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Technical White Paper for the Optimal Network Performance Antenna Solution
Author Wanlilong (49089)
Issue V1.0
Date 2012-08-27
HUAWEI TECHNOLOGIES CO., LTD.
Issue V1.0 (2012-08-27) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
i
Copyright © Huawei Technologies Co., Ltd. 2012. All rights reserved.
No part of this document may be reproduced or transmitted in any form or by any means without prior
written consent of Huawei Technologies Co., Ltd.
Trademarks and Permissions
and other Huawei trademarks are trademarks of Huawei Technologies Co., Ltd.
All other trademarks and trade names mentioned in this document are the property of their respective
holders.
Notice
The purchased products, services and features are stipulated by the contract made between Huawei and
the customer. All or part of the products, services and features described in this document may not be
within the purchase scope or the usage scope. Unless otherwise specified in the contract, all statements,
information, and recommendations in this document are provided "AS IS" without warranties, guarantees or
representations of any kind, either express or implied.
The information in this document is subject to change without notice. Every effort has been made in the
preparation of this document to ensure accuracy of the contents, but all statements, information, and
recommendations in this document do not constitute a warranty of any kind, express or implied.
Huawei Technologies Co., Ltd.
Address: Huawei Industrial Base
Bantian, Longgang
Shenzhen 518129
People's Republic of China
Website: http://www.huawei.com
Email: [email protected]
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-08-27) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
ii
Contents
1 Overview ......................................................................................................................................... 3
2 Challenges to Antenna Design Brought by MBB Network Development ........................ 4
3 Key Antenna Issues and Solutions of the Optimal Network Performance....................... 5
3.1 Optimizing Antenna Design to Improve Network Performance Using 3D Patterns ........................................ 5
3.2 Achieving Antenna ParameterParameter Balance with the Optimal Network Performance ............................ 6
3.3 Synchronizing the Antenna and Base Station Algorithms to Achieve the Optimal Network Performance .... 11
4 Conclusion .................................................................................................................................... 13
5 Acronyms and Abbreviations ................................................................................................... 14
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
3
1 Overview
Legacy antennas are widely used on networks. The design of archaic antennas meets basic requirements
of network applications. With the development of mobile broadband (MBB) and the increasing requirements
for network performance, the design of antennas faces new challenges, for example, providing the optimal
network performance (especially improving throughput) based on the collaboration between antennas and
base station or networks.
This document describes the integrated network antenna solutions that optimize the SINR by improving
antenna patterns and best match the new base station algorithm. This document provides suggestions for the
antenna design and evaluation in the MBB era to improve network performance.
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
4
2 Challenges to Antenna Design Brought by MBB Network Development
UMTS/HSPA/LTE radio networks evolve from analog/GSM/CDMA networks. The objective of radio
networks changes from providing basic voice consecutive coverage to providing networks with high
throughput and large capacity. As the front-end devices of radio networks, antennas must improve network
capacity and user throughput instead of merely transmitting radio signals.
The multiple-input multiple-output (MIMO) multiple-antenna technology is a major technology for
obtaining higher throughput in the UMTS, HSPA, and LTE. Antennas must coordinate with base station
algorithms to implement the MIMO technology and improve the MIMO performance. Therefore, antennas
are closely related to base station technologies.
In the MMB era, antenna development and implementation of higher throughput rate using antennas
bring challenges to the antenna design and evaluation.
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
5
3 Key Antenna Issues and Solutions of the Optimal Network Performance
3.1 Optimizing Antenna Design to Improve Network Performance Using 3D Patterns
Incomplete antenna knowledge is the primary antenna issue that hinders the antenna optimization and
the implementation of the highest throughput on radio networks. In the past, antenna parameters are
described using 2D patterns, which show antenna status on the whole. However, the main lobe of an antenna
is not the same as other lobes. According to the following formula, only 1.1% antennas are accurately and
correctly described using 2D patterns:
360 x 2/(360 x 180) = 1.1%
If only 2D patterns exist, 3D patterns are obtained using a fitting formula. Three-D patterns reflect the
antenna status completely and truthfully. Tests and comparison show that 3D patterns can eliminate errors
caused by 2D patterns.
0 lists the error statistics between a fitted 3D pattern and a real 3D pattern (dB).
Table 3-1Error statistics between a fitted 3D pattern and a real 3D pattern (dB)
GB Mode 1 Mode 2 Mode 3 Mode 4 RET2 Mode 1 Mode 2 Mode 3 Mode 4
TDS
Broadcast
beam
AVG
value 0.67 10.59 11.84 2.36
direction
al
Electrical
tilt (ET =
2°)
AVG
value 0.18 4.21 0.1 1.22
Standard
deviation 6.84 14.44 9.65 6.27
Standard
deviation 5.16 11.6 6.67 5.66
Max
value 42.1 76.23 58.5 44.74
Maximum
value 38.32 58.8 45.85 44.08
YW0 Mode 1 Mode 2 Mode 3 Mode 4 RET7 Mode 1 Mode 2 Mode 3 Mode 4
TDS
0°
Traffic
beam
Standard
deviation 1.66 13.53 3.83 4.55 direction
alElectric
al tilt (ET
= 7°)
AVG
value 0.1 3.35 0.1 3.46
Standard
deviation 6.66 13.59 8.23 6.53
Standard
deviation 5.89 10.5 6.22 5.87
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
6
Max
value 49.75 62.29 52.71 48.92
Maximum
value 43.02 58.05 42.59 46.14
YW60 Mode 1 Mode 2 Mode 3 Mode 4 RET12 Mode 1 Mode 2 Mode 3 Mode 4
TDS
60°
Traffic
beam
AVG
value 2.03 10.66 6.8 2.27
direction
alElectric
al tilt (ET
= 12°)
AVG
value 0.06 3.67 0.67 3.16
Standard
deviation 6.42 13.7 9.76 6.83
Standard
deviation 5.9 10.98 6.58 6.2
Max
value 40.29 70.53 67.36 46.5 Max value 49.87 50.92 53 52.91
0 shows how to obtain a precise 3D pattern.
Figure 3-1Obtaining a precise 3D pattern
In the preliminary design stage, the HFSS software can be used to generate 3D patterns. During
closed-loop optimization, verification, and selection of antennas, the advanced SG128 spherical near-field
antenna test range can be used to obtain precise 3D patterns. The advanced SG128 spherical near-field
antenna test range adopts the ultra-wideband dual-polarization high-performance probe, highly precise
five-axis turntable (with the precision up to 0.01°), high-performance shielding room, AEMI absorber, laser
infrared aligner, professional mounting bracket, and advanced temperature control technology.
3.2 Achieving Antenna Parameter Balance with the Optimal Network Performance
Antenna parameters are classified into twenty categories and 37 sub-categories. Tens of parameters form
an antenna system, which leads the industry development jointly with the operator's. The parameters guide
antenna design to meet basic requirements of network applications. However, no weighting factor of
parameters is quantified during antenna design to obtain the optimal network performance. The following
items are the reasons why no dedicated parameter can guarantee the highest network throughput:
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
7
Antenna parameters are mutually constrained and increase or decrease randomly. The parameter
relationship is not quantified.
Certain parameters are not mutually affected but affected by multiple parameters. For example, the
pattern gain is affected by the vertical beam width, horizontal beam width, side lobe suppression, null
filling, and feeder loss, and so on.
Antennas are developed from single band to dual-band, tri-band, quad-band, and penta-band antennas.
The scope of antenna adjustment and optimization varies.
Therefore, quantifying the weighting factor of a set of parameters is impossible or the quantified factor
is not unique and varies with conditions.
Antennas are deployed to obtain high network performance. To handle this key antenna issue,
parameters must be selected based on the network performance. That is, parameters are designed and
optimized to obtain the optimal network performance, which is the unique standard for selecting solutions
and antenna parameter combinations.
Coverage with strong signals is the prerequisite and the basic and mandatory condition for radio
communication. The SINR must meet related requirements. The higher the SINR is, the higher the
throughput is. With meeting basic requirements of signal strength, improvement of the signal-to-noise
distribution on the entire network is essential in antenna design to increase the network throughput. The
optimal network performance is implemented using the following methods during antenna design:
Determine the general relationship between the network performance and an independent parameter or
certain linked parameters.
Two examples are taken to describe the impact of independent parameters and linked parameters on the
network performance. The first example describes the impact of the PIM on sensitivity, coverage distance,
coverage area, and uplink throughput. 0 shows the impact of the PIM on the network coverage and
performance.
Figure 3-2 Impact of the PIM on the network coverage and performance
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
8
The second example describes the impact of linked parameters on the network performance based on
the antenna gain changes. If the antenna gain changes independently, the network performance changes
monotonically. If the antenna gain changes jointly with the vertical beam width, the network performance
does not change monotonically. That is, the gain is unnecessarily the highest if changes of the vertical beam
width are considered. Balance must be achieved between the antenna gain and vertical beam width.
0 shows the impact of the antenna gain and linked vertical beam width on the network performance.
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
9
Figure 3-3mpact of the antenna gain and linked vertical beam width on the network performance
There may be several optimum parameter combinations in the reference parameters. Select required
parameters to form the optimal parameter combination set.
Iterate network performance evaluation and parameter design for closed-loop optimization.
During the design, an antenna solution is determined quickly based on the reference parameters or the
optimal parameter combination set. The optimal parameter combination set is selected preferentially.
Alternatively, other parameter sets can be selected based on actual conditions. With the iteration of network
performance evaluation and parameter design, a solution providing the highest network capacity is obtained.
0 shows two parameter solutions: H and X. As shown in 0, the antenna gain of solution H is higher. However,
the horizontal beam width and front-to-rear ratio are not as good as those of solution X. Which solution
should be selected?
Figure 3-4 Patterns of two parameter solutions
Simulation analysis of typical scenarios shows that the cell average throughput (CAT) and cell edge
throughput (CET) rate of solution H are better. Therefore, solution H or parameter sets in solution H are
selected.
0 shows the simulation result.
Figure 3-5 Simulation result
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
10
Define more appropriate parameters to evaluate the antenna performance.
Parameters are non-equivalent to the network performance. The impact of different parameters on the
network performance varies accordingly. The front-to-rear ratio is taken as an example. The front-to-rear
ratio can be used to estimate the interference to the back lobe. However, a more appropriate parameter can be
defined based on the network conditions. 0 shows a new parameter: SiteFBR.
Figure 3-6 SiteFBR
0 shows three sectors (S1, S2, and S3) at a site that are marked by different colors. Assume that φ (top)
is 0º, φ (left) is –90º, and φ (right) is +90º in this figure. Signals in the area outside +60º and –60º are
interferences to the other two sectors. For a sector, there are three φ values. Signals with maximum φ value
are valid signals and those with the other two φ values are interferences. Therefore, SiteFBR(φ) is calculated
as follows:
SiteFBR(φ) = Signal/(Interference1 + Interference2)
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
11
In this way, the SiteFBR(φ) for each φ value can be calculated. The three sectors use antennas of the
same model. Therefore, the SiteFBR value is the same when the φ value increases or decreases by 120º. After
the SiteFBR(φ) is calculated, the average SiteFBR can be obtained.
In 0, SiteFBR(φ) is calculated based on solutions H and X in 0. As shown in 0, solution H is better. The
calculated SiteFBR(φ) values for the two solutions are as follows:
SiteFBR_H = 17.84 dB
SiteFBR_X = 14.73 dB
The SiteFBR parameter effectively combines nearly all antenna parameters of a horizontal pattern,
including gain, side lobe, back lobe, horizontal lobe width, Squint, ±60º roll-off, and typical site
configurations. The SiteFBR parameter is more advanced than the front-to-rear ratio and can provide better
network performance.
3.3 Synchronizing the Antenna and Base Station Algorithms to Achieve the Optimal Network Performance
With the base station technology evolving to the LTE, the multiple-antenna high-order MIMO becomes
the key technology for increasing network throughput. The MIMO technology is closely related to antennas.
Selection and design of antennas must be consistent with the base station technology for optimal matching.
In this way, the highest network throughput can be obtained.
Design of the tri-band antenna is taken as an example to describe the impact of the side by side (SBS)
and Stack solutions on the MIMO performance. The tri-band antenna is configured with a low band and two
high bands. The low band is 796–960 MHz and the high bands are both 1710–2690 MHz. 0 shows the high
frequency bands of the two solutions.
In the Stack solution, low-band antennas and high-band antennas are deployed coaxially and high-band
antennas are mounted vertically.
In the SBS solution, low-band antennas and the antenna array marked by +x are deployed coaxially and
the high-band antennas are horizontally deployed in parallel.
Figure 3-7 Deployment of high-band antennas in the SBS and Stack solutions
Difference of the two solutions:
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
12
The dipole quantity of the Stack solution is less than that of the SBS solution due to limited total length.
Consequently, the antenna gain is less and the vertical lobe width is longer in the Stack solution.
Antenna relevance of the Stack solution is smaller than that of the SBS solution.
The LTE simulation is conducted for the two solutions based on case 1 of the 3rd Generation
Partnership Project (3GPP), as shown in 0.
Figure 3-8 LTE simulation result
The simulation result shows that the CAT and CET of the SBS solution are much better than those in the
Stack solution, especially the CET. the SBS solution outshines the Stack solution when deploying 4x2 or 4x4
high-order MIMO . Therefore, SBS is the optimal solution of tri-ban antenna.
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
13
4 Conclusion
This document provides suggestions on the antenna development trend and design or evaluation based
on challenges to MBB networks.
Adopt precise 3D patterns in antenna design or evaluation to provide references for antenna selection.
Evaluate antenna parameters based on the network performance. Thoroughly research the relationship
between important parameters (such as PIM and gain) and the network performance. Define appropriate
network performance parameters (such as the SiteFBR). Achieve parameter balance (for example,
balance between the gain and vertical lobe width) to obtain the optimal network performance.
Design the antenna architecture based on new base station technologies such as the MIMO to optimally
match base stations and antennas. In multi-ban antennas, the SBS antenna solution can maximize the
network performance and is the optimal option for antenna selection in the MBB era.
Technical White Paper for the Optimal Network Performance Antenna Solution
Issue V1.0 (2012-11-02) Huawei Proprietary and Confidential
Copyright © Huawei Technologies Co., Ltd.
14
5 Acronyms and Abbreviations
C
CAT Cell Average Throughput
CET Cell Edge throughput
E2E End to End
MBB Mobile Broad Band
MIMO Multi-Input Multi-Output
RTT Radio Transfers Technology
RNPS Radio Network Planning Solution
RRM Radio Resource Management
SINR Signal Interference Noise Ratio