5G Massive MIMO ACP Coverage Optimization
Huawei Technologies Co., Ltd
Course Objectives
Understand the main challenges of 5G Massive MIMO optimization
Master the objectives and methods of 5G Massive MIMO coverage optimization
Understand 5G Massive MIMO ACP optimization principles
Master the 5G Massive MIMO ACP delivery process
and tool usage
Huawei Technologies Co., Ltd
Massive MIMO Optimization Overview
Massive MIMO ACP Coverage Optimization Principles Massive MIMO ACP Delivery Guide
Typical Massive MIMO ACP Cases
Contents
page
Advantages of 5G Massive MIMO
gNodeB Baseband
gNodeB Baseband
T1 T1 R1 R1
TN TN RN RN Radio Front
WideBeam Wide Bro Broadcast B Beam eam
adc ast Bea m Bro adca
st Be am Broadcast B eam B roa dca st Be am
Br oadc ast B eam Br oa dc as t B ea m B roa dc
ast B ea m
PBCH/SS/CSI-RS/PDCCH/PDSCH
All Channel Support BF ， Improve Antenna Gain, Extend Coverage Radius
Horizontal
Beamforming Vertical Beamforming
Narrow Beam ， Decrease Interference ， Improve SINR
High Building ： Vertical 4 beam
Square+Building ： Vertical 2 beam Square ： Horizontal 8
Beam
Support 3D BF, adopt to multi Scenario, support beam costumed
Increases SU-MIMO/MU-MIMO flow number and double
the capacity
Challenges Faced by 5G Coverage Optimization
Challenge 1: Massive MIMO broadcast beam weights can be adjusted from one-dimensional to multi-dimensional. There are thousands of weight combinations for a single AAU. How to select the optimal weight in different scenarios?
Coverage Scenario
Horizontal Beamwidt
h
Vertical Beamwidt
h
Digital tilt adjustmen t range
Adjustable Steps of Digital Tilt
Digital azimuth adjustmen
t range
digital azimuth adjustable
step number
Total Adjustable
Steps
Default 105° 6° -2-13 16 0 1 16
SCENARIO_1 110° 6° -2-13 16 0 1 16
SCENARIO_2 90° 6° -2-13 16 -10-10 21 336
SCENARIO_3 65° 6° -2-13 16 -22-22 45 720
SCENARIO_4 45° 6° -2-13 16 -32-32 65 1040
SCENARIO_5 25° 6° -2-13 16 -42-42 85 1360
SCENARIO_6 110° 12° 0-9 10 0 1 10
SCENARIO_7 90° 12° 0-9 10 -10-10 21 210
SCENARIO_8 65° 12° 0-9 10 -22-22 45 450
SCENARIO_9 45° 12° 0-9 10 -32-32 65 650
SCENARIO_10 25° 12° 0-9 10 -42-42 85 850
SCENARIO_11 15° 12° 0-9 10 -47-47 95 950
SCENARIO_12 110° 25° 6 1 0 1 1
SCENARIO_13 65° 25° 6 1 -22-22 45 45
SCENARIO_14 45° 25° 6 1 -32-32 65 65
SCENARIO_15 25° 25° 6 1 -42-42 85 85
SCENARIO_16 15° 25° 6 1 -47-47 95 95
Measurement scenario: RAN3.0, 64T, slot assignment 4:1, SSB-
based beam densification disabled Grand
total 6899
Beam0
Beam1
Beam2
Beam3
Beam4
Beam5
Beam6 SSB outsourcing
According to 3GPP specifications, a sub-6 GHz band supports a maximum of eight SSB-beams for polling transmission. The maximum number of SSB-beams supported is related to the slot
assignment and timeslot structure. The number of SSB-beams and the direction of each beam used in each coverage scenario can be flexibly designed. Theoretically, infinite weight combinations are supported. In actual engineering applications, Huawei gNodeBs are preconfigured with 17 typical coverage scenarios and support remote adjustment of tilts and azimuths, meeting requirements in common wide coverage and building coverage scenarios.
Slot0 Slot1 Slot2 Slot3
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
SSBBea m0 SSBBea m1 SSBBea m2 SSBBea m3 SSBBea m4 SSBBea m5 SSBBea m6
Default,T6,A0 Scenario_3,T6,A0 Scenario_8,T6,A0
Scenario_8,T6,A20 Scenario_8,T0,A20
Scenario_8,Beam Density
horizontal wave width narrowing
vertical beamwidth broadening
Adjusting the azimuth
Adjusting the tilt Enabling beam
densification
Challenges Faced by 5G Coverage Optimization
The broadcast beam weight adjustment mode is extended from one-dimensional (horizontal beamwidth) of traditional antennas to multi-dimensional (horizontal beamwidth/vertical beamwidth/tilt
angle/azimuth/beam densification).
Challenges Faced by 5G Coverage Optimization
Challenge 2: Both SSB and CSI-RS beam coverage optimization must be considered. CSI-RS used for channel measurement does not support RSRP and SINR reporting.
Question Call drop
Handov er failure
Frequent
handovers Low
MCS Low
RANK Low rate
Poor SSB quality √ √ √ √
Poor CSI-RS quality √ √ √
SSB: Cell-level polling transmission. A maximum of eight sub-6 GHz beams are
supported.
CSI-RS: UE-level; 64T supports 32 beams.
The SSB and CSI-RS envelopes differ greatly, and the SSB beam direction can be adjusted. The coverage difference is obvious.
The SSB(SS/PBCH Block) functions:
CSI-RS functions and classification:
Function CSI-RS Type Description Channel Quality
Measurement (CM)
NZP-CSI-RS
(Non-Zero Power CSI-RS) The UE reports the following information to the eNodeB:
CQI, PMI, rank indicator (RI), and layer indicator (LI)
CSI-IM
(CSI-RS Interference Measurement)
Beam Management
(BM)
NZP-CSI-RS The UE reports the following information for beam measurement:
L1-RSRP, CRI (CSI-RS resource indicator)
Time and frequency offset
tracing (TRS)
TRS (Tracking RS) Used for precise time and frequency offset tracing.
RRM/RLM
Measurement
NZP-CSI-RSUsed for mobility management
measurement. The UE reports CSI-RSRP, CSI-RSRQ, and CSI-SINR.
Function Channe
l Description
Downstream
synchronization
SSSPSS Clock synchronization, frame synchronization, and symbol synchronizationCell Signal Quality
Measurement
SSS Used for RSRP/RSRQ/SINR measurement, initial beam selection, and RRM measurement.Sending a System
Broadcast Message
PBCHUsed by users to obtain necessary information for network access, including the system frame number (SFN) and the system frame number (SCS) used by the RMSI.
The SSB reflects the quality of the broadcast channel, affects the initial access and handover performance of 5G UEs, and determines the coverage on the road and the entire network. CSI-RS reflects the quality of traffic channels, affects CQI reporting, MCS selection, and rank of 5G UEs, and determines the user-perceived throughtput.
Both SSB and CSI-RS have significant impact on user experience of 5G networks. During network planning and optimization,
coverage and interference of SSB and CSI-RS must be both considered.
64T 32T 8T 4T 2T
AAUs AAU and A+P antenna
RRU+antenn
a, EasymacroRRU+antenna
, Easymacro RRU+antenna , book RRU
TDD TDD TDD TDD/FDD TDD/FDD
High Higher Medium Low Low
17 types 9 types 4 types 1–2 types 1 type
Supported Supported Partially
supported Not supported Not supported
supportedNot Partially
supported Supported Supported Partially supported
Supported Supported Supported Partially
supported Partially supported
Challenges Faced by 5G Coverage Optimization
Challenge 3: 5G products have various forms and differ greatly in capabilities. RF adjustment methods are diversified but their functions are different.
Traditional manual RF Adjustment cannot cope with this situation
Optimization Method Impleme ntation
cost Implementation Effect Limitation
Tradition al method
Mechanical
Tilt High The SSB and traffic channel
beams are affected. Limited by the mounting kit or installation condition
Azimuth High The SSB and traffic channel
beams are affected. Limited by the mounting kit or installation condition
Electrical tilt Medium The SSB and traffic channel beams are affected.
Some products do not support this function. In co-antenna scenarios, it is complex to modify the electrical tilt.
Power Low
The SSB and traffic channel beams are affected at the same time. The SSB power offset is supported.
Limited by the maximum transmit power and maximum convergence capability of the product
broadca st beam
Pattern
Coverage
Scenario Low Only the horizontal or vertical beamwidth of the SSB beam is affected.
32T and lower-capacity products do not support all 17 scenarios.
Digital tilt Low Only the vertical direction of the SSB beam is affected.
The vertical beamwidth cannot be adjusted when it reaches the maximum value.
8T and lower-capacity products do not support this function,
Digital
azimuth Low Only the horizontal direction of the SSB beam is affected.
The horizontal beamwidth cannot be adjusted when it reaches the maximum value.
Not supported by 4T and lower-capacity products
• Beam Scenario
• Digital-tilt
• Digital-Azimuth
SSB CSIRS/PDSCH
• Power
• M-tilt
• M-Azimuth
• E-tilt
Coverage Scenario
Digital Tilt/Azimuth
Electrical tilt
Product
Form
Mechanical Tilt/Azimuth Number of
TRx
Product Appearanc
e
Coverage capability
Duplex Mode
Challenges Faced by 5G Coverage Optimization
Challenge 4: How to Optimize Road Coverage and Entire Network Coverage?
Challenge 5: RF optimization is difficult to implement because GSM, UMTS, LTE, and NR share the same antenna system.
At the early stage of 5G commercial use, the number of subscribers is small but most of them is high-value users. However, RF optimization is mainly based on DT data.
which can only reflect road coverage situation. Only optimize the road coverage may decrease the real 5G user experience. Therefore, Optimization must to consider both the road coverage and throughput improvement and non-road performance not decrease
com pre
hen sive op tim um
Limited by expensive antenna installation platform resources, operators mostly deploy 5G by sharing antennas, including LTE and NR sharing AAU and G/U/L/NR sharing passive antennas (or A+P). During 5G optimization, the impact on existing RATs needs to be considered, increasing the complexity.
NR 2.6G/3.5G LTE 2.6G/3.5 G 3.5G/2.6G, 32T/64T
3.5G, 8T
700/800/900/1800 /2100M, 2T/4T
NR 3.5G/700/2100 L1800/2100 U900/2100 Co-AAU
Co-passive antenna Co- A+P antenna
3.5G, 32T
700/
800/
900/
1800/
2100,
2T/4T
Main Functions of 5G ACP Coverage Optimization
SSB coverage optimization based on DT data: The SSB RSRP and SINR reflect the quality of the broadcast channel and affect the initial access and handover performance of 5G users. SSB coverage optimization can ensure continuous 5G coverage and basic handover performance.
DT-based CSI-RS coverage optimization
①: The CSI-RS RSRP and SINR reflect the quality of the traffic channel, affect the CQI/PMI/RI reporting, MCS index selection, and greatly determine 5G user throughput. CSI-RS coverage optimization can provide better service experience for users.
Handover chain optimization based on DT data: In the cluster single-user peak rate optimization scenario, comprehensively consider factors such as the distance, coverage, and quality of surrounding cells, create a handover sequence model for serving cells on the road, and perform RF parameter optimization based on the handover chain modeling result to obtain a better cluster single-user peak rate.
Road optimization also predicts the coverage of the entire network
②: In the 5G network engineering optimization phase, coverage
optimization is performed based on DT data. This method may cause the coverage of other areas to decrease while improving road coverage. ACP uses both DT data and BT propagation model simulation data to optimize road coverage and improve the coverage of the entire network.
Coverage optimization based on simulation data
②: In the optimization phase of a new 5G network, if DT or MR data cannot be collected, BT propagation model simulation data is used to optimize the coverage of the entire network.
LTE and NR co-antenna coverage optimization: In the LTE and NR co-antenna deployment scenario, besides NR DT data, DT/MR data on the LTE side is collected to ensure that LTE and NR coverage is both improved during optimization. For details, see 《
5G MCE Service Solution LTE-NR (Co-AAU) ACP Coverage Optimization Technique Guide 》
Function restrictions:
① . This function is a test feature and requires the support of the Mate 30 terminal. In addition, the base station must be upgraded to RAN3.0 and the periodic CSI-RS measurement must be enabled. If you need to use this function, contact the HQ for support.
② . This function is a test feature. The BT propagation model needs to be deployed on the GENEXCloud platform. If the BT propagation model needs to be used,
contact the HQ to confirm whether the platform supports this function and assist in BT propagation model calibration.
Huawei Technologies Co., Ltd
Massive MIMO Optimization Overview
Massive MIMO ACP Coverage Optimization Principles Massive MIMO ACP Delivery Guide
Typical Massive MIMO ACP Cases
Contents
page
Massive MIMO ACP Solution Process
Data is the basis. Algorithms is the key Decision-making is the goal
Overlapping coverage evaluation
Coverage Quality Overshoot
limit
Target Setting
Solution output and result display
Automatic optimization Input Grid-based data
evaluation
Coverage evaluation Interference
evaluation
Overshoot coverage evaluation
Before After
Gain
Prediction Grid
Level Cell Level Network Level Traffic weight
Rasterize
Existing network Engineering
parameters
Electronic map
Antenna file DT data
Adjustment suggestion
Etilt DAzimuth Beam
Scenario
Mtilt Azimuth.
Dtilt
Satisf y target
s?
Search the desired
result
Simulation (Coverage , Quality)
N
Y
Problem grid identifica tion
Problem grid aggrega tion
Proble matic area aggreg ation
Optimiz ation Proble
matic cell filterin g
Iterative optimization algorithm
Overlapping
Overlapping
coverage evaluation
Path Loss Matrix
Cell 1
Cell 2 PL2
PL1
PL3 Cell 3
NonRoad Grid Road Grid
Key Capability 1: Coverage and Interference Modeling & Path Loss Modeling
Performs grid-based processing on DT data with a precision of 5m, constructs a RSRP matrix model from each NR cell to each grid, and generate a SSB coverage and interference relationship model based on NR cell information in the grid.
The path loss matrix is constructed on the the cell information, location information, and RSRP information from DT/ frequency scanning/MDT data. The path loss matrix is a key factor for predicting coverage changes after antenna adjustment. The path loss modeling has two
solutions: fixed path loss and non-fixed path loss.
In the fixed path loss solution, Keep the same path loss for coverage prediction before and after RF adjustment
In the non-fixed path loss solution, calculate the multipath propagation information of radio signals before and after RF adjustment based
on Huawei self-developed beam tracing (BT) model to obtain more accurate path loss changes. The non-fixed path loss solution depends
on the GPU resources deployed on the GENEXCloud platform to implement BT propagation model acceleration.
Key Capability 2: Unique Device-Pipe-Chip Synergy CSI-RS Measurement Capability in the Industry
MATE30
Balong5000 BTS5900 Probe
3GPP Release 15 (38.331) defines CSI-RS RRM (mobility) measurement. Based on the unique device-pipe synergy capability in the industry, Huawei gNodeBs, HiSilicon Balong5000 chips, Mate 30 terminals, and Probe drive test tools collaborate to implement CSI-RS RRM measurement defined in 3GPP specifications, the 20A/B version supports CSI-RS RSRP and SINR measurement (including cell-level and beam-level measurement) for the serving cell and the three strongest neighboring cells. Other main equipment and terminal vendors do not have specific plans. Huawei is more than one year ahead of the industry.
Note that the CSI-RS RRM measurement capability of the base station and terminal side in the current version (20A/B) is not mature. Therefore, the CSI-RS optimization solution needs to be further matured.
Category Problem Description Current Progress Estimated
Resolution Time
Base Station
After periodic CSI-RS RRM measurement is enabled, event A3 cannot be reported. (SSB and CSI use the same MearObjectId.)
Temporary solution: The HiSilicon provides a temporary non-standard version. Formal solution: The 22A version works with the HiSilicon B5010 version, and the CSI inter- frequency measurement is used to solve the problem.
Temporary solution: 2020.06 Official solution:
2021Q4
Base Station
2T/4T devices are not supported.
8T/32T/64T hybrid networking is not supported.
In 20B, hybrid networking of 32T and 64T is supported. In 21B, beam mapping solutions are added to support various networking modes.
2021Q2
Base Station
CSI-RS RRM measurement supports a maximum of three neighboring cells, affecting the accuracy of CSI-RS interference modeling.
The beam mapping solution is added in version 21B. B5010 supports one primary
serving cell and seven neighboring cells. 2021Q4 Terminal The RSRP and SINR of CSI-RS 3I cannot be reported. The Mate 40 has accepted the RSRP and
SINR reporting requirements of CSI-RS 3I. 2020Q4
Terminal
The B5000 platform specifications are limited. CSI-RS RRM
measurement is supported for a maximum of four cells, affecting the accuracy of CSI-RS
interference modeling.
B5010 has been required to support CSI-RS
RRM measurement of eight cells. In discussion
Terminal
The B5000 platform specifications are limited. L3 supports only the CSI-RS RRM measurement of the strongest beam, affecting the accuracy of CSI-RS interference modeling.
A requirement has been submitted to B5010 for reporting the measurement of the eight strongest beams. The requirement is under discussion.
In discussion
For int ern al r efe ren ce onl y!
Optimization
Objectives Target threshold (automatic/manual) Weight SSB RSRP SSB RSRP Threshold
Default value: CDF10%
x
SSB SINR SSB SINR Threshold
Default value: CDF10% y
SSB Overlapping
1. Minimum Signal Level 2. RSRP
3. Cell Number
Default value: 105 dBm, 9 dB, 2
z
SSB Overshooting
1. Distance Factor
2. Minimum Signal Level
3. Overshooting Grid Ratio Threshold 4. Overshooting Grid Count Threshold
Default value: 1.2-times inter-site distance, CDF10%, 15%, 10
-
Key Capability 3: Multi-Optimization Target Setting, Automatic
Identification of Problematic Grids, and Problematic Area Aggregation
Currently, four optimization objectives can be set for massive MIMO ACP, including SSB RSRP, SSB SINR, SSB Overlapping, and Overshooting
Coverage. Coverage-First optimization and Quality-First optimization are supported. The optimization objective threshold and weight can be automatically generated.
Identify problem grids based on the optimization target, including weak coverage grids, overlapping coverage grids, and poor SINR grids. Converges problematic grids into problematic areas based on the types and distribution of problematic grids, and automatically generates polygon boundaries of problematic areas.
Problem grid identification
Problematic area aggregation
Problem area selection
The grids whose performance is lower than the set threshold are problem grids
Based on the geographical
distribution, aggregate problem grids using the CFDP clustering
algorithm
The boundary of the problem area is extracted by
AlphaShape algorithm, and the selection is completed based on convex hull extraction and least squares ellipse fitting
Control point
Noise point
Core Point boundary point
When CSI-RS coverage optimization is selected, the optimization objective will the CSI-RS RSRP item.
, 0
( ) n k k n( ), [0,1].
i
C t P B t t
Key Capability 4: All Scenarios + Full Beam 3D Antenna File Simulation
* N = +
RF single-TRX antenna file TRX Weight File Single-beam antenna file
=
External antenna file The 3D antenna file can indicate the antenna gain of the AAU or
passive antenna in each direction (horizontal 0–359°, vertical 90–90°, and step 1°) of the three-dimensional space. It is an important input for ACP coverage prediction.
Massive MIMO ACP (2020Q1) has integrated 30+ types of AAU and RRU antenna files, traversed all beam scenarios, digital tilts, and digital azimuth combinations, and considered the impact of features such as slot assignment and beam densification on the number of beams. The number of antenna files of a single AAU model exceeds 10,000, supports 3D and 2D display of SSB- and CSI-RS-based beam patterns and full-range optimization of broadcast beam patterns.
Note: To improve the tool running efficiency, the digital azimuth is
optimized in iteration mode with a step of 5°.
Key Capability 5: Accurate Prediction of Coverage and Interference
The ACP obtains the initial path loss based on DT data. During iterative optimization, the ACP calculates the antenna gain and path loss changes after RF or pattern parameter adjustment based on the 3D antenna file and BT propagation model, accurately predicts the RSRP of each cell in each grid after adjustment, then, predicts the optimized SINR and overlapping coverage rate based on big data modeling.
Path loss
Step 1: Calculate the initial path loss.
TX power AAU
s
Pathloss = TX Power - Feeder loss + Antenna Gain –
RSRP_before
Step 2: Calculate the antenna gain change.
The antenna gain increases
by 6dB at the same position.
Gain before
adjustment: 11 dBi Gain after
adjustment: 17 dBi
Adjust the digital azimuth
clockwise by 20°.
Step 3: Change the path loss in BT propagation model simulation.
Path loss before adjustment: 127 dB
Path loss after adjustment: 125 dB
The path loss at the same
position decreases by
2 dB.
Adjust the digital azimuth
clockwise by
Example: RSRP after = （ -95dBm ） + （ 6dB ） - （ -2dB ） = 20°. -87dBm
The path loss of the direct path remains unchanged.
Variable reflection path loss
RSRP after =RSRP before +∆ antenna gain - ∆ pathloss
SINR after =10*log(RSRP after /( Interference after + Noise ))
Determine the optimal serving cell of each road section and complete the primary serving cell distribution modeling.
Iterative Optimization Based on Primary Serving Cell Distribution
Modeling
1. Candidate cell set:
Select the cells whose RSRP difference between each DT sampling point and the strongest cell is within 6 dB as candidate cells.
2. Quality sorting: Sort candidate cells based on factors such as the SINR, rate, distance, and sector direction.
4. Generate the optimal serving cell distribution:
Consider candidate cell ranking and handover factors to generate the target serving cell of each road section.
3. Exclude abnormal cells.
Candidate cells with overshoot coverage, discontinuous coverage, and less than 10 sampling points are excluded.
5. Online review: The primary serving cell distribution modeling result can be reviewed online, and the target serving cell of each road section can be manually adjusted.
During iterative optimization, the pattern and RF parameters are adjusted to enhance the coverage of the target serving cell on each road section and reduce the coverage of other cells. In this way, the distribution of serving cells on each road section after optimization is close to the modeling result, improving the overall coverage quality and throughput of the road.
Key Capability 6: Primary Serving Cell Distribution Modeling and Optimization for Optimal Quality and Throughput
Cell
Name RSRP (dBm) SINR
(dB) Throughput
(Mbps) Distance
(m) Candidate Cell Quality
sorting
A -81 13 540 349 YES 2
B -84 9 515 349 YES 3
C -86 6 470 420 YES 4
D -101 -6 null 420 NO null
E -95 -1 351 327 NO null
F -83 10 633 327 YES 1
G -91 1 413 559 NO null
H -104 -9 null 559 NO null
In the cluster single-user peak rate optimization scenario, factors such as coverage, quality, throughput, and distance of
surrounding cells are considered. The distribution model of the best primary serving cell in each road section is built, and
RF parameters are optimized based on the modeling result to obtain better road coverage and peak throughput.
Example of ACP iteration optimization
SSB CSI-RS
Before
Beam Scenario ： S2_H90V6 D-Azimuth ： 0°
Coverage fulfillment rate ： 87.5%
Overlapping fulfillment rate ： 75%
Finess ： 87.5*0.75+75*0.25= 84.375
1st round of iteration optimization Beam Scenario ： S1_H110V6 D-Azimuth ： 0°
Coverage fulfillment rate ： 100%
Overlapping fulfillment rate ： 75%
Finess ： 100*0.75+75*0.25= 93.75
2nd round of iteration optimization Beam Scenario ： S2_ H90V6
D-Azimuth ： -10°
Coverage fulfillment rate ： 100%
Overlapping fulfillment rate ： 87.5%
Finess ： 100*0.75+87.5*0.25= 96.875
Optimization
Object Threshold Weight
SSB RSRP -100dBm 0.75
SSB Overlapping -
105dBm,2Cell,6d
B 0.25
1 2 3 4 5 6 7 1 8
2 3 4 5 6 7 8 Tuning Cell
Overlapping
1 2 3 4 5 6 7
1 8
2 3 4 5 6 7 8
Overlapping Weak Coverage
1 2 3 4 5 6 7
1 8
2 3 4 5 6 7 8
Overlapping
Tuning Cell
(1) Reduce the proportion of weak SSB coverage and low SINR, reduce abnormal events, and improve the 5G
camping ratio.
2. Reduce the SSB overlapping coverage to reduce throughput drops caused by frequent handovers.
3) Change the distribution of road serving cells and select the best cell for each road section to improve
the Throughput.
Gains of DT-based Massive MIMO ACP Optimization
4. Increase the CSI-RS RSRP, improve the CQI/MCS, reduce overshoot coverage, and improve the rank.
Base Beam Scenario
Optimized Beam
Scenario Base D-tilt Optimized D-tilt Base SSB
RSRP Optimized
SSB RSRP Base SSB
SINR Optimized SSB SINR
H65V6 H65V12 6 3 -93dBm -88dBm 1 dB 5 dB
ACP improves SSB coverage in the following ways:
Improves SSB coverage of the serving cell.
Increase the horizontal or vertical beamwidth.
Decrease the downtilt (weak coverage at the cell edge).
Increase the tilt (weak coverage at the cell center).
Adjust the azimuth to align the main lobes.
Increase the SSB power offset.
ACP reduces overlapping coverage by using the following methods:
Improves SSB coverage of the serving cell.
Reduce the SSB coverage of non-serving cells.
Reduce the horizontal or vertical beamwidth.
Increase the tilt.
Change the azimuth to deviate the main lobe.
Decrease the SSB power offset.
Cell Base Beam
Scenario Optimized
Beam Scenario Base D-
Azimuth Optimized D-
Azimuth Base SSB
RSRP Optimized SSB RSRP
Serving H65V6 H90V12 0 0 -91dBm -87dBm
Neighboring H105V6 H65V6 0 20 -93dBm -99dBm
Based on the primary serving cell modeling and iterative optimization, select better serving cells for low-throughput roads to ensure proper
distribution of road serving cells and better DT test throughput.
Cell Base Beam Scenario
Optimize d Beam Scenario
Base
D-tilt Optimize
d D-tilt Base SSB
RSRP Optimized
SSB RSRP Avg RANK
Avg DL Throughput
(Mbps)
A H65V12 H65V6 6 9 -85dBm -89dBm 2.3 579
B H65V6 H90V12 8 4 -87dBm -84dBm 3.5 712
HO Times:11 Avg THP:437Mbps
HO Times:5 Avg THP:451Mbps
Cell Base M- Azimuth
Optimize d M- Azimuth
Base
M-tilt Optimiz
ed M-tilt Base CSI- RS RSRP
Optimized CSI-RS
RSRP Avg CQI Avg RANK
DL Throughput
(Mbps)
A 230 230 14 20 -81dBm -85dBm 11.5 1.7 365
B 240 270 11 11 -83dBm -78dBm 12.9 3 615
The unique CSI-RS measurement and optimization capability improves the CSI-RS RSRP and further improves the CQI and MCS. This feature identifies and resolves overshoot coverage cells to reduce the RANK decrease caused by over-long-distance coverage
Gains of DT-based Massive MIMO ACP Coverage Optimization
Cluster DT weak coverage rate
Cluster DT coverage
rate Overlapping coverage rate DL MAC Throughput(Mpbs)
Before
Optimization After Optimization
Before Optimizatio
n
After Optimization
Before
Optimization After Optimization Before
Optimization After Optimization
20%
16%
(relatively decreased by 20%)
80% 84% 20%
14%
(relatively decreased by 30%)
515 553
(Increased by 7.4%) Definition:
Cluster DT weak coverage rate
: For cluster DT data, the proportion of sampling points whose SSB RSRP and SSB SINR meet a certain threshold (for example, SSB RSRP ≥-91 dBm and SSB SINR ≥ -3) is called cluster DT coverage rate, and the proportion of sampling points whose SSB RSRP and SSB SINR do not meet the threshold is called cluster DT weak coverage rate.Overlapping coverage rate in cluster DT
: If the SSB RSRP difference between a neighboring cell and the primary serving cell is less than or equal to 9 dB, the SSB RSRP of the neighboring cell is greater than or equal to -105 dBm, and the number of neighboring cells that meet the preceding two conditions reaches a certain threshold (for example, greater than or equal to 2), this sampling point is called the overlapping coverage sampling point. The ratio of the overlapping coverage sampling points to all DT sampling points is called the cluster DT overlapping coverage rate.Note: For a specific cluster, the threshold and target of the weak coverage rate and overlapping coverage rate in the drive test must be determined based on the site planning target.
If the target is set too high, the target cannot be achieved. If the target is set too low, the initial value is too high, affecting the optimization gains. If the DT result reaches the planned weak coverage rate or overlapping coverage rate, ACP does not ensure coverage optimization gains.
The Coverage gain specifications:
Based on the planned coverage rate and overlapping coverage rate target of each site, when the DT coverage rate is lower than 90%, the DT weak coverage rate decreases by 20% after the engineering optimization is started and two rounds of optimization are performed. If the overlapping coverage rate is higher than 20%, the overlapping coverage rate decreases by 30% after two rounds of optimization.
Throughput gain specifications:
When only SSB coverage optimization is used, the throughput gain is not committed. When both primary serving cell distribution optimization and CSI optimization are used, the DL Throughput increases by more than 5% after two rounds of optimization.
For example, after APC optimization, the DT coverage rate and overlapping coverage rate of a cluster are improved as follows to
achieve ACP gains:
Huawei Technologies Co., Ltd
Massive MIMO Optimization Overview
Massive MIMO ACP Coverage Optimization Principles Massive MIMO ACP Delivery Guide
Typical Massive MIMO ACP Cases
Contents
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Massive MIMO ACP Coverage Optimization Delivery Process and Data Requirements
Data Source
Type Data Requirements
NR engineering parameters
Collect the last updatedNR cell engineering parameters. Ensure that the NR cell
information is complete and accurate. You can obtain the engineering parameter template required for ACP optimization from the ACP app when creating a task. In addition, you can use the Engineering Parameter Check app to automatically sort NR engineering parameters required for ACP optimization.
Configuration File
Collect the latest eNodeB configuration data to check the accuracy of cell configuration parameters in engineering parameters, including power, PCI, frequency, and pattern configuration.
Electronic map
Collect the electronic map within one year. The electronic map must be in Planet format and the precision is 5 m. The electronic map must contain at least the Clutter, Heights, Vector, and Buildings layers. When the electronic map is used together with ray tracing simulation, the Buildvector layer is also required. The electronic map can be obtained from the carrier's or Huawei GIS database. The electronic map uploaded by the ACP must be in .zip format.
Antenna file
The ACP tool has been preconfigured with the antenna file for traversing patterns of 5G massive MIMO antennas. You do not need to obtain the antenna file. You can view the massive MIMO antennas supported by the ACP tool in the antenna file library.
DT data
Collect DT data of the optimization area and export the data into a CSV file. During cluster optimization, the DT must be performed according to the specifications to ensure the rationality of the test route and vehicle speed.
The accuracy of engineering parameters and integrity of DT data are very important to the ACP optimization result. During data sorting, onsite survey and configuration check must be performed to ensure the data accuracy. Otherwise, the gains may not meet expectations or even negative gains may occur. The accuracy requirements for engineering parameters are as follows:
Longitude and latitude: ±20 m; Antenna height: ±5 m; Azimuth: ±10°; Mechanical Tilt: ±2°
Note: In rooftop site scenarios, if the distance of each sector antenna exceeds 20m, the
longitude and latitude of each antenna need to be collected.
ACP-based coverage optimization delivery suggestions in massive MIMO DT scenarios
In the cluster engineering optimization phase, you are advised to perform ACP optimization in two rounds.
Round1: physical RF parameter optimization
If serious weak coverage, overshoot coverage, or overlapping coverage occurs in a cell due to the following reasons, preferentially adjust mechanical antenna parameters. If necessary, adjust the antenna installation position. Considering the implementation cost and optimization efficiency, the proportion of cells whose mechanical antenna parameters are adjusted is generally within 15%.
Round 2: pattern parameter optimization
After the first round of optimization, the second round of
optimization focuses on broadcast beam pattern to optimize the primary serving cell of each road section and reduce
unnecessary handovers.
Note: In 4G/5G co-antenna (or AAU) scenarios (for example, CMCC 2.6G), collect MDT data on the 4G side and DT data on the 5G side for joint RF optimization to ensure that 4G coverage does not decrease while 5G coverage is improved, for details, see 《
5G MCE Service Solution LTE-NR (Co-AAU) ACP Coverage Optimization Technique Guide 》 .
(1) The angle between the antennas of co-site cells is less than 90°.
2. The main lobe of the antenna is severely blocked 3) The installation position or direction of the antenna is inconsistent with the planning.
4) The planned mechanical tilt is too large or too small,
resulting in improper coverage.
Huawei Technologies Co., Ltd
Massive MIMO Optimization Overview
Massive MIMO ACP Coverage Optimization Principles Massive MIMO ACP Delivery Guide
Typical Massive MIMO ACP Cases
Contents
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Operator H country C, the coverage rate of CL108 improved by 14 pp after ACP optimization, and the DL Throughput Improved by 15.3% (67.5 Mbps) - 1/2.
Objective Unit Base plane Optimized
Solution 1 Solution 2
SSB RSRP dBm 87.03 84.41 83.52
SSB SINR dB 6.56 10.21 9.96
SSB Overlapping Grid/Grid 1.74 1.53 1.5.
CL108 is a typical urban scenario. 112 NR 2.6 GHz cells have been deployed. Before the optimization, the DT coverage rate is 61.7% (standard: SSB RSRP ≥ -91 and SSB SINR ≥ -3). The overall coverage is poor. Two ACP optimization solutions, with different optimization target thresholds and weights. ACP optimization is performed based on the MM Pattern parameter. After the task is executed, two optimization solutions and prediction results are as follows:
The RSRP prediction gain of solution 2 is relatively high,
and the adjustment proportion is relatively low. Therefore, solution 2 is selected
ACP provides pattern
adjustment advice for 36 cells (32.14%), in which 16 cells are involved in beam adjustment.
Optimization Object Solution 1 Solution 2
SSB RSRP Threshold: Weight -99 dBm: 1 -95 dBm: 3
SSB SINR Threshold: Weight 1.5 dB: 1 3 dB: 1
SSB Overlapping: Weight -105 dBm/2/9: 1 -105 dBm/2/9: 3
SSB Overshooting -99 dBm -95 dBm
Tuning Parameter Solution 1 Solution 2
Beam Scenario 21 16
Digital Downtilt 22. 23
Digital Azimuth 9 6
KPIs 0921 ACP Before
Optimization 0921 ACP after optimization
Serving SSRSRP (dBm) -87.46 -85.47
Serving SSSINR (dB) 6.83 8.59
Coverage of Center (RSRP ≥ -88 and SINR ≥- 3) 55.14 66.57
Coverage of Universal (RSRP ≥ -91 and SINR ≥ -3) 61.7 75.93
NR 1 Stream Rate (%) 21.05 12.1.
NR 2 Stream Rate (%) 52.99 57.64
NR 3 Stream Rate (%) 23.19 - 23.19 26.58
NR 4 Stream Rate (%) 2.77 3.67
NR DL IBLER (%) 9.43 8.91
NR DL MAC THR (Mbit/s) 440.75 508.28
NR DL PRB Number 369626.21 386455.66
NR DL RBLER (%) 0.37 0.15
NR DL Retrans Rate (%) 10.27 9.71
NR Rank Indicator Expects 2.05 2.18
NR Wide Band CQI 12.46 12.82
NR DL MCS Experts 19.06 20.13
Handover Delay (ms) 25.32 26.1
Handover Success Rate (%) 99.4 100
IntraFreq Handover Success Rate (%) 99.4 100
IntraFreqHandover Delay (ms) 25.32 26.1
NR Residence Time(s) 3412.76 3470.65
According to geographic statistics (5 m x 5 m), the average SSB-RSRP improve by 2 dB, the SSB-SINR improve by 1.76 dB, the coverage rate (RSRP ≥-91dBm and SINR ≥ -3dB) iimprove by 14.23pp, and the MAC DL Throughput improve by 15.3% (67.53 Mbit/s). After the ACP optimization, the number of RRC connection reestablishment times decreases from 5 to 1.
SINR Prediction Change vs SINR Test Change
The SINR prediction improvement area is basically the same as the actual test improvement area.
The number of RRC connection
reestablishments decreases 4 Comparison of DL MAC Throughput
Operator H country C, the coverage rate of CL108 improved by 14 pp after ACP optimization,
and the DL Throughput Improved by 15.3% (67.5 Mbps) - 2/2.
Operator H Country C, SSB-SINR improve by 2.5dB, and the E2E efficiency is 41% higher than Manual Optimization.
KPIs Before
Optimiza tion
Manual optimiz
ation
Manual optimizati
on gain
ACP optimization
ACP Optimizati
on Gains Serving SSB RSRP (dBm) 72.27 73.35 1.08 72.12 0.15 Serving SSB SINR (dB) 11.23 13.29 2.06 13.58 2.35 CSI-RS Channel RSRP
(dBm) 76.88 76.25 0.63 76.42 0.46
CSI-RS Channel SINR (dB) 30.58 31.01 0.43 30.67 0.09 NR DL PDCP THR (Mbit/s) 364.12 367.36 3.24 375 10.88 NR DL MAC THR (Mbit/s) 369.03 366.04 2.99 376.07 7.04
In the high-quality area of Hexi cluster in HangZhou Wulin, 5G massive MIMO ACP performance gains are mainly verified, and the efficiency comparison between manual optimization and 5G ACP is also tested.
Three rounds of DT data analysis (manual) – 3 round 750
5.1 DT KPI Statistics 30
5.2 Analysis of Abnormal Events (Access Failure, Re-establishment,
Call Drop, and Handover Failure) 180
5.3 SSB RSRP Weak Coverage Analysis 180
5.4 SSB SINR Weak Coverage Analysis 240
5.5 Outputting Optimization Advice (Parameters and Patterns) 120 Three rounds of drive test data analysis (ACP) – 3 round 360
6.1 DT Counter Statistics 30
6.2 Analysis of Abnormal Events (Access Failure, Re-establishment,
Call Drop, and Handover Failure) 180
6.3 Importing ACP Data (Engineering Parameters, Maps, and Test Logs) 30 6.4 Setting and Execution of ACP Optimization Parameters 60
6.5 Checking ACP Optimization Advice 60
According to the ACP optimization result, the SSB SINR improve by 2.35 dB compared with the baseline, and the manual
optimization increases by 2.06 dB.
Compared with manual optimization and ACP optimization, DT coverage optimization doubles the efficiency of manual optimization in terms of data analysis and saves the E2E cluster optimization duration by 41%.
Cell Name Baseline Manual ACP
Xinkai Hotel C_65, HZ S0_H105_V6_T3_A0 S4_H45_V6_T9_A15: S5_H45_V6_T5_A15:
Xinkai Hotel C_66, HZ S0_H105_V6_T3_A0 S4_H45_V6_T6_A10:
Xinkai Hotel C_67, HZ S0_H105_V6_T3_A0 S3_H65_V6_T6_A0: S14_H45_V25_T6_A0
HZ金海宾馆 C_65 S0_H105_V6_T3_A0 S4_H45_V6_T9_A0 S5_H25_V6_T3_A0
HZ金海宾馆 C_66 S0_H105_V6_T3_A0 S5_H25_V6_T3_A-10 S8_H65_V12_T5_A0
HZ金海宾馆 C_67 S0_H105_V6_T3_A0 S3_H65_V6_T3_A0 S4_H45_V6_T0_A20
HZ器材厂 C_65 S3_H65_V6_T6_A0
HZ器材厂 C_66 S0_H105_V6_T3_A0 S0_H105_V6_T6_A0
HZ器材厂 C_67 S3_H65_V6_T3_A0 S3_H65_V6_T9_A0
HZ light business building C_65 S0_H105_V6_T3_A0 S4_H45_V6_T9_A30: S4_H45_V6_T8_A30:
HZ light business building C_66 S0_H105_V6_T3_A0 S5_H25_V6_T3_A20: S15_H25_V25_T6_A0:
HZ Lightweight Business Building C_67 S0_H105_V6_T3_A0 S3_H65_V6_T3_A0:
C_65, No.6 Building, No.1 LY Wulin Building,
Peking University Building, Central, China S0_H105_V6_T3_A0 S2_H90_V6_T4_A0 C_66, Building 4, No. 1, LY Wulin, Building Peking
University, HZ S0_H105_V6_T3_A0 S5_H25_V6_T9_A20: S3_H65_V6_T6_A0:
No.4 Building C_67, No.1 LY Wulin Building,
Peking University Building, Central, China S0_H105_V6_T3_A0 S5_H25_V6_T6_A30: S5_H45_V6_T9_A0:
Suning Electric Appliance C_65, LY Hushu Road,
Peking University Building, Central, China S0_H105_V6_T3_A0 S5_H25_V6_T9_A20:
ACP performs pattern optimization for 10 cells. The optimized coverage scenarios include S0/3/4/5/8/14/15, totally 7 types
Pattern optimization is manually performed for 14 cells. After optimization, the
coverage scenarios include S0, S2, S3, S4, and S5, total 5 types
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