Mentum planet v5.3 LTE TDDcase study for NSN Hangzhou trial

Peter Cheung, Technical ConsultantMentum HK10 Jun 2011 (updated 15 Jun 2011)

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summaryUse planet v5.3• Aim

• Convert NSN Hangzhou LTE TDD trial project• Optimize site based on some KPI example

• Input• 2D maps (clutter, height, clutter height)• 3D maps (building polygon with AGL)• NSN project data (site long/lat, PCI, freq, antenna, NL)• Default site config (power, loading)

• 3D Propagation model• UM model at 2.6GHz with 2D + 3D maps• With default building penetration loss

• Optimization setting• Range and cost of optimization parameter• KPI range and weight• Define AOI, UE, environment

• ACP (automatic cell planning) output • Optimize site config per sector level (e.g., height, azimuth power, type, m-tilt, e-tilt)• Compare network analysis before/after optimization

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Input map (1) – raster maps

• 5m, 20m resolution maps• Height, clutter, clutter height • converted from BIN file format to vertical mapinfomapper format (grc and grd + TAB)• all maps have same projection (Gauss-Kruger 117)

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Input map (2) – vectors

• boundary, airport, street, subway, Mstreet, Oroad, expressway, railways etc.• converted from ASCII file format to vertical mapinfo mapper format (MAP, ID, DAT + TAB)• all maps have same projection (Gauss-Kruger 117)

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Input map (3) – 3D building map

• converted from ASCII file format to vertical mapinfo mapper format (MAP, ID, DAT + TAB) with mapped column for polygon_ID, AGL (i.e., float type)• all maps have same projection (Gauss-Kruger 117)

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Input data (1) – site config

Convert NSN excel file into planet excel worksheet (site, antenna, sectors, sector_antennas etc)

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Input data (2) – neighbor list

Convert NSN neighbor list excel file into planet format excel

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Input antenna (1) – comba files

Convert comba antenna pattern and group mult-band, multi-etilt pattern into 1 planet antenna file format (.paf)• ODS-090R15NV06(F)• ODS-090R15ND06(F)

• max etilt = 6• used freq range = 2.6GHz• x-polar with 4 antenna column (i.e., 8 port)

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Input antenna (2) – conversion

• based on given etilt=0 and 6, interpolate etilt pattern in between (1..5]• assume +/-45 pattern is same for each etilt/band combination• use 65 deg broadcast azimuth BW (for now)

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Converted planet project

• 2 group of sites (hangzhou and xiasha)• total 48 sites, 141 sectors• assume only 1x 5MHz 2.6GHz carrier per each sector

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Spectrum and frame config – (1)• EARFCN=37750+300=38050 or 2600MHz for LTE TDD• BW = 5MHz

• Use default set of MCS bearer• 2 methods in planet (single value CINR or use spectrum

efficiency curve), use later method in this case study

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Spectrum and frame config – (2)

Assume no ICIC Assume • frame config has 12 DL slots

“DSUDD_DSUDD”• S-subframe config “5”

DL overhead config (PDCCH)UL overhead config (DRS, SRS, PUCCH)

% DL RE used for overhead (CP + PDCCH/PCFICH/PCHICH + PBCH + RS + PSS + SSS) for different # tx antenna

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Spectrum and frame config (3)For slow fading, assume spatial correlation between best serving sector and interfering sector is considered in CINR estimation

CINR Standard DeviationScenario Correlated slow fading

Noise limited areas Standard deviation of signal strength

Interference fully correlated with server 0 dB

Areas between co-site sectors 0 dB

Interference not correlated with server Based on correlation between signals

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Default site configLoading % = 50 (DL), UL (20)UL noise rise = 1.5dB

PA power = 43dB (before any splitter)No power boost for RS, PSS/SSS

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3D propagation model – (1)

• 3D model known as universal model (UM) from France Telecom – orange lab

• Freq = 2600MHz• Rx height = 1.5m (can change for different

building level for indoor coverage)

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3D propagation model – (2)5m, 20m, height

2D raster map

5m, 20m, Clutter height

5m, 20m, clutter map with Clutter type and approx clutter height [for pixel where clutter height map is not available]

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3D propagation model – (3)

3D building map

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3D propagation model – (4)

UM specific generated data

Facet represents reflection from far away large obstacles

Morphologies represent mapping of clutter class to UM clutter class for customized optimization

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3D propagation model – (5)

Graph represents horizontal guided propagation direction to account for horizontal diffraction either on side OR top of building

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3D propagation model – (6)

<3km, UM prediction resolution = 5m (geodata map)>3km, UM prediction resolution = 5x2m=10m

Used model defined building penetration loss (outside/inside, inside/inside) , which depends on tx/rx path length, angle, freq

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3D prediction – RSSI example

3D prediction shows • attenuated indoor coverage due to penetration• scattering by building• Waveguide effect along narrow street

Hangzhou site example xiasha site example

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Optimization setting – range

Optimization range is defined independently per sector

Relative means with respect to current NSN settingAzimuth = relative [-30, +30] degM downtilt = relative [-5, +5] degPower = relative [-5, +5] dBAntenna height = fixed at NSN setting

Optimize antenna pattern by picking either from antenna group “NSN_comba” which contains 2 antenna, ODS-090R15NV06(F) , ODS-090R15ND06(F)

Optimize e-tilt = absolute range [0..6] deg

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Pre-optimization (1) – define AOI

• AOI (area of interest) is defined for site group “hangzhou”.• Optimization in planet will be restricted to site group “hangzhou” within this AOI.

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Pre-optimization (2) – define UE

Define UE equipment• no monte carlo simulation is performed in this case study• set usual UL parameter

(e.g., 0dBi antenna, all bearer suported in UL, 24dBm)

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Pre-optimization (3) – define environment

• each pixel is split into up to 4 environment• each environment has its related parameter such as speed and fast fading margin• some clutter has certain clutter disabled

(e.g., no indoor/deep indoor for water related clutter)

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Pre-optimization (4) – analysis

• run network analysis using NSN default site config (i.e., before ACP)• set RSRP threshold = -105dBm• use FULL UL power control (i.e., UL txpower is reduced until CINR for required MCS bearer is reached)• UE speed = 3km/hr • use linear PoC vs loading % curve to compute co-channel interference• target cell edge coverage prob = 85% for outdoor environment

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Optimization (1) – define profile

set profile as combination of different optimization goals, e.g., • (weight=2) RSRP coverage with balanced footprint • (weight=1) spectrum efficiency

Combination of optimization KPI that can be selected

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Optimization (2) – define scenario

Define optimization scenario• choose optimization ONLY• choose optimized sector = considered sector

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Optimization (3) – run scenario

Set optimization to area = AOI, environment = indoor (wt=1) and outdoor (wt=2)

Sector loading used in optimization• if optimization KPI is dependent on loading, it requires traffic map from AOI area• static load (constant) OR dynamic load (fluctuate during ACP process)

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ACP – setting summaryProfile 1 Profile 2 Check ACP result

KPI Profile 1 loading

dependent

KPI Profile 2 loading dependent

ACP result 1 RSRP>-105dBm

no NA Compare RSRP before/after ACP

ACP result 2 RSRP>-105dBm with

balance footprint

no Max Spectrum efficiency with bin weighting by traffic

map

Yes, create a default traffic

map

Compare balanced RSRP AND DL max data

rate before/after ACP with same loading %

Do 2 ACP scenario

• for both indoor environment (weight=1), outdoor environment (weight=2)

• first scenario with load independent KPI, e.g., max area % for RSRP > -105dBm, and compare with NSN RSRP layer

• second scenario with a default traffic map and max area % for both RSRP > -105dBm and spectrum efficiency and compare both RSRP and DL max data rate with NSN case

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ACP 1 – RSRP

• Optimization done in step 0..20• ACP finished in 1 min• report shows progressive RSRP gain %• report shows site config changes• apply optimized site config at final step

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ACP 1 – RSRP comparisonAfter ACP (outdoor environment)

before ACP(outdoor environment)

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ACP 1 – RSRP statistics (outdoor)

Area % within AOI with RSRP > -105dBm

Before ACP

after ACP

• ACP gives about 15% area gain with RSRP above KPI• area % outside range represent NULL pixel value (e.g., no best server available, other 3 environment – indoor, deep indoor, vehicular)

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ACP 2 – create traffic map

Default traffic map• traffic map area = optimization AOI• assume total # subscriber = 500 within AOI• assume some clutter weight

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ACP 2 – loading used

Assume• max 1000 sub per 1x carrier (i.e., within 500 max sub limit)• min RSRP required = -105dBm

Use same static loading as per sector (i.e., 50% DL loading)

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ACP 2 – result

• Optimization done in step 0..22• ACP finished in 15 min• report shows progressive RSRP gain % and spectrum efficiency• report shows site config changes• apply optimized site config at step 5

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ACP 2 – RSRPAfter ACP (outdoor environment)

before ACP(outdoor environment)

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ACP 2 – RSRP statistics

Area % within AOI with RSRP > -105dBm

Before ACP

after ACP

ACP gives about 18% area gain with RSRP above KPI

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ACP 2 – RSRP balanceUse numeric grid filter to generate histogram of RSRP per different best serving sector

RSRP layer

Best serving sector layer

Before ACPafter ACP

ACP config gives a higher and more balanced RSRP per each best serving sector area

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ACP 2 – max DL data rateAfter ACP (outdoor environment)

before ACP(outdoor environment)

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ACP 2 – max DL data rate statistics

ACP gives area gain different for different max DL data rate range• 0~1Mbps à area gain 3.7%• 1~2Mbps à area gain 2.1%• 2~5Mbps à area gain 3.5%• 5~10Mbps à area gain 0.6%

Area % within AOI with DL max data rate at range 0, 1, 2, 5, 10Mbps

Before ACP

after ACP

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ACP 2 – DL max spectrum efficiencyAfter ACP (outdoor environment)

before ACP(outdoor environment)

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ACP 2 – max DL spectrum efficiency statistics

ACP gives different area % for different DL spectrum efficiency range• 0~1 à 6.3% gain• 1~2 à 2.7% gain• 2~5 à 0.6 % gain• 5~10 à same

Before ACP

After ACP

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ACP 2 – DL CINR After ACP (outdoor environment)

before ACP(outdoor environment)

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ACP 2 – DL CINR

Before ACP

After ACP

ACP gives different area % for different DL CINR• 0~5dB à 7.2% gain• 5~10dB à 5.2% gain• 10~20dB à 2.5 % gain• >20dB à 1.2 % gain