Technical White Paper for the Optimal Network ... - Huawei Network Performance Antenna Solution ... parameters must be selected based on ... Technical White Paper for the Optimal Network

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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.

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Copyright © Huawei Technologies Co., Ltd. 2012. All rights reserved.

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holders.

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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]

http://www.huawei.com/

mailto:[email protected]




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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




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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.




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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.




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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




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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:




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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




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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.




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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




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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)




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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:




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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.




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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.




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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

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Technical White Paper for the Optimal Network ... - Huawei Network Performance Antenna Solution ... parameters must be selected based on ... Technical White Paper for the Optimal Network