method is the centralized one where central function stores information about NodeBs including their location and a simple distance Page 17

calculation is triggered when a new cell is introduced. This calculation provides an optimum PCI assignment for the newly introduced cell.

Self-Optimization Self-optimization is the utilization of mobile measurement recordings and performance indicator statistics collected by the base station systems to auto tune the network settings to maximize the quality and performance.

The highlights of Self optimization are as below mentioned:Measurement based – Cell parameter adjustments based on the theoretical propagation prediction models and manual statistics analysis to be replaced with decisions driven by real time system performance measurement, traffic profile and spatial distribution of users.

Sector and adjacency based – A homogeneous network configurations are usually not optimal and therefore requires an optimization of the finest possible resolution i.e. sector or adjacency based.

Multivendor capable – Access and analyse statistics from different vendor subsystem and make decision based on it. Provide automatic or autonomous mechanism to implement the derived changes into the multivendor network.

Multi technology capable – Must be able to deal with Inter Radio Access Technology which includes load balancing and traffic sharing between 2G, 3G and LTE overlaid networks as well as improve capacity and quality in the multi technology system.

Auto configurable – Able to leverage all the information collected from different system OSS and operate according to high level of objectives, trade-offs and policies set by operators in terms of capacity, coverage, quality or a combination of them.

The aim of an optimization system is to improve the performance of the Radio Access Network both single and multiple access technologies without the expense of any. The inputs for such optimization system are performance counters/statistics, call traces, alarms and the operator requirement of KPI target. The outputs for the optimization system are the new proposed values for the radio configuration parameters. The operator’s requirements include prioritization between various different KPI’s, allowed KPI margins, allowed ranges and criteria to accept data statistical event.

The automated solution must lower the required operational effort and improve the performance of the network.

The process is typically automated by following the 3 steps:1. Collecting network information from the RAN system 2. Executing an optimization algorithm based on the gathered data 3. Implementing the required changes in the network One of the most common features of automatic control cases is the use of feedback information, the so called closed loop system. In closed loop system the adaptive iterative process is adopted in which the optimization system modifies the network parameters and the resulting performance indicators are subsequently fed as input for the optimization system to derive the next set of parameter settings. The advantage lies in the ability to control unstable systems.

(Ramiro & Hamied, (2012) Self-organizing networks: self-planning, self-optimization and selfhealing for GSM, UMTS and LTE, John Wiley & Sons Ltd.)

Self-Healing Self-healing is the execution of a routine action that keeps the network operational and prevents disruptive problems to occur. Self-healing covers Page 19

the areas of fault management, fault correction and O&M (operations and Maintenance). The need for automation in this field is aggravated by the deployment of new and complex technologies and the increase in the broadband data usage by the users. Such high demands put a lot of pressure on the network engineers whose job is to ensure a high network quality of service and at a reduced operational expenditure. In order to streamline the performance monitoring and troubleshooting process it is necessary to automate the complete process of analysis to carry out the cross correlation between the performance and configuration management and detect existing and upcoming problem in near real time. The diagnosis of the problem and execution of remedy should also be added in the process so that the problem is also cured automatically.

The healing solution should portray the following characteristics:Multi source – Capable of analysing all sources of information that can be collected autonomously from multiple sources such as performance counters from OSS configuration management, alarms from fault management terminal and call data records from intelligent network servers.

Multivendor – Must be able to access and interpret different sources of information from different vendor equipment. It should be able to analyse and suggest changes to parameters for different vendor systems and not just general suggestions for vendor independent systems.

Multi technology – Must be able to deal effectively with different technologies both independently and in combined space. Hence it must be possible to analyse problems in the IRAT handover mechanism

by

analysing both external and internal neighbour relations and other operational problems where a power outage in a collocated site is impacting both the technologies.

Flexibility – The algorithms which runs to automate analysis of outage and other O&M issues should be flexible enough to be tuned as per the

operator requirement as most of the time the operator already has lots of local knowledge which can be wisely used by the system to make informed decisions.

Configurable and capable of triggering actions – It must have the capability to configure policies by the operator and be able to trigger actions when required. For example if an urgent need is there to plan a new set of frequencies for a cluster of the radio network then this should be possible for the self-optimization component of the self-healing solution to trigger this activity.

Self-Healing Process:

Detection – Detect the symptoms of the problem when it is about to occur Diagnosis – Perform the root cause analysis of the detected problem Cure – Application of the corrective action to fully or partially restore the service.

Fig 6 above: Self-healing process

Inputs for self-healing – There are many possible reasons for cell outage, starting from internal configuration issues to other external factors such as power outage, battery Page 21

rectifier problems, fuel shortage or weather related issues. All this makes the detection a bit complex and cumbersome that requires examining a wide range of potential problems to be accessed from different sources. Such information from different sources are categorized in the below list.

1. Configuration management data 2. Fault management and alarm data 3. Performance management data 4. Call traces 5. Network topology information Cure in Self-Healing – Since we will be checking the live network performance in the next chapter, it is good to know about the possible cure solutions in a SON network. Below are some of the potential actions to fix the performance degradation.

1. Reversion of parameter settings back to the original 2. Assignment of default value for out of range parameter settings 3. Activation some special features that got disabled 4. Perform complex action such as generation of new frequency plan or scrambling code plan.

5. Neighbour addition and deletion and active set updating

(Ramiro & Hamied, (2012) Self-organizing networks: self-planning, self-optimization and selfhealing for GSM, UMTS and LTE, John Wiley & Sons Ltd.)

Chapter 3 3 Project Design – SON implementation 3.1

CCLB and CANR trial on 3G RNC The SON OM feature trial consisted of CCLB (Closed loop and later

open loop), C ANR (open loop) and G ANR (need to have basis). This test was conducted on 2 RNCs in Victoria Island area of Lagos. The total number

of cells to be optimized were 185. There were two carriers deployed. (The network operator has authorized the vendor to conduct test trials using SON for a specific period of time in a managed service framework. Among other feature trials done by the vendor, some tests were allowed to match the scope of my project. Subscribers have 3G voice and data access but LTE modems have been distributed to some offices and to the drive test team to conduct extensive LTE analysis.)

F1 – UARFCN 1564 – 101 cells F2 – UARFCN 1588 – 84 cells

Fig 7 above: Lagos Victoria Island (Location) and the 3G/LTE site map (the trial cluster) SON – Coverage and Capacity Load Balancing (CCLB) layout

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Fig 8 above: SON (OM) Deployed architecture (Courtesy: Ericsson RSSA (Region Sub Saharan Africa) SON OM Roadmap, 2012)

Fig 9 above: SON OM deployment diagram (Courtesy: Ericsson RSSA (Region Sub Saharan Africa) SON OM Roadmap, 2012)

Fig 10 above: Hardware setup: SON server farm connected via citrix to the local OSS

C-ANR (Centralized Automatic Neighbor relation Optimization) and CCLB (Coverage and Capacity Load Balancing) closed-loop executions were run on the 3G network. New site integration was also carried out and the ANR was tested. The SON Optimization Manager settings which were used are listed below.

individualOffset

[-1, 2]

qOffset2sn

[-3,3]dB

minPwrMax maximumTransmissionPwrMaxDLPwrCap

qRxLevMin Qqualmin + sInterSearch

[-115, -111]dB

minPwrRl

[0,2] [-10, 20]dB

[-12,-6]dB

primaryCpichPower

[8, 11] %

sRatSearch

[0,4]

primarySchPower

usedFreqThresh2dEcNo

[-15, -12]

secondarySchPower

[-1, -3] dB [-2.5, -5] dB

usedFreqThresh2dRscp

[-103, -100]

bchPower

[-3, -5] dB

loadSharingMargin

[0,50]

aichPower

[-5, -8] dB

Pathlossthreshold

[120, 140]

pichPower

[-4, -8] dB

powerOffsetPpm

[-5,6]

pchPower

[0, -4] dB

constantValueCprach

[-30,-15]

maxFach1power

[1,3]dB

pwrAdm maxFach2power [80, 90] Table above: SON policy manager system settings

[-0.5, 4]

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[1, 3] dB

The Projects main system interface on the OSS side was Ericsson SON-OM (Operations Manager) and it collected data, parsed and performed data maintenance, decision-making and implementation of changes in the natwork without human intervention. User-defined �settings� (i.e. Parameters that control the optimization behavior) were kept to the necessary minimum, so that critical, sensitive settings that may be required for the optimization to deliver good results were auto-calculated and hidden from the user. This helped in avoiding complexity from adjusting parameters of the RAN. SON-OM has visibility of several vendors and technologies, its optimization algorithms do not have discontinuities nor border effects.

Figure 11 above: SON-OM explorer window

Fig 12 above: SON-OM functionality

SON Operations Manager has an ANR feature which will be used in the project. It is intended to provide a neighbour plan which minimizes missing neighbors and set optimum priorities (ranking) of defined neighbors.

Actions taken by the ANR feature would: –

Create neighbor relation (IAF, IEF and IRAT).

–

Delete neighbor relation (IAF, IEF and IRAT).

–

Change neighbor priority (selection Priority parameter in Utran Relation, Gsm Relation managed objects).

CCLB feature in SON-OM should help improve capacity, coverage and cell Quality (e.g. RACH BLER) should not be compromised. CCLB is designed to reduce the amount of overhead power: –

RET

–

CPICH power

–

Common Channel power

–

FACH power

–

RL power

–

RACH power

–

Maximum Transmission Power

RET which is part of CCLB should also help in improving the network quality by changing the coverage footprint.

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Fig 13 above: RET equipment installed in a cross polar antenna. (Courtesy Kathrein Germany)

3.2

SON RET test for LTE

In the last section we saw the CCLB & ANR features of SON and its performance on a 3G network. Now we will try out the Intra frequency load balancing and RET optimization SON feature on an LTE network.

Intra-frequency Load Balancing periodically adjusts Idle and Connected mode cell border parameters based on network capacity. LB principles include:

a) Maximizing LTE Capacity without degrading Quality b) Minimizing congestion without losing LTE coverage

Coverage and Capacity Optimization periodically adjusts RET (Remote Electrical Tilt of Antenna) and other parameters for HO and cell reselection based on network capacity and trace data.

Fig 14 above shows the coverage footprint adjustments after change of RET

In this trial we will see two scenarios, performance with SON and performance without SON.

When SON is enabled we will check the following things:-

a) SON monitoring network congestion and for coverage gaps b) SON suggesting changes (open loop) and optional automatic change implementation (closed loop) c) Independent Load Balance and Electrical Tilt capabilities d) Tracking KPI’s impacted with SON based on identified traffic state e) KPI’s monitored with SON functionality active resulting in changes either in RET and/or cell border threshold to solve congestion or coverage gaps.

When SON is disabled we will check the following things:-

a) Baseline reference b) Network configuration reverted to original state without changes triggered by SON Page 29

c) No traffic monitoring d) No changes in network configuration e) Network configuration falling back to its original configuration state prior to any changes suggested by SON to address congestions or coverage gaps.

Drive test setup to measure the performance before and after SON activation.

UE Specification a) MS1: LG VS870 performing VoLTE calls b) MS2: LG VL600 performing upload and download c) MS3: Pantech UML290 performing continuous pings

Time Line for drive test Pre-Reversion Drive Test: 9th to 11th June Post-Reversion Drive Test: 16th to 18th June Reversion happened at 1AM on 16th June DAY1: 9th June vs 16th June DAY2: 10th June vs 17th June DAY3: 11th June vs 18th June DAY Date Cluster

Day1 9th June Vic. Island

Day2 10th June

Day3 11th June

RET

Large

SON -OM Optimized Network

12th June

13th June

14th June

15th June

Day1 16th June Vic. Island

Day2 17th June

Day3 18th June

RET

Large

Network parameters reverted back to baseline

3.3

GSM Spectrum Management using SON

The high capacity GSM spectrum management was checked using SON in the same network setup. The main objective was to see the performance of network quality after activating SON for GSM. Two main features of RAN (Radio Access Network) which greatly contributes to the quality of the network are FLP (Fractional Load Planning) and BTS Soft synchronization. In these two areas SON can really help by dynamically adjusting the fractional load of the cell based on the amount of interference and traffic carried by the cell. SON can also dynamically activate soft synchronization within neighbouring cells so that the handover happens more smoothly.

Fractional Loading in GSM is an important parameter in SFH (Synthesizer Frequency Hopping) system. It is calculated as: FLP =

highest non BCCH transceiver count in a cell Number of hopping channels

(Capacity enhancing Techniques, SYS12- Motorola 2001-2 – Motorola Systems Training Manual)

Since the number of frequency channels is always higher than the radio count in a cell, some channels will be idle at one time. Thus, loading factor is equivalent to the maximum channel occupancy to total channel ratio in a cell at any given instant. The lower the value the lower is the channel loading, which indicates fewer collisions of frequencies and hence better quality.

A theoretical maximum of 50% is permitted in 1X3 SFH. Any value higher than 50% practically, results in unacceptable quality. Some commonly used loading factors are 40%, 33%, 25% etc. In 1X1 SFH, a practical tested loading factor is 1/6 or 16.7%. For a rough comparison, this is about equivalent to a 33% loading in 1X3 SFH or a well–planned 4X3Xn fixed re– use network, as far as average quality is concerned. In terms of spectrum utilization or capacity, 1X1 at 16.7% loading is equivalent to 1X3 at 50% loading.

Page 31

Since in our network the SFH is deployed in an ADHOC method, the and MAL (Mobile Allocation List) which is the list of frequencies on which the call should HOP is not same for all the cells and the MAIO (Mobile Allocation Index Offset) ensures that the collision does not happen or even if it happens, it happens at big signal level difference so that the noise/interference is minimal and doesn’t affect the capacity. As and when the voice traffic grew on the 2G network, this collision started to be happening more frequently and hence the poor quality. An intelligent system could dynamically adjust the MAL and MAIO for the cells thereby improving the quality in real time when the traffic increases or decreases.

Fig 15 above: APBSC1 Cluster where SON was activated for GSM BSC

Chapter 4 4 Results – SON Performance 4.1

CCLB and CANR on 3G RNC - Results The Baseline week 22 (daily average of KPI’s) were captured at the

beginning of the project and Optimized week 23 (daily average of KPI’s) were

measured to compare the performance before and after the SON implementation. Weekends were excluded from calculations, in order to avoid distortion due to low traffic days.

Baseline

Optimized

Improve ment

Improvemen t (%)

98.42 0.46 0.68 0.63 5380

99.61 0.15 0.31 0.27 5382

1.19 0.31 0.37 0.36 2

1.21% 67.39% 54.41% 57.14% 0.04%

5855

6384

529

9.04%

1280 19.45 58.82 0.36 0.38 0.61 20.55 2374 404 177 -5.83 1.61 27.13

107 -0.05 1.42 0.05 0.23 0.26 0.21 179 41 22 0.54 0 1.77

9.12% -0.26% 2.47% 12.20% 37.70% 29.89% 1.03% 8.15% 11.30% 14.19% 8.48% 0.00%

CS IRAT HO (%)

1173 19.5 57.4 0.41 0.61 0.87 20.34 2195 363 155 -6.37 1.61 28.9

CS IRAT HFR (%)

1.18

0.85

0.33

27.97%

LGS002 (RNC) Availability / Accessibility

Cell Availability (%) CS CSFR (%) HSDPA CSFR (%) PS CSFR (%)

Traffic

CS Erlangs HSDPA Traffic Vol (GBits) HSUPA Traffic Vol (GBits) PS DL Traffic Vol (GBits) PS UL Traffic Vol (GBits) CS DCR (%)

Retainab

HSDPA DCR (%) PS DCR (%) Avg Used CQI HSDPA Inst Thr (kbps)

Integ.

HSDPA User Thr (kbps) HSUPA User Thr (Kbps) Avg CPICH EcNo SHO Factor

Mobility

Colour legend

10%

5%

Traffic

-10% (absolute value >0.1)

Baseline

Optimized

Improvem ent

Improvemen t (%)

Cell Availability (%)

99.9

99.83

-0.07

-0.07%

CS CSFR (%)

0.55

0.38

0.17

30.91%

HSDPA CSFR (%)

1.07

0.46

0.61

57.01%

PS CSFR (%)

1.01

0.41

0.6

59.41%

CS Erlangs

7757

7464

-293

-3.78%

LGS003 (RNC)

Availability / Accessibility

-5%

6.12%

Page 33

HSDPA Traffic Vol (GBits)

9243

9910

667

7.22%

1984

2001

17

0.86%

29.33

27.94

-1.39

-4.74%

100.91

97.03

-3.88

-3.85%

Avg Used CQI

0.88 0.72 0.74 19.95

0.62 0.52 0.57 20.36

0.26 0.2 0.17 0.41

29.55% 27.78% 22.97% 2.06%

HSDPA Inst Thr (kbps)

2089

2265

176

8.43%

HSDPA User Thr (kbps)

CS IRAT HO (%)

354 171.47 -6.73 1.6 30.48

399 184.6 -5.81 1.6 28.85

45 13.13 0.92 0 1.63

12.70% 7.66% 13.67% 0.00% 5.35%

CS IRAT HFR (%)

1.27

0.86

0.41

32.28%

HSUPA Traffic Vol (GBits) PS DL Traffic Vol (GBits) PS UL Traffic Vol (GBits) CS DCR (%) Retainab

HSDPA DCR (%) PS DCR (%)

Integ.

HSUPA User Thr (Kbps) Avg CPICH EcNo SHO Factor Mobility

Table 14 above shows the Network performance improvement result post implementation of SON

LGS003

LGS002

0% 9%

CS traffic HSDPA traffic CS CSFR reduction HSDPA CSFR reduction CS DCR reduction HSDPA DCR reduction HSDPA Cell Throughput HSDPA User Throughput HSUPA User Throughput

67% 54% 12% 38% 8% 11% 14% 0%

20%

40%

60%

CS traffic HSDPA traffic CS CSFR reduction HSDPA CSFR reduction CS DCR reduction HSDPA DCR reduction HSDPA Cell Throughput HSDPA User Throughput HSUPA User Throughput

80%

-20%

-4% 7% 31% 57% 30% 28% 8% 12% 18% 0%

20%

40%

60%

Table 17 above shows the Optimization Gains

It is seen that the 

CS traffic was retained in LGS002 while in LGS003 it has slightly decreased due to seasonal traffic pattern.



IRAT HO reduction achieved by SON-OM optimization.



HS traffic volume has increased around 8% in both RNC’s

80%



Accessibility - Significant improvements (>30%) on both RNC’s for CS and HSDPA services have been achieved.



Retainability- Important DCR reduction has been obtained in CS and HS services.



Throughput & quality- Improvement on HSDPA throughput (~10%) thanks to a cleaner scenario (EcNo improvement higher than 0.5dB) and power savings



Capacity - More efficient power usage is achieved, 15% more HS traffic is carried using the same power. Despite traffic voice has not increase due to seasonal pattern much better CSFR values have been reached.

4.1.1 KPI Charts CS traffic shows similar values during Baseline and Optimized week in LGS002, while in LGS003 slight increment due to seasonality is observed.

HSDPA traffic volume increased in both RNC’s Legend (Date Lines for below charts) CCLB Implementation Open Loop CCLB Implementation Closed loop

C ANR Implementation Closed Loop C ANR Implementation Open Loop

CS Traffic (Erlangs) 9000

LGS002

HSDPA Traffic Volume (Gbits) LGS002

LGS003

LGS003

10500 8500

6000

7500

5000

6500

4000

5500 25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

9500

7000

25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

8000

Graph 18 above charts show that the traffic didn’t change much except for LGS003 RNC

DCR improved in the two optimized RNC’s both in CS and HSDPA. Page 35

Peaks observed during optimization period correspond to some issues occurred in individual nodes impacting performance daily values.

It is remarkable how the first iteration lead CS DCR 0.2 lower.

CS DCR %

LGS002

1.2

LGS003

HSDPA DCR % LGS002

1.5

LGS003

1 1

0.8 0.6

0.5

0.4 25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd‌

0

25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd‌

0.2

Graph 19 above Charts shows that the DCR (Drop Call Rate) improved in both RNCs

Important reduction of CSFR has been achieved despite good initial values in LGS002. RRC connection and RAB establishment have improved.

Individual sites issues impacted the performance in specific days. For Baseline and Optimized week these cells have been excluded from KPI calculation

HSDPA CSFR %

CS Call Setup Failure Rate % LGS002

1.5

LGS002 LGS003

LGS003

1

0

25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

20th June

22nd June

18th June

16th June

14th June

12th June

8th June

10th June

6th June

4th June

2nd June

31st May

29th May

27th May

0.5

25th May

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Graph 20 above charts show significant reduction in Setup Failures

HS throughput shows a clear upward trend across the SON-OM executions, reaching up to 8% of improvement. Average EcNo has also been improved >0.5Db Avg CPICH ECNO dB

HSDPA Cell Throughput Kbps LGS002

LGS003

2500 2300

1900

25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

2100

LGS002

-5 -5.5 -6 -6.5 -7 -7.5 -8

LGS003

25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

2700

Graph 21 above charts show that the throughput as well as interference improved

CS IRAT HO decreased gradually during all optimization period at the same time that CS iRAT HO Failure Rate was improved.

It is important to note that IRAT HO rate is more dependent on CCLB configuration, related to the IRAT thresholds, while HO failure rate relies more on C-ANR optimization.

CS iRAT HO % LGS002 LGS003

LGS002

1.6

LGS003

1.4 1.2 1 0.6

25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

0.8 25th May 27th May 29th May 31st May 2nd June 4th June 6th June 8th June 10th June 12th June 14th June 16th June 18th June 20th June 22nd June

32 31 30 29 28 27 26 25

CS iRAT HO Failure Rate %

Graph 22 above charts show that the IRAT handover attempts decreased and the failure rate also decreased (a good sign of improvement)

Page 37

4.1.2 Capacity Enhancements This section aims to show the capacity gains that can be achieved by the CCLB algorithm. Main capacity driver considered here is Available DL Power.

Other capacity triggers such as lack of CE, lack of Iub BW or channelization codes are not considered as it assumed that the network is mature enough to have this triggers already properly dimensioned.

Data is split into two different categories: Baseline, comprising five working days from the 25th May to the 31st May 2015, and Optimized, including data from the 1st up to the 23rd June 2015.

KPI Aggregation: KPIs are calculated for each cell and later aggregated at optimization area level for each hour. This means that all the cells from each RNC are aggregated into a single value per hour for both Baseline and Optimized scenarios. Each of these values will be a point in the scatter plots that are presented in this section.

Capacity Enhancements voice

Graph 23 above: Scatter chart above: similar carried traffic observed with a significant reduction on CSFR

Many cells having hours of high traffic and high blocking in baseline while in the optimized scenario, peaks are reduced drastically.

Graph 24 above: Chart shows that the same traffic was carried by the system but with lesser power which indicated lower interference

Improved accessibility with a reduction on power consumption (9.4%) ~30% power usage at high traffic hours (optimized scenario) vs. ~ 33% (baseline scenario).

Capacity enhancements Data

15%

Graph 25 above graph shows that more traffic volume was carried (15%) showing an outstanding reduction on CSFR (%)

Page 39

CS CSFR was significantly impacted during the optimized week in LGS003 by specific sites (VI0682 & VI0031). Since overall CSFR is very low, single site issues may greatly interfere in the general plot. These nodes degradation happened on 4th June, and last SON-OM changes were implemented on 1st June, so they were excluded for KPI calculation.

High traffic hours present lower accessibility problems in optimized scenario.

15%

Graph 26 above scatter plot HSDPA traffic volume Vs HS Power used

From the trial we saw that with the same power consumption, the cells in optimized scenario are able to carry higher traffic volume roughly around (15%).

4.1.3 Drive Test Plot comparison Drive test (Field Measurement Tests) were carried out to measure the performance before and after implementation of SON. The below receive signal strength for pilot channel shows a significant improvement. SON feature for automatic RET adjustment and power control to reduce interference really improves the network performance.

Plot 27 above: RSCP Plot – Before implementing SON Poor coverage due to Outage on ULG2842, U0532 and 897GA during DT.

Poor coverage area. Integrate planned sites ULG2952, ULG4860 and ULG2729 to improve the coverage and quality at the problem area.

Plot 28 above: RSCP Plot – after implementing SON

The legend has different range for different colours, due to a post processing error but if analysed closely, the improvement still is quite significant. The below listed statistical KPI generated from the drive test logs give greater insight with numerical comparison however from the plot above we can see Page 41

that the bottom circle which has an inherent poor signal level due to a coverage hole also improved in signal level after introduction of SON. In the later section we will introduce a site in this location. A planned site is pending integration and we will allow SON to automatically set it’s RET and PCI. This will also help us measure the correctness of SON function for new site integration to the existing network.

Statistics from drive test before and after implementing SON Before

After

The above table showing pilot channel receive signal strength shows that the samples have shifted from -85 -95 range to -75 -85 range which means that the downlink signal strength has become stronger after implementation of SON.

The above table shows the similar result we saw in the table above

The above pie chart shows the pies in the signal strength ranges and we can see that the -75 to Max dBm pie chunk increased in size after SON implementation

Apart from Signal strength, one important indicator for network quality is Ec/Io which means the ratio of the received energy per chip (= code bit) and the interference level, usually given in dB. The drive test field measurement plots show the Ec/Io before and after implementation of SON.

Plot 29 above Plot shows Ec/Io before implementing SON

Poor quality / coverage due to Outage on ULG2842, U0532 & 897GA during DT.

Poor quality areas due to poor coverage. Integrate planned sites ULG2952, ULG4860 and ULG2729 to improve the coverage and quality at the problem area.

Plot 30 above Plot shows Ec/Io after implementing SON Page 43

The plot shows the dark green samples from the drive test which is an indicator of very good signal quality. The dark green lines are quite dominant after the SON was enabled. The red/orange samples are poor quality samples. We can see that the north east part of the cluster has orange samples and this was due to a site which was having an outage due to operational reasons which skewed our result. In the bottom right of the map we also see orange samples, which were due to poor signal level in this area.

The statistics generated from the drive test logs provide further information about the network quality before and after the implementation of SON.

Statistics from drive test before and after implementing SON Before

After

The above table showing Ec/Io distribution that the samples have shifted upwards i.e. which means that the interference on carrier channel has reduced after implementation of SON.

The above chart shows the same data in histogram

The pie chart above also shows that the interference has reduced in the network

In the next section we will see the impact of SON on an LTE network just as we saw the impact on 3G network.

4.2

SON RET feature trial on LTE network

4.2.1 Volte UE performance result The below charts will show us the performance of key KPI’s after implementing SON in three different types of clusters and then halting the SON and reversing the settings to original baseline.

Graph 31 above: shows the (RSRP) Reference Signal Received Power

Page 45

We can see that in the Small Island and Medium RET cluster the SON activity led to a higher RSRP whereas in the large island cluster the RSRP actually deteriorated.

Graph 32 above shows the (RSRQ) Reference Signal Received Quality

The RSRQ is nothing but (N x RSRP)/RSSI, where N is the number of RBs over the measurement bandwidth. RSSI (Received Signal Strength Indicator) contains all different sources of power which includes power from co-channel serving cells and non-serving adjacent channel cells, adjacent channel interference, thermal noise, etc. Higher the ratio, better the quality is.

We can see that in all the clusters the quality was better when the SON was active.

Graph 33 above depicts the UE average SNR (Signal to Noise Ratio) for 3 different clusters

We can see that the SNR was better for all the clusters when SON was active.

Graph 34 above shows the CQI (Channel Quality Indicator) for 3 different Clusters

The CQI shows the current channel quality on which the UE is transmitting.

In our trial on the three clusters we see that island and large cluster had better channel quality observed when SON was active. We see slight degradation on the RET cluster but this is very small.

Volte UE performance summary

Day 1 Vic. Island

Day 2 RET Cluster

Day 3 Large Cluster

LTE Mode Total Sample Time (mins) Session Setup S.R. Handover Attempts Drops LTE Mode Total Sample Time (mins) Session Setup S.R. Handover Attempts Drops LTE Mode Total Sample Time (mins) Session Setup S.R. Handover Attempts

Pre - Reversion 100.00% 209 80.55% 195 7 100.00% 237 100.00% 292 17 100.00% 454 94.80% 512

Drops

39 Page 47

Post - Reversion 100.00% 208 87.00% 222 14 100.00% 196 88.09% 195 22 100.00% 446 92.18% 449 37

4.2.2 FTP UE performance result Similar to Voice over LTE measurements we did in the last section; in this section we check the performance of FTP (File transfer Protocol) which is a form of data transfer.

Graph 35 above shows the (RSRP) Reference Signal Received Power

We can see that in all the clusters the SON activity led to a higher RSRP.

Graph 36 above shows the (RSRQ) Reference Signal Received Quality

We can see that in all the clusters the quality was better when the SON was active.

Graph 37 above depicts the UE average SNR (Signal to Noise Ratio) for 3 different clusters

We can see that the SNR was better for all the clusters when SON was active

Graph 38 above shows the CQI (Channel Quality Indicator) for 3 different Clusters

The CQI shows the current channel quality on which the UE is transmitting.

We see that all the clusters had better channel quality observed when SON was active.

Page 49

Graph 39 above shows the FTP user equipment Throughput for 3 different Clusters

We can see that the throughput improved in all the clusters when SON was on except for the RET cluster.

FTP UE performance summary

Day 1 Vic. Island

Day 2 RET Cluster

Day 3 Large Cluster

LTE Mode Normalized DL Throughput (Mbps) Total Sample Time (Min) Session Setup S.R. Handover Attempts Drops LTE Mode Normalized DL Throughput (Mbps) Total Sample Time (Min) Session Setup S.R. Handover Attempts Drops LTE Mode Normalized DL Throughput (Mbps) Total Sample Time (Min) Session Setup S.R. Handover Attempts Drops

Pre - Reversion 100.00% 3.865 154 100.00% 119 7 100.00% 2.552 237 100.00% 292 17 100.00% 4.737 451 93.18% 308 27

Post - Reversion 100.00% 2.337 199 93.75% 162 3 100.00% 3.081 196 88.09% 195 22 100.00% 3.771 448 96.55% 348 22

4.2.3 SON Load balancing feature trial Phase 1 (OpenLoop) LB on Small and Large Cluster – week 27 SON active

Phase 2 (OpenLoop) LB + RET on Small Cluster, LB on Large Cluster – week 29 SON active

Phase 3 (OpenLoop & CloseLoop) Small Cluster – week 30 SON active Large Cluster – week 30 SON active Site Specific – week 30 SON ctive

Map 40 above: shows the clusters where the load balancing trial took place

4.2.3.1

KPI Performance Phase 1 Result

Improvement observed in DL User Throughput with no reduction in traffic.

DL Throughput increased by 2.6% No KPI degradation has been observed within the cluster Load balance algorithm demonstrated at cell level - worked as expected

Page 51

KPI Week 26 week 27 Changes % DL Throughput 4.02 4.12 2.60% UL Throughput 0.316 0.32 0.98% ERAB Estb. Success Rate 99.59 99.58 -0.01% Retainability 99.85 99.86 0.01% HO Success Rate 96.31 96.21 -0.10% CQI 7.4 7.39 -0.14% Table 41 above shows the performance of a small and Large cluster after SON was switched ON

We can see that there was slight improvement in the downlink throughput CIO Distribution

Graph 42 above: CIO distribution

3.73% of the total number of adjacencies is different to the default value. We can see that after SON optimization there are more number of adjacent neighbours available for handover purposes.

4.2.3.2

KPI Performance Phase 2 Result

No degradation noticed on week 29 with LB + RET on Small Cluster and LB on Large Cluster. Improvement was observed in DL and UL user throughput with no reduction on traffic. SON had positive impact on PDCCH utilization and normalized CQI Rank 1 and Rank 2. Improvement observed in PRB utilization (DL and UL). Better retainability observed with higher RRC reestablishment success rate. Large Cluster KPIs remained roughly constant on cluster level.

KPI Week 28 Week 29 Changes % Small Cluster Init Erab Est SR 99.85 99.84 0.0% Samall Cluster Retainability 99.81 99.79 0.0% Small Cluster RRC Reestab SR 35.20 35.76 1.6% Small Cluster DL Throughput 5.38 5.69 5.8% Small Cluster UL Throughput 0.31 0.32 2.6% Small Cluster Intrafrequency SR 95.44 95.69 0.3% Small Cluster DL PRB Util 23.10 21.09 -8.7% Small Cluster UL PRB Util 7.20 7.05 -2.1% Small Cluster PDCCH Util 15.29 15.07 -1.4% Small Cluster CQI Dist 6.87 6.95 1.3% Small Cluster CQI Dist R2 7.15 7.24 1.3% Small Cluster AVG RRC conn user 23.60 23.22 -1.6% Small Cluster DL Volume 129317.90 125017.10 -3.3% Small Cluster UL Volume 14779.58 14947.71 1.1% Table 43 above shows the performance of a small and Large cluster after SON was switched ON

We can see some KPI’s improved after SON implemented in the cluster. The largest gain was observed in downlink throughput.

Page 53

4.2.3.3

KPI Performance Phase 3 Result

Large Cluster

KPI

Accessi bility

Retain ability

Covera ge

Traffic

Resour ce Usage

Avg. RRC Connected users Avg. RRC Connection Attempts Avg. Initial E-RAB Establishment Succ (%) Avg. Retainability (Drop Rate %) Avg. RRRC reestablishment succ rate (%) Avg. CQI Normalized Distribution Avg. CQI Normalized Distribution (Rank 2) Avg. UE Downlink Throughput (Mbps) Avg. UE Uplink Throughput (Mbps) Avg. Cluster Downlink Volume (MB) Avg. Cluster Uplink Volume (MB) Avg. Downlink PRB Utilization (%) Avg. Uplink PRB Utilization (%) Avg. eNB Scheduled Transmit Diversity (%) Avg. eNB Scheduled MIMO Percentage (%) Avg. PDCH Utilization (%) Avg. PDCH High Load (%) Avg MP Load Distribution (%)

Number of top achievements in each week

Week 30 SON Featu re active

Week 31 SON Froze n

Week 32 Networ k original configu ration

28 34,36 0,698

29 32,84 5,845

28 35,012, 953

99.89

99.89

99.86

0.14

0.15

0.16

Higher average UE connected on Week 31 with SON frozen Significant increase on RRC Connection Attempts on Reversion Week Higher success rate noticed on week where SON was configuring NW Lower Drop Rate noticed with SON feature enabled on week 30

36.09

35.73

35.29

Higher RRC Restablishment rate with SON feature active

7.36

7.24

7.25

Higher CQI observed with SON

7.84

7.74

7.73

4.55

4.05

3.92

0.27 45,51 6,780 5,410, 228

0.27 45,92 8,133 5,542, 605

0.26 50,042, 003 5,736,0 58

Higher CQI Rank 2 observed with SON Higher UE Throughput with Son feature enabled Higher UE Throughput with Son feature enabled

23.33

25.97

25.23

Higher resource use with SON

7.43

7.77

7.58

Higher resource use with SON

66.36

67

66.73

33.64

33

33.27

14.76

15.97

15.49

0.7

0.95

0.94

10.37

10.4

10.4

Higher TXD Usage on week 31 higher MIMO usage with SON feature enabled Higher PDCCH Utilization on week 31 with SON frozen Lower PDCCH utilization and higher UE Throughput on the same week Lower PDCCH utilization and higher UE Throughput on the same week

9

7

4(2)

More achievements noticed with SON feature enabled

Comments

Increasing trend on cluster volume Increasing trend on cluster volume

(2) means achievements on Reversion week if volume metrics are excluded

SON Feature Active, week 30: July 20th – July 26th 2015 SON Feature Frozen, week 31: July 27th – Aug 2nd 2015.

SON feature and configuration removed, week 32: Aug 3 rd – Aug 9th 2015 Small Cluster

KPI

Access ibility

Retain ability

Covera ge

Traffic

Resour ce Usage

Avg. RRC Connected users Avg. RRC Connection Attempts Avg. Initial E-RAB Establishment Succ (%) Avg. Retainability (Drop Rate %) Avg. RRRC reestablishment succ rate (%) Avg. CQI Normalized Distribution Avg. CQI Normalized Distribution (Rank 2) Avg. UE Downlink Throughput (Mbps) Avg. UE Uplink Throughput (Mbps) Avg. Cluster Downlink Volume (MB) Avg. Cluster Uplink Volume (MB) Avg. Downlink PRB Utilization (%) Avg. Uplink PRB Utilization (%) Avg. eNB Scheduled Transmit Diversity (%) Avg. eNB Scheduled MIMO Percentage (%) Avg. PDCH Utilization (%) Avg. PDCH High Load (%) Avg MP Load Distribution (%)

Number of top achievements in each week

Week 30 SON Featu re active

Week 31 SON Froze n

Week 32 Networ k original configu ration

23 4618 822

24 4384 581

23 47697 65

99.85

99.85

99.86

0.21

0.22

0.21

36.59

37.17

35.72

6.88

6.7

6.75

7.22

7.04

7.06

4.07

3.6

3.49

0.15 6139 416 7118 43

0.16 6361 393 7214 95

0.18 39673 83 73201 5

21.8

25

24.31

Higher resource use with SON

7.18

7.46

7.23

Higher resource use with SON

68.53

69.84

68.46

31.47

30.16

31.54

15.28

16.62

15.93

1.04

1.4

1.15

10.19

10.07

10.06

Higher TXD Usage on week 31 higher MIMO usage with SON feature enabled Higher PDCCH Utilization on week 31 with SON frozen Lower PDCCH utilization and higher UE Throughput on the same week Lower PDCCH utilization and higher UE Throughput on the same week

7 (5)

More achievements noticed with SON feature enabled if cluster volume is excluded

5

6

Comments

No degradation Higher attempts after reversion 0.01% higher E-RAB Establishment Succ on reversion week No degradation noticed with SON feature enabled Higher RRC Restablishment Success Rates on weeks with SON Higher CQI noticed with SON feature enabled Higher CQI Rank 2 noticed with SON feature enabled Higher UE throughput noticed with SON feature enabled Increasing trend on UE UL Throughput weekly Increasing trend on cluster volume Increasing trend on cluster volume

(5) means achievements on Reversion week if volume metrics are excluded

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Cumulative distribution of performance Graphs

Graph 44 above: RET Cluster VoLTE UE Pre/Post RSRQ distribution

Graph 45 above: RET Cluster VoLTE UE Pre/Post RSRP distribution

We can see that the post SON RSRQ and RSRP improved in all the clusters.

Graph 46 above: RET Cluster VoLTE UE Pre/Post SINR distribution

4.2.4 New site introduction We will see the effect of RET using SON on a newly introduced site.

Map 47 above:shows the RSCP coverage heat plot and the new site without its coverage

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Map 48 above shows the new site and its neighbours

The algorithm detected high overlap between the neighbor cells (SON active)

SON deactivated.

Last settings retained

Table 49 above: shows the electrical tilt before SON activated and after it was started

We can see that the site started with zero degree RET (Remote electrical tilt) but the RET feature in SON adjusted the electrical tilt considering the neighbouring cell overlap and interference during high traffic time. This shows how effective SON could be during integration of new sites. It helps automatically adjust antenna tilt without any human intervention.

4.3

GSM Spectrum Management using SON

Graph 50 above shows the spread of BCCH and TCH ARFCN’s in the network.

BCCH Total ARFCN

TCH

Spectrum segment Total ARFCN Spectrum segment

900 MHz

14

76-89

10

90-99

1800 MHz

10

662-671

64

672-735

Table 51 above shows the frequency spectrum allocated to be used in the network

Graph 52 above shows the gain in interference reduction achieved by adhoc planning the MAIO and MAL ARFCN’s

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4.3.1 KPI Performance Analysis SON activated SON activated

Graphs 53 above: Percentage Bad quality SQI has improved after AFP implementation. This directly relates to better speech quality and subscriber experience

SON activ ated

SON activ ated

Graphs 54 above: Percentage Drop on Call Setup and Total Number of Drop Count reduced after SON. Percentage Bad Interference Channel Measurement also reduced after SON

4.3.2 Drive test KPI Plots Comparison

Plot 55 above: Receive Quality plot before SON was activated

Plot 56 above: Receive Quality plot after SON was activated

We can see that in many areas the signal quality improved after SON was activated and it did the automatic MAIO and MAL adjustments.

Learnings and Recommendations

5 5.1

SON PCI problems

The following SON PCI related issues were seen during the trial.

1) PCI conflict with 2nd Neighbor cell in other OSS-RC – During our trial when site 2952 was added to the network. The Physical cell identification was allowed to be done by the SON. The PCI allocated a cell id which already existed in the network but in a different OSS (Operation Sub System). It was realized that 2nd order neighbor cell was not seen to be taken into account for SON PCI calculations. In automatic mode this can result in a problem which could trigger a domino effect

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when a big number of new sites are supposed to be integrated. This needs to be checked and configured separately.

Fig 57 above shows how the SON allocated the same cell id of a second order neighbour

2) No consideration of RF shift in Automatic Mode – PCI calculation in automatic mode did not consider subcell ID conflict in the same site. It should have broken the SBM PCI allocation policy but it didn’t. The problem was reported to the relevant vendor for a correction patch.

Fig 58 above: shows the PCI calculation problem with SON self-configuration module

SON-OM doesn’t guard KPI thresholds at individual cell level while executing the optimization algorithms. The level of optimization and KPI improvement

depends on different variables. Following could be some of the reasons if results don’t show significant improvement.

1. The existing congestion in Radio Network resources could result in higher KPI improvements.

2. The size of the cluster also matters and RET was used only for small number of sites in the trial network. RET optimization of around 15 sites is not very useful to test the algorithm. There is concern that UE measurements impact battery life there is a need for better antenna electrical tilt control for maintaining its impact on performance

In the same way as Load balancing, ANR and RET works, a similar approach can be followed if any NodeB suffers an outage, the antenna tilts and coverage overlaps of the neighbouring cells can be adjusted to effectively compensate for the coverage holes that are created. This could not be tested in the trial due to unavailability of permission to halt a site.

5.2

Multivendor Issues observed

Multivendor SON is difficult to implement and to get the expected gains due to various factors.

1. Every vendor systems provides different granularity to set up the value of certain configuration parameters, e.g. some vendors allows the adjustment of power parameter settings, such as the CPICH, with increasing and decreasing steps of 0.1 dB, whereas others only allow this in steps of 0.5 dB or even higher.

2. Only some vendors has specific counters for network events such as dropped call reasons, bad quality reasons and bad received signal level, etc. which helps in identifying the most probable cause of the problem and therefore effectively leading to core identification process of the root cause.

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3. The utilization of the PRACH channel is very important when we are dealing with 3G Cells with very large coverage footprints. Some vendor systems does not have such measurements in their OSS and hence this information has to be collected using a UE trace. This leads to extra complexity and extra data collection and different processing mechanism.

4. In an event of a system crash, when the system fails to recover, many vendor equipment has an internal process to restart. In these cases, the SelfHealing function of the SON schedule the changes based on its own algorithm and many times restart function is not considered. This leads to multiple restart or multiple cv uploads.

These examples make it clear that multi-vendor support and flexibility is key requirements for SON systems, which will need to read input information in different formats, carry out analyses that consider vendor-specific implementations and generate corrective actions that are in line with the mechanisms and limitations of the equipment supplied by different infrastructure vendors.

6

Conclusion In this dissertation project, we had an overview of SON offerings in

advanced wireless networks such as Long Term Evolution (LTE) and 3G UMTS systems. We also did a test for its functionality in GSM network.

Although SON showed huge benefits towards having optimum mobile network quality but the benefits come with challenges in making them a reality.

We analysed the working of SON in a multi technology network Key features such as Intra frequency Load balancing, RET optimization (open loop and closed loop) ANR optimization and GSM spectrum management worked well but there were some problems observed with LTE

PCI function. Things such as reliability, parameters conflicts, data measurement and processing, algorithms development and policy tuning and goals must be taken into consideration before implementing self-organization concepts in practical network.

We saw a lot of improvements in the network when SON was activated but there were also glitches which required human intervention for corrections. The results show that SON approach is very effective for dense network deployment however this is still a very new technology and the networks are so complex that smooth automation will take a lot of fine tuning of the system. So far the optimization engineers role still looks like will be required, not to do the extensive work on the system but just to keep an eye on the SON’s working, behavior and the correctness of the expected output.

From the Network operators perspective, this new technology definitely adds value and saves time in dealing with quality issues in real time. All what is required is to fine tune the policy management part of the SON and ensure that exceptions are handled well. There can always be cost savings due to less human intervention in maintaining the quality of the network but there is more to gain by the efficient working of the system than by the isolating the cost of human resource alone.

The main benefit of C-ANR is captured with Automation of timeconsuming tasks; which will result in OPEX improvement. There is also potential improvement in customer perceive quality by reducing drop call rate (DCR) which will result in increased revenue. The operator while choosing the SON solution should emphasise on its flexible architecture and robust algorithms.

References 1. Østerbø, O. and Grøndalen, O. (2012), ‘Benefits of Self-Organizing Networks (SON) for Mobile Operators’, Journal of Computer Networks and Communications, Vol. 2012, Article ID 862527. Available at www.hindawi.com/journals/jcnc/2012/862527/ Page 65

2. NEC Corporation. (2009), “Self-Organizing Networks (SON)”, Journal White Paper. Available at www.nec.com/nsp/lte 3. Juan Ramiro (ed.), Khalid Hamied (ed.), (2012) Self-organizing networks : self-planning, self-optimization and self-healing for GSM, UMTS and LTE, TK5103.2.S46 2011, John Wiley & Sons Ltd 4. Nokia Networks White Paper, Product code C401-011915-WP201508-1-EN, Nokia Solutions & Networks, http://networks.nokia.com/file/34496/lte-m-optimizing-lte-for-theinternet-of-things.

5. Self-Optimizing Networks- Removing Risk and Adding Value, Tim McElligott, Frost & Sullivan, February 2014, http://www.frost.com/sublib/display-report.do?id=D532-00-B2-00-00 – Accessed on May 2015 6. Zhang Congling & Xie Guozhu, 2003, Maximizing Spectral Effeciency: Huawei Technologies, http://www1.huawei.com/en/static/HW080431.pdf

Abbreviation 2G 3G 3GPP AFP AMR ANR ARFCN ARPU ATM BCCH BSC BTS CANR CCLB CE CIO

Second Generation Third Generation 3rd Generation Partnership Project Automatic Frequency Planning Adaptive Multi Rate Automatic Neighbour Relation Absolute Radio Frequency Channel Number Average revenue Per User Asynchronous Transfer Mode Broadcast Control Channel Base Station Controller Base Transceiver Station Centralized Automatic Neighbour Relation Centralized Closed loop Load Balancing Channel Elements Cell Individual Offset

CPICH CQI CS CSFR DCR DHCP DL DUG DWDM EIGRP FLP FTP GSM HFR HO HSDPA HSN HSPA ICM IP IRAT KPI LTE MAIO MAL NE NEM NGMN NPV O&M OMC OPEX OSPF OSS PCI PDCCH PDH PNNI PSC QoS RAB RAN RBS

Common Pilot Channel Channel Quality Index Circuit Switched Call Setup Failure Rate Drop Call rate Dynamic Host Configuration Protocol Downlink Digital Unit for GSM Dense Wavelength Division Multiplexing Enhanced Interior Gateway Routing Protocol Fractional Load Planning File Transfer Protocol Group Special Mobile Handover Failure Rate Hand Over High Speed Downlink Packet Access Hopping Sequence Number High Speed Packet Access Interference Channel Measurement Internet Protocol Inter Radio Access Technology Key Performance Indicator Long Term Evolution Mobile Allocation Index Offset Mobile Allocation List Network Elements Network Monitoring Next Generation Mobile Network Net Present Value Operations and Maintenance Operations and Maintenance Centre Operational Expenditure Open Shortest Path First Operation Support System Physical Channel Index Packet Data Channel Plesiochronus Digital Hierarchy Private Network to Network Interface Pseudo Scrambling Code Quality of Service Radio Bearer Access Radio Access Network Radio Base Station Page 67

RET RNC ROI RRC RRM RSRP RSRQ SDH SFH SHO SIU SON SQI TCH TDM TRX TX TXPWR UARFCN UE UL UMTS

Remote Electrical Tilt Radio Network Controller Return On Investment Radio Resource Control Radio Resource Management Reference Signal Receive Power Reference Signal Receive Quality Synchronous Digital Hierarchy Synthesizer Frequency Hopping Soft Handover Site Integration Unit Self Organizing Network Speech Quality Index Traffic Chanel Time Division Multiple Access Transceiver Transmission Transmit Power UMTS Absolute Radio Frequency Channel Number User Equipment Uplink Universal Mobile Telecommunication Systems

Bibliography

1. 3GPP Work Items on Self-Organizing Networks v.0.0.6, Oct. 2010.

2. 3GPP TR 36.902 v9.2.0, “Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Self-Configuring and Self-Optimizing Network (SON) Use Cases and Solutions (Release 9),” 2010.

3. LTE-M – Optimizing LTE for the Internet of Things, Nokia Networks White Paper, Product code C401-011915-WP-201508-1-EN, Nokia Solutions & Networks, (Accessed on July 2015), http://networks.nokia.com/file/34496/lte-m-optimizing-lte-for-theinternet-of-things

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5. Centralized SON (C-SON) is paving the way to true zero touch networks, Infonetics Research, (Accessed on July 2015), http://www.infonetics.com/pr/2015/C-SON-White-Paper-Highlights.asp 6. Self Organizing Network, “NECs proposals for next generation radio network management”, Feb 2009 NEC Corporation, (Accessed on July 2015), http://pt.nec.com/pt_PT/en/global/solutions/nsp/lte/pdf/son.pdf

7. NGMN – Advancements in LTE optimized networks – New white paper, by Vicki Livingstone, July 19 2009 (Accessed in July 2015), https://www.ngmn.org/news/archive/archive-details/article/herecomes-the-son.html

8. COST 231 (1999) Final Report http://www.lx.it.pt/cost231/

9. Erik, A. and S. Holm, S. (2004) Tuning of Empirical Radio Propagation Models Effect of Location Accuracy,

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Wireless Personal Communications, 30, pp. 267–281, Kluwer Academic Publishers, Dordrecht.

10. Juan Ramiro (ed.), Khalid Hamied (ed.), (2012) Self-organizing networks : self-planning, self-optimization and self-healing for GSM, UMTS and LTE, TK5103.2.S46 2011, John Wiley & Sons Ltd. 11. The benefits of Self Organizing Backhaul network, September 27 2013, Ericsson Review, (Accessed on August 2015), http://www.ericsson.com/res/thecompany/docs/publications/ericsson_r eview/2013/er-son-transport.pdf

12. Handover Parameter Optimisation in LTE Victor Ermolayev, professor, technical expert, MERA, Russia, (Accessed in May 2015), http://www.mera.com/sites/default/files/Handover%20Parameter%20O ptimisation%20in%20LTE.pdf 13. White paper for mobile operators – e SON, Airhop Communication Inc. Feb 2013, (Accessed in May 2015), http://www.airhopcommweb.com/ah/WhitePaper/MWC13_SON_for_carriers_final.pdf

14. Journal of Computer Networks and Communications Volume 2012 (2012), Article ID 862527, 16 pages http://dx.doi.org/10.1155/2012/862527, (Accessed in May 2015) 15. The evaluation of SON to Extended SON, John M. Graybeal and Kamakshi Sridhar, (Accessed in July 2015), http://onlinelibrary.wiley.com/doi/10.1002/bltj.20454/pdf

16. Key Takeaways of Successfully managing SON, Astellia, (Accessed in June 2015), http://www.astellia.com/en/key-take-aways-forsuccessfully-managing-SON.

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