GPX147 B Product Description (V1 0).pdf

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Guide for GSM Network

Performance Loss Evaluation

After Refarming


Contents

Relationships Between Network Performance and Network Characteristics

KPI Performance Evaluation Methods

Macroscopic Evaluation Method� — EFL

Microscopic Evaluation Method — Refined Network Architecture

Impact of Features

Experience Compensation

Refarming Cases

Page 3


Network characteristics: In brief, it refers to topology and configuration of BTSs on the network, including frequency reuse pattern,

traffic density, distance between sites, height of sites, antenna direction, antenna down tilt, carrier configuration of cells and so on.

The more complex network characteristics is, the evaluation of C/I and coverage is more difficult and therefore KPI performance

evaluation is even more difficult. Combined use of features also increases the difficulty in KPI evaluation. As a result, the

accuracy of KPI performance evaluation is unlikely to be 100%，but we will try our best to approach 100% as much as possible.

Macroscopic

Microscopic

1. Network performance is strongly correlated with network characteristics.

2. Influencing factors of network characteristics are various and interrelated, which increases the complexity of performance evaluation.

Combined use of

features leads to the

difficulty in KPI

evaluation.

Network Characteristics

EFL(impacting C/I)

Overlapped Coverage (impacting C/I and signal level)

Traffic Density

Frequency Reuse Pattern (Frequency Resource/Average Site Type)

Cross Coverage

Normal Coverage

Week Coverage

Insufficient Coverage

TCHHproportion

Average Distance Between Sites

Relationships Between Network Performance and

Network CharacteristicsBefore evaluating KPIs in refarming scenarios, we firstly introduce the relationships between network performance and network

characteristics:

Features


Contents



(1) Macroscopic Evaluation Method� — EFL

(2) Microscopic Evaluation Method — Refining Network Architecture

(3) Impact of Features

(4) Experience Compensation

Refarming Cases

Page 6


(1) KPI Performance Macroscopic Evaluation Method —

Effective Frequency Load (EFL)

Page 7

Similar to CDMA network, GSM networks in big cities now become self-interference limited systems. In interference-limited

scenarios, the more the effective traffic loads per frequency band (EFL), the interference becomes more serious. Accordingly,

KPIs deteriorate.

EFL = Equivalent traffic/Effective coverage scope/Number of available frequencies = Number of TRXs per area x Carrier Utilization Rate/Number

of available frequencies, where Carrier Utilization Rate = (Full-rate calls (Erl) + Half-rate calls (Erl)/2 + PS equivalent traffic)/Number of TRXs, PS

equivalent traffic = Average number of occupied PDCHs.

The following is an example for the impact of EFL on KPIs:

94 Available Frequencies

Number of

900MHz Macro

Sites per Area

Average Number

of Carriers per Cell

Number of TRXs per Area

Carrier Utilization

Rate

Number of Frequencies

Occupied by TCH

EFL

Call Drop Rate on TCH

per Cell (Excluding Handover)

Success Rate of Radio

Handovers

Chengdu 4.729 6.34 89.94 51% 68 0.67455 0.84% 97.90%

Hangzhou 6.699 4.39 88.2 41.26% 63 0.57764

Shijiangzhuang 3.744 7.314 82.14 39% 71 0.451192 0.73% 99.10%

Tianjin 3.482 6.02 62.88 43.24% 66 0.411959 0.70% 99.20%

EFL in Chengdu is highest, which leads to lowest C/I and worst network performance.

Shijiangzhuang Chengdu Hangzhou Tianjin

Macroscopic evaluation: EFL changes → C/I changes → KPIs change

Macroscopic

qualitative

evaluation in

preliminary

stage


(1) KPI Performance Macroscopic Evaluation Method� —

Refarming Scenarios

Page 8

The network performance after refarming depends on the following factors:(1) Decrease in frequency resources: Available frequencies decrease → EFL increases/tight frequency reuse → Interference is stronger → Network performance is

worse

(2) Traffic migration scheme: Traffic migration → Traffic density changes/Average site type decreases/TCHH proportion increases → EFL changes → Interference

changes → Network performance changes

TCHH proportion changes:

In refarming scenarios, increasing TCHH proportion is commonly used for load sharing and affects the indicators MOS and HQI.

Traffic migration between 900 MHz and 1800 MHz frequency bands:

Traffic in refarming frequency band decreases → EFL decreases → Interference decreases → Network performance is better

Traffic migration to UMTS/LTE network:

GSM traffic decreases → EFL decreases → Interference decreases → Network performance is better

Site type/configuration changes:

The decrease in frequency resources leads to an increase in EFL and tightness in frequency reuse and a decrease in network performance. Therefore, certain networks after traffic

migration will reduce their configurations (or increase TCHH proportion, or migrate traffic to other frequency bands or other network modes) to decrease frequency reuse tightness, EFL

and interference.

(3) Distance between sites and terrain feature: They remain unchanged after refarming and therefore their impact on network performance can be ignored.

(4) Coverage scope:

Power-matching is necessary before and after refarming to ensure the consistency of coverage. However, coverage scope may experience changes under the following

circumstances which should be concerned in power-matching and preventive measures (increasing TRX modules and transmit power at the top of the BTS cabinet ) should

be taken.

A. When the UMTS 900 MHz is enabled and shares multicarrier modules with GSM network, the carrier power of GSM decreases and the coverage becomes worse.

B. Antenna changes (for example, decrease in antenna gains when a new frequency band is enabled) or GU antenna-sharing (involving combining and power loss).

C. Impact of GU antenna-sharing on the GSM network:

After refarming, if GU antenna-sharing scheme is used and GSM antenna parameters follow UMTS, GSM coverage cannot be optimized, which has negative impact on KPIs.

(5) Impact of small frequency spacing between GSM and UMTS networks on the GSM network:

In GU small frequency spacing scenario, when the UMTS is enabled it exerts extra interference on the GSM. The stronger the interference, the worse the GSM network

performance.

(6) Enabling the relevant features and functions (refer to gains of the features in sales guide) may affect KPIs.


(2) KPI Performance Microscopic

Evaluation Method

Page 9

This method

can be used

in bid

answering.

Degradation of KPIsAfter Refarming = (KPIsAfter Refarming – KPIsBefore Refarming)/KPIsBefore Refarming

Description:

1. Preestimate the KPIs before and after refarming respectively, and obtain the change tendency and change

amplitude of KPIs.

2. On enhanced dual-band network, estimate the KPIs of the two frequency bands respectively, then obtain the

KPIs of the network based on the ratio of underlaid TRXs to overlaid TRXs.

Obtain

network

KPIs

•Frequency resource, average site type, frequency reuse pattern, traffic; coverage scope, distance between sites, terrain feature...

1. Refined evaluation: Based on the impact of network characteristics , obtain KPI 1

•Power control, DTX, EICC, VAMOS, IBCA…

2. Impact of features: Based on specifications for its impact on performance KPIs, obtain KPI 2.

•Live network KPIs, live network-based C/I changes, experience in delivered refarming projects

3. Experience compensation: Based on the live network experience, modify the KPI changes.


(2) Microscopic Evaluation Method — Refined

Network Architecture

Page 8

Microscopic KPI performance evaluation is mainly based on the following network

characteristics:

Frequency resources, average site type, frequency reuse pattern of BCCH/TCH;

Coverage scope, distance between sites, terrain feature, frequency band;

Traffic per channel, Percentage of half-rate calls, AMR penetration rate;

Based on data collection and analysis on the live network, perform KPI baseline fitting

according to the network characteristics and obtain KPI baseline values of similar

networks.

For details about the impact of relevant network characteristics on KPIs, see the

following parts of the slide.

In refarming scenarios, the following factors are likely to change:

(1) Frequency resources decrease → Frequency reuse pattern changes →

Interference increases → Network performance is worse

(2) Traffic migration → Traffic per channel changes → Average site type decreases

→ Percentage of half-rate calls increases → Interference changes → Network

performance changes

(3) Coverage scope changes (For details, see the relevant description in Page 6.)

(4) Distance between sites and terrain feature: The two factors remain unchanged after

refarming and therefore their impact on network performance can be ignored.


(2.1) Impact of Decreased Frequency Resources on C/I —

Strongly Correlated to Refarming

Page 9

Frequency resources decrease → Tightness of frequency reuse pattern increases → EFL and

FRLOAD increase → C/I is lower → Network performance is worse

The following figure shows the decreasing tendency of C/I based on the changes in frequency reuse pattern.

Frequency reuse pattern: 6x3 → 5x3 → 4x3 → 3x3 → 2x3

Note: Y (on the vertical axis) is the percentage of C/I lower than x (on the horizontal axis).


(2.1) Relationship Between C/I, RxQuality, BER and

Network Performance

The bit error rate or bit error ratio (BER) is the number of bit errors divided by the total number of transferred bits

during a studied time interval. The BER can be considered as an approximate estimate of the bit error probability

for a long time interval.

RxQuality is used to evaluate the call quality by calculating the BER in radio communication. It can be obtained

in traffic measurement statistics.

The following table shows that if C/I becomes lower, BER and RxQuality are affected and further KPI changes.

Page 10

C/I

[dB]BER Range (%)

RxQuality [Reported Values

Specified by 3GPP TS]Impact on KPI

23 BER < 0.2 % RXQUAL_0C/I is higher → BER is

lower → RxQuality is

higher → KPI is better.

Base on data

collection and analysis

on the live network,

perform KPI baseline

fitting and obtain the

correlation between

C/I and KPI of similar

networks.

19 0.2 % < BER < 0.4 % RXQUAL_1

17 0.4 % < BER < 0.8 % RXQUAL_2

15 0.8 % < BER < 1.6 % RXQUAL_3

13 1.6 % < BER < 3.2 % RXQUAL_4

11 3.2 % < BER < 6.4 % RXQUAL_5

8 6.4 % < BER < 12.8 % RXQUAL_6

4

12.8 % < BER

RXQUAL_7

Figure 1 shows that BER improvement is

based on the increase in C/I.

Correlation between C/I with BER, RxQuality and KPI


(2.2) Impact on TCHH Proportion changes — Correlated to

Refarming

Owing to the decrease in frequency resources, the frequency planning is inconvenient.

Therefore, TRX configuration of the GSM network is reduced. To prevent system

capacity being limited, TCHH proportion is increased. The full-rate transmission rate of

Um interfaces is 16 kbit/s, while HR supports 8 kbit/s which deteriorates user

experience, the MOS scores, and HQI.


(2.3) Impact of Distance Between Sites

Page 12

The distance between sites is unlikely to change after refarming. Therefore, it seldom deteriorates the network performance. Due to the difference in

distance between sites, the absolute values of KPIs are different.

Distance between sites decreases → Overlapped coverage scope increases (interfered cells may increase) → C/I decreases

→ Network performance is worse

In the preceding figure, cells 4 and 5 are added and use the frequency F2. Cell 2 uses the frequency F1. Assuming that the height and downtilt

of the antenna remain unchanged, the overlapped coverage between cells 1 and 3 remains unchanged but interfered cells increase.

Therefore, if the height and downtilt of antenna remain unchanged, the distance between sites decreases, the number of cells within the

overlapped coverage scope increases. Accordingly, the uplink of the current cell is interfered by the uplink of neighboring cells and the

network performance deteriorates.

Cell 1 (F1) Cell 2 (F2) Cell 3 (F1)Cell 4 (F2) Cell 5 (F2)

Cell 1 (F1) Cell 2 (F2) Cell 3 (F1)



Page 13

Any mature and usable feature is tested and verified in relevant

scenarios and released in official documents such as sales

guide. The procedure for evaluating network KPIs based on the

impact of features are as follows:

Step 1: Obtain the application scenarios of the feature and

specifications for its impact on performance KPIs in different

scenarios by referring to the relevant released documents.

Step 2: Evaluate whether the feature should be enabled and the

impact on network performance when it is enabled.

Step 3: Based on KPI 1 and the impact of features, obtain KPI 2.

Functions and Features Changes(XX->YY)

BSC Version V900R012

Huawei III Power Control Used

DTX Unused

ICC/EICC Unused

TFO Unused

MCPA Unused

IBCA Unused

VAMOS Unused

GU Antenna Sharing Unused

GU 3.8 MHz Unused

……


(4) Experience Compensation/Correction Factors

Page 14

This evaluation method typically reflects the performance of similar networks which, however, may be different

from the live network performance. The reasons is as follows:

(1) Different networks have different characteristics such as the terrain feature, average distance between

sites in the whole network and actual distance between sites in each subnetwork. This may decrease the

accuracy in the preestimated KPIs before refarming.

(2) Difference in capability of network optimization may lead to KPIs difference even in similar network

scenarios.

The following information can be used to correct the preestimated KPIs before refarming:

Live network KPIs before refarming

It is better to provide the live network KPIs before refarming. If there is a large difference between the preestimated KPIs before refarming

and the live network KPIs, calculate the absolute values of KPIs after refarming based on the live network KPIs and the preestimated KPIs

before refarming changing amplitude. (Note: In refarming scenarios, it is better to promise the changing amplitude of KPIs instead of the

absolute value of KPIs. This is because the absolute value of the live network KPIs may change because traffic may increase during the

period between GSM migration and bid answering.)

Simulation results based on C/I of the live network before and after refarming (using relevant engineering

parameters and digital maps)

The C/I values on the similar networks are discrete. Based on the concrete C/I value on the live network, KPI baseline values on the similar

networks can be changed to adapt to the live network.

Experience in delivered refarming projects can be learned.


Contents



(1) Macroscopic Evaluation Method� — EFL

(2) Microscopic Evaluation Method — Refined Network Architecture


(4) Experience Compensation

Refarming Cases

Page 15


Case 1: French SFR Refarming Project (in Rural Area)

Spectrum information: Before refarming: Frequency band: 900 MHz Bandwidth: 9.8 MHz;

After refarming: Frequency band: 900 MHz Bandwidth: 5.6 MHz

Product version: SRAN 3.0; Module type: MRRU

Refarming Project Implementation Process:

1. Replan the frequency on the original NSN network (NSN still uses the 9.8 MHz bandwidth, and the TRXs to be reduced use the 4.2

MHz bandwidth which will be given away to UMTS 900MHz.)

2. Migrate the GSM 900 MHz to Huawei equipment. Disable some of the GSM carriers and enable the UMTS 900 MHz carriers (The

frequency bandwidth on the GSM network is reduced to 5.6 MHz).

GSM 900MHz Configuration Reduction Scheme (Average frequency reuse changes from 49/2.87 = 17 to 29/2.52 = 11.5.)

Comparison of KPIs Before and After Refarming (Decrease in Frequency Resources, Tight Frequency Reuse, Increase in

Interference, Decrease in Call Drop Rate)

Page 16

Before Refarming After Refarming

Number

of

Sectors

Number of

TRXs

Average Site

Type

Number

of

Sectors

Number of

TRXs

Average Site

Type

93 267 S2.87 93 234 S2.52


Case 2: AIS Refarming Project in Thailand (Distance

Between Sites in HYI Urban Area - 500 m)Spectrum information: Before refarming: Frequency band: 900 MHz Bandwidth: 17.5 MHz;

After refarming: Frequency band: 900 MHz Bandwidth: 12.5 MHz

Refarming Project Implementation Process:

GSM 1800 MHz migration + capacity expansion (March 27, 2011 – April 12, 2011)

Migrate traffic from 900 MHz to 1800 MHz frequency bands (Continuous implementation starts from April 17, 2011; A small quantity of

traffic has been migrated after 1800 MHz migration.)

Refarming GSM 900 MHz (Implemented on NSN equipment and completed on May 4, 2011. The GSM 900 MHz still uses the 17.5 MHz

frequency bandwidth. The TRXs to be reduced use the 5 MHz frequency bandwidth which will be given to the UMTS 900 MHz.)

Remove 5 MHz frequency bandwidth on the 900 MHz frequency band and reduce the configuration of GSM 900 MHz (Implemented on

NSN equipment and completed on May 8, 2011.)

GSM 900 MHz Configuration Reduction Scheme Before and After Refarming (FrLoad increases from 20% to 25%.)

Comparison of KPIs on the GSM 900 MHz Network Before and After Refarming

Page 17

Before Refarming After Refarming

Frequency

Bandwidth

Frequency

Reuse Pattern

Average

Site Type

Frequency

Bandwidth

Frequency

Reuse Pattern

Average Site

Type

Proportion of

Migrated Traffic

17.5 MHz

BCCH:9*3

TCH:5*3 S5 12.5 MHz

BCCH:7*3

TCH:4*3 S4 About 6%


Thank you

www.huawei.com

Documents

GPX147 B Product Description (V1 0).pdf