INTRODUCTION
B
y 1992, many European countries hadoperational second generation (2G) global system for mobile communication (GSM) systems, and GSM started to attract interest worldwide. GSM proved to be a major commercial success for system manufacturers and network operators, many of which enjoyed exponential growth until the end of the decade. The most valuable and limited resource of GSM is the available frequency spectrum, which limits the system capacity. The successful take-up of GSM services led to continuous development of sophisticated algorithms to maximize the system capacity. This caused a substantial technological evolution of GSM, with annual (and often biannual) releases of new functionality, which have increased the complexity of the system. The evolution of the Ericsson™ “locating” algorithm, which is very capacity-efficient but quite complex, with several operators unable to fully exploit the benefits of this functionality, is an example. Underutilization of available functionality, coupled with an exponential increase in subscriber numbers, resulted in many operators overdimensioning their base station subsystems (BSSs) with continuous aggressive deployment of new base stations. Thus, constant change and evolution of GSM networks have necessitated the continuous optimization of the offered quality of service (QoS).
Many publications on GSM describe the system, its architecture, and its evolution. However, limited sources document QoS, network performance management, and optimization. Many European
performance, and the industry has developed GSM optimization expertise (mainly through trial and error), but this expertise is not fully documented. There is usually more than one solution to a prob-lem, which (unlike for design or site acquisition) makes it difficult to proceduralize optimization techniques and problem solutions. Engineers need to be open-minded, with good analytical skills and good understanding of the overall system and its individual components. Performance management and QoS optimization are subjects that cannot be fully taught. Expertise must be gained through trial and error, in an attempt to maintain optimum and constant QoS offered by dynamic and ever-changing GSM networks.
This paper focuses on 2G QoS, as well as the advantages and disadvantages of each mecha-nism available to monitor, analyze, and improve it. The paper also describes the most common QoS shortfalls and provides improvement recommendations, which serve as a useful reference in performance analysis and optimi-zation for specific projects.
WHAT IS QUALITY OF SERVICE?
O
verall QoS for 2G, 2.5G, and 3G systems comprises three important components, all of which need to be constantly monitored and optimized as networks change in response to increasing coverage and capacity demands:• Accessibility – getting on the system • Retainability – staying on the system • Connection quality – having a good service
Michael Pipikakis
[email protected]EVALUATING AND IMPROVING THE
QUALITY OF SERVICE OF
SECOND-GENERATION CELLULAR SYSTEMS
EVALUATING AND IMPROVING THE
QUALITY OF SERVICE OF SECOND-
GENERATION CELLULAR SYSTEMS
Abstract—This paper provides an insight into network performance management and quality of service (QoS) of matured second generation (2G) cellular systems (after the pre-/post-launch testing and optimization phase). It identifies the components of QoS and the available mechanisms to analyze and evaluate them. The paper also identifies important key performance indicators (KPIs) that need to be monitored and optimized and provides a way to collect and classify data for analysis. Finally, the most common QoS shortfalls and possible solutions are discussed.
Issue Date: September 2004
Issue Date: September 2004
QUALITY OF SERVICE EVALUATION
T
he three mechanisms available to monitor, analyze, and evaluate QoS and take corrective actions are customer complaints, drive tests, and network statistics, all three of which are described below. Each mechanism has certain advantages and disadvantages, usually with conflicting priorities for limited optimization resources. Customer ComplaintsAdvantages
• Real problems experienced by customers using the service
• Decision-forming/influential Disadvantages
• Subjective
• Often vague with little supporting data • Often received too late to react to the
situation
• Require filtering by customer service before being handled by the engineering
department Drive Tests Advantages
• Real calls
• Cause of failure can be identified • Good for benchmarking
• Good for network pre-launch tuning (startups and new deployment projects) Disadvantages
• Low volumes/statistically insignificant • One terminal type
• Only ground level and in-car service • Predetermined routes, calling patterns only • Labor-intensive analysis
Network Statistics Advantages
• All calls can be monitored
• Trends can be measured, by specific geographical areas of interest or for the entire network
• Trends are stable Disadvantages
• Indicate problems but not their causes or solutions
• Do not differentiate customer value ABBREVIATIONS, ACRONYMS, AND TERMS
2G second-generation
3G third-generation
BCCH broadcast control channel
BH busy hour
BL both links
BLR block error rate
BSC base station controller
BSIC base station identity code
BSS base station subsystem
BTS base transceiver station
CCSR call completion success rate
CFR congestion failure rate
CI cell identification
CRH cell reselect hysteresis
CRO cell reselect offset
CS circuit switched
CTR cell traffic recording
DCR dropped call rate
DL downlink
DTCHR dropped traffic channel rate
DXC digital cross connect
EIR equipment identity register
GPRS general packet radio service
GSM global system for mobile
communication
HSN hopping sequence number
HSR handover success rate
KPI key performance indicator
LAC location area code
LAPD link access protocol on the D-channel
MHT mean holding time
MSC mobile switching center
OFTEL Office of Telecommunications
PS packet switched
QoS quality of service
RF radio frequency
RTT roundtrip time
SDCCH standalone dedicated control channel
SDCCHSR SDCCH success rate
SMS short message service
TA timing advance
TBF temporary block flow
TCH traffic channel
Established GSM operators use clearly defined network QoS key performance indicators (KPIs) with target thresholds to be achieved. The KPI thresholds are usually revised once a year, and new goals are set as the business priorities change. Network performance management and optimization activities ensure that QoS targets are met.
For underperforming areas (sections of the network failing the KPI thresholds), optimization projects are initiated. Using all available methods, these projects fully analyze the performance of the area to understand the problems and take corrective actions. In such optimization projects, a combination of customer complaints, drive tests, and network statistics is used. Usually, statistical analysis and customer complaints are used to identify problems, while drive tests are used to verify them and/or the solution(s). However, drive tests alone cannot be relied on to provide insight into the offered service. Drive tests can only provide an indicator of QoS for traffic that is highly mobile and at ground level. A large proportion of traffic offered via mature networks is static and often originates at higher-than-ground levels. In several mature European networks there is, on average, only one handover per call, which indicates the static nature of traffic. This makes statistics the most useful mechanism for identifying QoS shortfalls. However, experience is required in recognizing problem trends, identifying the causes, and taking corrective actions. This, in turn, requires good knowledge of the system, analytical skills, and experience in network performance management and optimization. Nevertheless, using statistical analysis properly and to the fullest extent possible can significantly improve QoS.
WHAT NEEDS TO BE MONITORED AND OPTIMIZED?
T
he trends of several KPIs must be closely monitored. A summary of the most important KPIs that can have an impact on the offered QoS follows.Circuit Switched (CS) – Voice
• DCR: The dropped call rate (DCR) provides the customer-perceived dropout perfor-mance. It is calculated over an area of the entire network or a geographical area and not on a per-cell basis, because a call cannot be statistically related to just one cell, due to handovers.
• Minute-Erlang/Drop: This KPI indicates the average time between dropped calls. It is a division of traffic expressed in minute-Erlangs divided by the total drops and is inversely proportional to DCR. It is a good way to evaluate the effectiveness of optimization activities because it takes into account the carried traffic and is more sensitive to changes than DCR.
• CFR: The congestion failure rate (CFR) indicates the failure rate of assignments due to congestion and can be used on a cell basis for engineering, planning, and trouble-shooting purposes and on an area basis to provide a measure of the customer-perceived traffic congestion. GSM operators have developed sophisticated CFR formulas to account for the effects of features such as directed retry and cell load-sharing when measuring customer-experienced congestion.
• CCSR: The call completion success rate (CCSR) can be derived either from network statistics or from drive test statistics. It takes into account the fact that all failures are either drops or unsuccessful call set-ups. The total number of failures is divided by the total number of call attempts. It is a good method to use to evaluate the network accessibility and retainability as perceived by the customers. In the United Kingdom, the Office of Telecommunications (OFTEL), a governing body, uses CCSR from drive tests to declare the best network for QoS. Every 6 months, all network operators make approximately 22,000 calls while driving 305 pre-defined routes with clearly defined call patterns. At the end of the cycle, the operators submit a summary of the results and all drive-test files to OFTEL [1].
• DTCHR: The dropped traffic channel rate (DTCHR) indicates the drops at the cell level. It is used for engineering purposes only (and not for reporting), to identify cells with high drops. Optimizing these cells improves DCR and CCSR.
• SDCCHSR: The standalone dedicated control channel success rate (SDCCHSR) indicates the rate of successful air interface signaling channel assignments and is used for engineering purposes only, to optimize cells with high failure rate. Optimizing such cells improves CCSR.
• HSR: Handover success rate (HSR) indicates the success of handovers. Minimizing handover failures improves DCR.
Performance
management and
QoS optimization
cannot be fully
taught. Expertise
must be gained
through trial
and error.
Packet Switched (PS) – Data (GPRS)
• Cell Throughput: Cell throughput is an end-to-end KPI used at the cell and network levels to indicate data throughput.
• RTT: Decreasing roundtrip time (RTT) delay increases throughput.
• TBF Multiplexing: Temporary block flow (TBF) multiplexing indicates the number of users per time slot usage of general packet radio service (GPRS) resources. A high number of users per time slot decreases the data throughput.
ORGANIZING STATISTICAL DATA PRIOR TO ANALYSIS
A
s shown in Figure 1, performance andconfiguration data are collected in the switching nodes and usually aggregated into a statistics database and a configuration database, respectively. The statistics database is divided into “object types,” which correspond to different equipment or system function blocks. Each object type contains several event counters. The basic time unit for data collection is 15 minutes, i.e., the base station controller (BSC) uploads the entire object counter data to the statistics database every 15 minutes. Proposed methods for organizing and classifying the available data follow.
Observation Time Intervals
When manipulating statistical data, it is important to define appropriate time frames within which the data will be gathered and processed. The following observation time intervals are suggested for statistical evaluation:
• Hour: Hourly statistics give a detailed picture of network performance. They are useful to help spot temporary problems and identify trends.
• Peak Hour: Peak hour statistics are of great significance, because they correspond to the time of heavy utilization of network resources. In a way, they provide the “worst-case” scenario.
• Day: Daily statistics are introduced to provide a way of averaging temporary fluctuations of hourly data. Problems can be identified and corrective actions triggered with more confidence. Trends with daily values are also used for reporting and benchmarking.
• Online: Online statistics provide almost real-time monitoring of the network, if this is necessary. Statistics can be obtained directly from the switching node, where outputs are available every 15 minutes.
Classification by Network Level
As shown in Table 1, the monitoring process and statistical analysis take place at different levels:
• Network-wide: The entire network (to provide a “global” overview)
• Geographical Area or Region: All cells belonging to specified geographical regions (to obtain and compare results for performance in different areas)
• City: All cells belonging to specified major cities (to obtain and compare results for performance in different cities)
• BSC: All cells belonging to certain switching nodes (to obtain switching node-related statistics and compare performance of different nodes)
• Cell: Individual cells as well as neighboring cell relationships
Classification by Resource Type or Event
Statistics can be classified by resource type or the events they refer to. Both user-defined formulas and “raw” counters are grouped into one of the following categories:
• Random access channel measurements • Standalone dedicated control channel
(SDCCH) measurements • TCH measurements • Idle channel measurements • Handover measurements
• Subscriber disconnection measurements • Link access protocol on the D-channel
(LAPD) signaling measurements MSC BSC MSC BSC MSC BSC SMS EIR Performance Evaluation X.25 Network DXC Crossconnect Performance
CAUSES OF CERTAIN QoS SHORTFALLS AND POSSIBLE SOLUTIONS
A
lthough the most common QoS shortfallsand suggested possible higher level solutions are discussed, a detailed description of the functionality to be fine-tuned and parameter settings is beyond the scope of this paper. Because coverage, spectrum utilization, and traffic load differ from one area to another and from one network to another, engineers must determine optimized parameter values for a specific area of a network.
Accessibility Optimization SDCCH Congestion
• Causes
SDCCH availability, high number of location updates, high level of short message service (SMS) traffic, high number of call set-up bids • Action
– Check historical statistics of SDCCH
availability. In some systems, time slots may go into sleep mode. Historical data can show if certain time slots are constantly idle. If this occurs over a long period of
(BH), a base transceiver station (BTS) restart and retest validation may be required.
– Check for high number of location updates,
call set-ups, and SMS traffic. Increasing the cell reselect hysteresis (CRH) will delay GPRS reselection. It might be wise to expand SDCCH resources, if possible. This can be done at the expense of one TCH, which can be converted to eight SDCCHs. It is advisable to aim for no SDCCH congestion at all times.
TCH Congestion • Causes
TCH availability, missing neighbors, missing assignments in neighbor list, traffic distribution
• Action
– Check TCH availability. TCH time slots
may go into sleep mode. Real-time data can show if certain time slots are constantly idle. If this occurs over a long period of time and especially during the BH, a BTS restart and retest validation may be required.
Level
KPI
Target RangePerformanceQoS Attributes
Entire Network
CCSR from Drive Tests 97–99.5% Accessibility/Retainability CCSR Calculated 98–99.5% Accessibility/Retainability Minute-Erlang/Drop 100–250 min. Retainability
DCR 0.5–2% Retainability
Half-Rate Traffic 0–30% Speech Quality Silence/One-Way Transmission 0–1% Speech Quality
SDCCHSR 98–99.9% Accessibility
Area or Region
CCSR from Drive Tests 97–99.5% Accessibility/Retainability
DCR 0.5–1.8% Retainability
Minute-Erlang/Drop 100–250 min. Retainability
BH CFR 1–4% Accessibility
SDCCHSR 98–99% Accessibility
Major City
CCSR from Drive Tests 97–99.5% Accessibility/Retainability Half-Rate Traffic 0–20% Speech Quality Silence/One-Way Transmission 0–1% Speech Quality Minute-Erlang/Drop 100–250 min. Retainability
SDCCHSR 98–99.9% Accessibility DCR 0.5–1.5% Retainability BH CFR 0.5–2% Accessibility All BSCs DCR 0–1.5% Retainability BH CFR 0.5–1.5% Accessibility/Retainability Silence/One-Way Transmission 0–1% Speech Quality
SDCCHSR 98.5–99.9% Accessibility
All Cells
% of Cells with Dropped TCH >2% 5–10% Retainability % of Cells with BH CFR >10% 5–15% Accessibility
% of Cells with HSR <95% 2–10% Accessibility/Retainability % of Cells with SDCCHSR <95% 1–5% Accessibility
– Check for cell mean holding time (MHT)
and compare it with that of the surrounding cells in the area. Greater MHT may be due to missing or incorrect neighbor cell definitions. Check the radio plan for missing neighbor cell assignments.
– Use traffic management (load shedding)
techniques that force traffic originating near the cell border to the surrounding cells. This can be achieved with optimum use of capacity-efficient features such as directed retry, cell load-sharing (traffic reason handover or changing the handover hysteresis parameters), and handover offset between two neighbor cells.
– In a hierarchical cell structure, distribute
traffic to lower or higher cell levels as required, using layer threshold and layer threshold hysteresis.
– Redistribute traffic among cells within the
same layer, using early handover from a congested cell to another cell. This can be accomplished by adjusting handover hysteresis and handover offset.
Note: The traffic distribution actions mentioned above will improve GPRS performance. They will reduce TBF multiplexing and the number of PS immediate assignment rejections and will also increase GPRS throughput.
Retainability and Quality Optimization Deterioration of Performance with Sudden Increase in the Number of TCH Drops
• Causes
Hardware problem, handover problem • Action
– Check historical statistics of TCH
availability. Check if there are any alarms on the cell or the transceiver or any of the TCH time slots.
– Check historical handover performance
for the cell. If some external neighbor cells (belonging to a different BSC or mobile switching center MSC) show no successful hand-overs, but only attempts, missing or incorrect handover definitions on the parent BSC or MSC could be the reason.
– Check whether any neighbor cells have
been deleted or whether any are not on the air. If any neighbor cells are not on the air, the serving cell may suffer TCH
congestion and show increased MHT. There will be an increase of immediate PS assignment rejections, TBF multiplexing, and reduction of GPRS throughput. TCH Drops due to Downlink Signal Quality
• Causes
Downlink interference, coverage • Action
– Identify cell pairs that have a high number of
handover attempts with reasonable downlink (DL) quality. This will help to identify the approximate area where mobiles experience DL interference. Check how and where the serving cell frequencies are reused to identify the interfering frequencies and plan a frequency change. This is valid for base-band frequency hopping systems. For synthesizer hopping systems, change the hopping sequence number (HSN). If the GPRS user is in a high interference area, there will be high value for block error rate (BLR) and poor throughput.
– When statistics show that drops are due to
downlink quality, the drops may be due to poor coverage. This is more common in hierarchical cell structures where traffic is forced down to lower layers using aggressive layer thresholds of –90 dBm or lower. Change the layer threshold to initiate earlier handovers to higher layers. Also modify the imperative (urgent) handover parameters to initiate earlier urgent handovers to higher layers due to bad quality. For cells on the same layer, use hysteresis and hysteresis offset to initiate early handover and modify the imperative handover parameters to also initiate earlier handover due to bad quality.
TCH Drops due to Uplink Signal Quality • Causes
Uplink interference, antenna feeder system, coverage
• Action
– Use cell traffic recording (CTR) and check
the uplink quality for certain timing advance (TA) values. Check the frequency plan to see what frequencies are used in these areas and schedule a frequency retune.
– If the cell serves with a high TA value,
make the cell less attractive in idle mode, using cell reselect offset (CRO).
Statistical
analysis and
customer
complaints are
used to identify
problems, while
drive tests are
used to verify
them and/or the
– There could be a problem in the antenna or
feeder systems. Investigate for any alarms on the site. Initiate damage assessment on coaxial and antenna systems.
– Consider increasing antenna downtilt to
reduce the service area of the cell. This can be done if there is coverage overlap so that a coverage hole is not created.
TCH Drops due to Both Links (BL) Signal Strength and due to Sudden Loss
• Causes
Coverage, hardware faults • Action
– This type of problem occurs in areas
where a cell serves a tube or tunnel. To confirm this, run CTR for this cell. Check the CTR file for both uplink and downlink signal strength. If any cell is a better server than this cell, then initiate early handover using hysteresis and hysteresis offset.
– In hierarchical cell structures, if the
affected cell is in a lower layer and if a cell from a higher layer is stronger in CTR, make early handover to the higher layer using layer threshold.
– In a duplexed transmit/receive situation, a
problem could exist in the antenna or feeder systems. Investigate for any alarms on the site. Check the antenna feeder system. TCH Drops due to Uplink Signal Strength
• Causes
Coverage, hardware faults • Action
– Check for any missing neighbor cell
relations or to see if any defined neighbors are out of service. Mobiles traveling in certain directions will run out of coverage and drop out.
– Run CTR for the affected cell and check TA
values. If TA values are high, restrict the coverage by making the cell less attractive in dedicated mode with CRO and in idle mode by initiating early handover with hysteresis and hysteresis offset.
– Consider installing a tower-mounted
amplifier (TMA) to boost the uplink and see if there is room for a TMA installation in the tower.
– Check downtilt and calculate if the existing
downtilt is correct for the intended coverage area. Increase downtilt if necessary.
– There could be a problem in the antenna or
feeder systems. Investigate for any alarms on the site. Check the feeder and antenna systems for proper operation.
Handover Performance Optimization Handover due to Degraded Signal Quality
• Causes
Downlink interference, uplink interference, coverage, antenna feeder system
• Action
– Identify cell pairs that have a high number
of handover attempts due to degraded signal quality. Check to see how and where the serving cell frequencies are reused to identify the interfering frequencies and plan a frequency change. This is valid for base-band frequency hopping systems. For synthesizer hopping systems, change the HSN.
– When statistics show that drops are due to
downlink quality, the drops may be due to poor coverage. In such cases, check the layer and layer threshold for the cell. Changing layer threshold will help when the cells are on different hierarchical layers. If the cells are on the same layer, change the value of hysteresis and hysteresis offset to initiate earlier handover.
– Run CTR for the affected cell and check TA
values. If TA values are high, restrict the coverage by making the cell less attractive in dedicated mode with CRO and in idle mode by initiating early handover with hysteresis and hysteresis offset.
– There could be a problem in the antenna or
feeder systems. Investigate for any alarms on the site. Check the feeder system. Handover Attempts but no Successful Handover Assignments
• Causes
Co-base station identity code/broadcast control channel (co-BSIC/BCCH) planning error, missing neighbor definition on the BSC and/or MSC
• Action
– Co-BSIC/BCCH planning errors occur
when a cell has two neighbors with the same BSIC and the same BCCH. Mobiles report measurements of the surrounding cells with their BSICs and BCCHs; the BSC uses this combination to identify the cell
Although drive
tests can only
provide an
indicator of QoS
for traffic that is
highly mobile and
at ground level,
they are good for
benchmarking
and ideal for
verifying applied
optimization
solutions.
identification (CI) of these cells and might direct the handover to the wrong cell. This can result in many dropped calls in the area. This can be identified from many handover attempts with no successful assignments. Change the BSIC of one of the neighbor cells.
– Check handover performance if there are
attempts but no successful assignments for some external neighbor definitions (neighbors on a different BSC and/or MSC). This is due to incorrectly defined external cells, i.e., the external neighbor cell has been incorrectly defined as a neighbor to the serving cell’s BSC with either wrong location area code (LAC) or BSIC or BCCH.
CONCLUSIONS
O
perator competency in managingperformance and optimizing QoS is not easily taught; it is developed, rather, mainly through trial and error. There are three main mechanisms for evaluating and optimizing QoS—customer complaints, drive test analysis, and statistical analysis. These mechanisms have advantages and disadvantages and can be utilized in parallel in large optimization projects. Customer complaints can be objective but are also misleading, and this mechanism is reactive. Drive tests are good for benchmarking and more ideal for verifying applied optimization solutions. Statistical analysis can identify trends but does not provide solutions. However, it can be a powerful tool for an experienced engineer with good analytical skills to use to identify problems and apply optimization solutions. The plethora of statistics generated in the network switches data must be organized before analysis. For effective network performance and evaluation, the monitoring process and statistical analysis must take place at different levels: network-wide, by geographical area or region, by city, at the BSC level, and at the cell level. Optimization solutions vary in different areas and networks but, as discussed in this paper, a generic approach can be developed to monitor and optimize the QoS as networks continuously change in response to changes in offered traffic and business priorities.
TRADEMARK
Ericsson is a trademark or registered trademark
of Telefonaktiebolaget LM Ericsson.
REFERENCES
[1] Office of Telecommunications (OFTEL), “Mobile Network Operators’ Call Success Rate Surveys – May 2003”
(http://www.ofcom.org.uk/static/archive/ oftel/publications/research/2003/call_survey/).
ADDITIONAL READING
•
“Radio Network Parameters and Cell Design Data”– Ericsson CME20 Documentation.•
“Counters in the Measurement Database for Traffic and Event Measurements in Radio Network” – Ericsson Function Specification.•
Nokia BSS S9.•
“BSC STS User Formulas” – Ericsson CME201 R9.BIOGRAPHY
Michael Pipikakis is a network
planning and wireless tech-nology manager for Bechtel’s Europe, Africa, Middle East, and Southwest Asia Region. He supports ongoing and new projects and new business development; writes guidelines and procedures for mobile network design, planning, and optimization; and participates in technology forums. Michael is a mobile networks specialist with 17 years of experience in the telecommunications industry, including more than 11 years in RF planning, design, optimization, and management of the end-to-end performance of cellular networks.
Before joining Bechtel, Michael held various management positions in the Vodafone Group’s radio system design and optimization department and development department over a 10-year period; worked for Cellnet UK and GEC Marconi UK; and was a telecommunications operator in the Greek Navy. From 1999 to 2003, he was a member of the Vodafone Global Forum for UMTS design harmonization. Michael has a BEng Honors in Electronics Engineering with Computing and Business from Kingston University in Surrey, England, and an HND in Radio Communications Systems Design from the Polytechnic School of Athens, Greece. He is a member of the Institution of Electrical Engineers.