The Benefits of Adaptive Antennas on Mobile Handsets for 3G Systems

Final Report

February 2003

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RA1002/R/17/105/3

This report was commissioned by the Radiocommunications Agency.

Copyright 2003 Multiple Access Communications Ltd

Multiple Access Communications Ltd Delta House, Enterprise Road Chilworth Science Park SOUTHAMPTON SO16 7NS, UK Tel: +44 (0)23 8076 7808 Fax: +44 (0)23 8076 0602

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Executive Summary

The Benefits of Adaptive Antennas on Mobile Handsets for 3G Systems

Final Report

Adaptive antenna technology at a cellular base station (BS) has been a subject of interest for

many years. With ongoing advancements in the performance of semiconductor technology,

chipsets that are both smaller and more powerful are now available for hand-held mobiles.

This trend is not expected to slow in the near future and thus the possibility of incorporating

smart antenna technology in the handsets seems more possible. In addition there have been

recent advances in antenna technology itself that allow small antennas to be located closer

together. With this in mind the Radiocommunications Agency (RA) has asked Multiple

Access Communications Limited (MAC Ltd) to investigate the current state of the art of

smart antenna technology for handsets and the likely performance enhancements they may

give to a Universal Mobile Telecommunications System (UMTS) network having the UMTS

Terrestrial Radio Access (UTRA) frequency division duplex (FDD) network radio interface.

The RA is particularly interested in a quantification of the effect of using smart handset

antennas on the following network parameters.

Capacity of the network.

Base station density in the network.

Data rates achievable in the network.

Size of dead zones caused by adjacent channel interference.

To begin with, MAC Ltd performed a three-week literature search during which it examined

conference and journal papers, contacted experts in the field, searched the internet and spoke

to various companies. A principal finding was that the gain in signal-to-interference plus

noise ratio (SINR) performance of an adaptive antenna over a single antenna followed a

log-normal probability density distribution. Diversity combining techniques achieved 6 to

9 dB gain in SINR, for the 99% reliability level. However, if interference rejection combining

was used, the gain in SINR increased to 23 dB in the presence of a single strong interferer.

This gain decreased significantly to 16 dB in the presence of two interferers. Multiple input

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multiple output (MIMO) adaptive antenna technology was also considered, but it was

concluded that the technology was not currently suited to hand-held mobiles, and neither is it

being deployed in 3G BSs.

The next step was to formulate a model for an ideal smart antenna in the handset. A statistical

model was created for the improvement in SINR based on distribution curves derived from

publications. The model was found to be of the log-normal type having mean and standard

deviation values shown in Table A. Observe that when the smart antenna is in line-of-sight of

the serving sector transmitter, the performance is not as good as when the antenna cannot see

the serving sector. Furthermore, as the number of interferers increases, the performance of the

smart antenna deteriorates.

The statistical model of the smart antenna was incorporated into MAC Ltds code division

multiple access (CMDA) network simulation tool, MACcdma, and simulations were run that

compared the performance of the network when the handsets used either a conventional

omnidirectional antenna or the smart antenna. Signal coverage was predicted over a 25 km2

area of Central London and simulations were run over a 9 km2 area for different capacities,

base station densities and voice/data services. Typical parameters for a 3G network having a

UTRA FDD radio interface were used.

To investigate the effect of using the smart antenna on the capacity of the network we

considered the percentage of the simulation area with a blocking probability that is equal to

or less than 2%. This was defined as the figure of merit (FoM). It was found that using the

smart antennas increased the capacity of a network by about a third when the FoM was

maintained at 95%. As the offered traffic increased, not only did the FoM decrease, but so did

Line-of-sight Non Line-of-sight Number of Interferers Mean (dB) Standard Deviation (dB) Mean (dB)

Standard Deviation (dB)

1 12.5 10.6 22 11.4

2 6.4 9.6 14 10.4

3 6.4 8.6 11.4 9.4

4 or more 6.4 7.6 8.8 8.4

Table A Mean and standard deviation values for the improvement in SINR performance in the presence of interferers.

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the improvement in capacity that a smart antenna gave over an omnidirectional antenna. We

also considered the effect of using the smart antenna to decrease the number of BSs that are

needed for a given capacity and FoM. We found that approximately 8% to 15% fewer BSs

were needed. However, this figure should be accepted with caution since we also found that

the performance was highly dependent on how well the network had been optimised for co-

channel interference. The smart antenna was also found to allow higher data rates on the

network. Between 20% and 100% higher data rates could be used with the smart antenna.

The improvement was found to be highest for low data rates and to decrease when high data

rates are used. Finally, we considered dead zones that are caused by adjacent channel

interference (ACI). It was found that smart antennas reduced dead zones to about a third of

the size that existed when only an omnidirectional antenna was used in the handsets.

However, the reader should be aware that distinguishing between areas of high blocking

caused by ACI and areas of high blocking due to co-channel interference is, to some extent, a

subjective process.

As a further study for the RA we considered the potential of using smart antennas in rural

environments to reduce the required number of BSs. In these environments we found that the

up link (UL) was the limiting link. Unless the BS also employed some form of adaptive

antenna technology there was little benefit in improving the down link (DL) with smart

antennas in the handsets. However, if the traffic was heavily DL biased the network may

become DL limited and smart antennas in the handset receiver would prove useful to improve

the link.

In light of the work that has been performed over the course of this project we recommend

that the following work be carried out in the future. First, it is suggested that more

simulations be run to bring more confidence to the current results and also to allow the trends

to be analysed in more detail. Secondly, it would be a useful exercise to investigate the

impact of radio resource management on the network. Examining a network in which there is

a mixture of users with handsets using omnidirectional antennas and handsets using smart

antennas could be very interesting. Finally, we recommend that studies are performed of

scenarios in which BS and mobile station (MS) adaptive antennas are combined into one

adaptive system.

Prepared by Multiple Access Communications Ltd

February 2003

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Table of Contents List of Abbreviations ............................................................................................................... 10

1 Introduction...................................................................................................................... 13

1.1 Organisation of the Report....................................................................................... 15

2 Literature Search .............................................................................................................. 15

2.1 Diversity Combining................................................................................................ 16

2.2 Beamforming ........................................................................................................... 18

2.3 MIMO technology ................................................................................................... 19

2.4 Recent Developments .............................................................................................. 21

2.4.1 Allgon Mobile Communications.......................................................................... 22

2.4.2 Virginia Polytechnic Institute and State University............................................. 23

2.4.3 University of Surrey Centre for Communication Systems Research................... 24

2.4.4 Philips Research Laboratories, Eindhoven, Holland ........................................... 25

2.5 The interaction of the antenna with the body........................................................... 26

2.6 Conclusions.............................................................................................................. 27

3 Development of a Statistical Model for a Smart Antenna ............................................... 28

3.1 Deriving the Log-Normal Distribution .................................................................... 29

3.1.1 The Non Line-of-Sight Case................................................................................ 29

3.1.2 The Line-of-Sight Case........................................................................................ 36

3.1.3 Number of Interferers .......................................................................................... 37

3.2 Velocity of the Handset ........................................................................................... 39

3.3 Differences Between Wideband and Narrowband Signals ...................................... 40

4 Simulation Procedures and Parameters............................................................................ 40

4.1 Networks and Coverage Predictions........................................................................ 41

4.1.1 Main Network ...................................................................................................... 42

4.1.2 Adjacent Network ................................................................................................ 43

4.1.3 Reduced Networks ............................................................................................... 44

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4.1.4 Uniform Networks ............................................................................................... 44

4.2 Monte Carlo Simulations using MACcdma............................................................. 46

4.2.1 Introduction to MACcdma ................................................................................... 46

4.2.2 Modifications to NP WorkPlace .......................................................................... 47

4.2.3 Modifications to MACcdma ................................................................................ 48

4.2.4 MACcdma Parameters ......................................................................................... 49

4.3 Description of Tests ................................................................................................. 49

4.3.1 Increase in Capacity............................................................................................. 50

4.3.2 Decrease in Base Station Density ........................................................................ 50

4.3.3 Increase in Data Rate ........................................................................................... 51

4.3.4 Reduction in Dead Zone Size .............................................................................. 51

5 Simulation Results ........................................................................................................... 52

5.1 Increase in Capacity................................................................................................. 53

5.2 Decrease in Base Station Density ............................................................................ 56

5.3 Increase in Date Rate ............................................................................................... 59

5.4 Reduction in Dead Zone Size .................................................................................. 61

5.5 Rural Link Budget.................................................................................................... 62

6 Summary of Results and Conclusions ............................................................................. 64

References................................................................................................................................ 68

Appendix A MACcdma Parameters ..................................................................................... 72

1 Simulation ........................................................................................................................ 72

1.1 Number of Simulation Snapshots ............................................................................ 72

1.2 Global Traffic Scale Factor...................................................................................... 72

1.3 Output Bin Size........................................................................................................ 72

1.4 Output Statistics Area .............................................................................................. 72

2 Network............................................................................................................................ 72

2.1 Orthogonality Factor................................................................................................ 72

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2.2 Pilot Channel Required Ec/I0.................................................................................... 73

2.3 Attenuation of Extra Interference ............................................................................ 73

2.4 RAKE Efficiency Factor.......................................................................................... 73

2.5 Down Link Noise Figure ......................................................................................... 73

2.6 Down Link Line Loss .............................................................................................. 73

2.7 Up Link Noise Figure .............................................................................................. 73

2.8 Up Link Line Loss ................................................................................................... 73

2.9 Maximum Traffic Channel Power ........................................................................... 74

2.10 Minimum Traffic Channel Power............................................................................ 74

2.11 Relative Pilot Channel Power .................................................................................. 74

2.12 Relative Common Channels Power ......................................................................... 74

2.13 Relative Total Traffic Power ................................................................................... 74

3 Services ............................................................................................................................ 74

3.1 Max MS Transmit Power......................................................................................... 75

3.2 SHO Enabled ........................................................................................................... 76

3.3 Processing Gain ....................................................................................................... 76

3.4 Required Eb/I0 .......................................................................................................... 76

3.5 Channels of this Type .............................................................................................. 76

3.6 Source Activity Factor ............................................................................................. 76

3.7 Transmit Cycle......................................................................................................... 76

3.8 Relative Power ......................................................................................................... 76

4 Call Admission Control ................................................................................................... 76

4.1 Call Admission Control Algorithm.......................................................................... 77

4.2 Down Link Power Headroom .................................................................................. 77

4.3 Maximum Reduction in Up Link............................................................................. 77

5 Soft Handover .................................................................................................................. 77

5.1 Up Link Margin ....................................................................................................... 77

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5.2 Maximum Active Set Size ....................................................................................... 77

5.3 Add/Drop Threshold ................................................................................................ 77

5.4 Add/Drop Hysteresis................................................................................................ 77

5.5 Replacement Hysteresis ........................................................................................... 78

Appendix B Extended Bibliography .................................................................................... 79

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List of Abbreviations 2G Second Generation

3G Third Generation

3GPP Third Generation Partnership Project

ACI Adjacent Channel Interference

ACLR Adjacent Channel Leakage Ratio

BLAST Bell Laboratories Layered Space-Time

BER Bit Error Rate

BS Base Station

CAC Call Admission Control

CCSR Centre for Communication Systems Research

CDF Cumulative Distribution Function

CDMA Code Division Multiple Access

CPICH Common Pilot Channel

DECT Digital European Cordless Telephone

DL Down Link

EAG Effective Antenna Gain

EGC Equal Gain Combining

ETSI European Telecommunications Standards Institute

FDD Frequency Division Duplex

FoM Figure of Merit

GSM Global System for Mobile Communications

IRC Interference Rejection Combining

IQHA Intelligent Quadrifilar Helical Antenna

LOS Line of Sight

MAC Ltd Multiple Access Communications Limited

MEG Mean Effective Gain

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MIMO Multiple Input Multiple Output

MPRG Mobile Portable Radio Research Group

MRC Maximal Ratio Combining

MS Mobile Station

NLOS Non Line of Sight

PDF Probability Density Function

QoS Quality of Service

RA Radiocommunications Agency

R&D Research and Development

RF Radio Frequency

SAR Specific Absorption Rate

SC Selection Combining

SDMA Space Division Multiple Access

SHO Soft Handover

SINR Signal-to-Interference plus Noise Ratio

SIR Signal-to-Interference Ratio

SMS Short Message Services

SNR Signal-to-Noise Ratio

STC Space-Time Coding

TDD Time Division Duplex

TDMA Time Division Multiple Access

UK United Kingdom

UL Up Link

UMTS Universal Mobile Telecommunications System

UTRA UMTS Terrestrial Radio Access

VCE Virtual Centre of Excellence

VTAG Virginia Tech Antenna Group

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VLSI Very Large Scale Integration

VTVT Virginia Tech VLSI Telecommunications

WCDMA Wideband Code Division Multiple Access

WTEC World Technology Evaluation Centre

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1 Introduction Commercially viable third generation (3G) networks are conspicuous by their absence in

Europe, in spite of the first European Telecommunications Standards Institute (ETSI) / Third

Generation Partnership Project (3GPP) specification issue, known as Release 99, in

December 1999. Since then we have had Release 4 in March 2001, and currently we are on

Release 5, issued in December 2001. The operators spent vast amounts of money on

acquiring the 3G licences, and are now faced with the huge costs of purchasing the 3G

equipment and deploying it. Nevertheless, the networks are being installed and the first 3G

network in the United Kingdom (UK) is expected to be operational in the next few months.

The 3G networks will be radically different from the previous second generation (2G) ones.

Instead of being focused on circuit-switched voice and short message services (SMS), 3G

will have, in addition, multimedia services. 3G will accommodate both circuit-switched and

packet data services, with transmission rates that in principle may be 2 Mbps, although the

maximum rate is more likely to be 384 kbps or only 128 kbps. Both symmetrical and

asymmetrical transmissions will be supported. The radio access method in 3G is based on

code division multiple access (CDMA), rather than the time division multiple access

(TDMA) used in the Global System for Mobile Communications (GSM). Therefore, the

interference conditions in 3G networks are radically different from those in GSM networks.

In 3G we have intracellular interference from users in their own cell, a situation that does not

occur in GSM. Further, as all cells may use the same carrier frequency, there is intercellular

interference from all cells. Much research has been directed to decreasing the intracellular

interference, eg, by using multi-user detection; and for mitigating the effects of intercellular

interference, eg, by using adaptive antennas at base stations (BSs) that, in addition to tracking

the wanted signal, are able to steer nulls in the antenna pattern towards the interfering signals.

The greater the amount of interference that can be removed, the more users that can be

accommodated for the same service. For example, for a single cell and using multi-user

intracellular cancellation methods, it has been shown [1] that, in the presence of multiple

users, the performance for any user can be made the same as if only one user was present. So

the quest to decrease interference in CDMA systems is worthwhile as it results in huge

performance gains in 3G networks.

The radio spectrum in the UK is regulated by the Radiocommunications Agency (RA). The

RA requires the spectrum to be used efficiently and effectively for the benefit of the nation,

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and accordingly spectral efficiency for good service provision is one of its key aspirations.

While the RA waits for 3G networks to become operational in the UK, it needs to know

which future technologies might result in major enhancements in spectral efficiency in 3G

networks. Such enhancements will manifest themselves by providing: (1) a significant

increase in the teletraffic carried for a given network spectrum allocation and BS density; (2)

a decrease in the number of BS sites for a given teletraffic and spectrum allocation; (3) an

increase in user bit rate; (4) a decrease in the number and size of dead zones due to adjacent

channel interference; and so on. One technology emerging from current research and

development (R&D) activities that seems capable of achieving these enhancements is

adaptive antenna technology for handsets. While diversity, beam switching, and

beamforming with interference cancellation have been used at BSs, where antenna size and

spacing, as well as processing power, are not critical, introducing such techniques into the

small handsets we have today is a daunting problem. This is because the physical size of

current handsets is smaller than a persons hand, and as the 3G Universal Mobile

Telecommunications System (UMTS) frequency division duplex (FDD) band is from 1920 to

2170 MHz, the half wavelength, and hence the separation required between the antenna

elements, is some seven centimetres. Therefore, the ability to deploy multiple antennas within

the restricted space of a small handset while ensuring that the signal fading on each antenna is

essentially uncorrelated is a very difficult task. Conventionally, the spacing between multiple

antennas is usually greater than half a wavelength, and to pack antennas closer together than

this requires novel concepts to ensure that the signal correlation between the received signals

on each antenna is low.

In October 2002 the RA issued an invitation to organisations to tender for a four-month

investigation into the benefits of adaptive antennas in mobile handsets for UMTS systems.

The RA required that the investigation commenced with a short literature search to identify

the current state of R&D in adaptive antenna technology for small 3G handsets. Next, on the

assumption that such handsets will exist in the future and will be universally deployed, the

RA wanted to know the increase in UMTS FDD network performance that would accrue

compared to when conventional omnidirectional antennas are used. Network performance

parameters of specific interest to the RA were the capacity, BS density, user bit rate, and the

dead zones due to the interference from an adjacent network.

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In our tender to the RA we proposed the following methodology. From the findings of a

literature search we would derive a statistical model for the signal-to-interference ratio (SIR)

of a handset having an adaptive time-varying antenna pattern to one having the conventional

fixed omnidirectional antenna pattern. The adaptive antenna would attempt to maximise the

SIR, ie, it would minimise the interference as well as tracking the wanted signal. This model

of the SIRs would then be integrated into the Multiple Access Communications Limited

(MAC Ltd) 3G simulator, called MACcdma. The UMTS simulations would then be run for

an area of Central London, and the improvements in network performance ascertained for

speech users only, 64 kbps data users only, and 144 kbps data users only. MAC Ltd was

awarded the contract, and what follows is the result of our investigation.

1.1 Organisation of the Report

The first task was a literature search, which included contacting international experts in the

field, as well as having discussions with companies who are in the business of making

relevant antennas. Section 2 describes these findings, and enables us in Section 3 to propose a

statistical model of a handset having a smart antenna that can adapt to changes in mobile

location and the number of interfering BS transmissions. We emphasise that the smart

antenna is assumed to have an impact on the down link (DL) only, ie, the BS to mobile

station (MS) link, and that the BS is considered to have a single antenna. Section 4 gives a

detailed description of our simulation procedures; specifically, it addresses how our UMTS

planning tools are modified to facilitate the statistical model we have derived for the handset,

and then describes the network parameters and services to be used in the simulation. Section

5 gives the results of the simulations, and their analysis. The final chapter discusses our key

findings, and identifies further work that is necessary to achieve a greater understanding of

the value of this technology for handsets as 3G networks mature.

2 Literature Search There has been much interest in deploying smart antennas at the BSs in cellular networks to

track up link (UL) transmissions from roaming handsets within their cells, while at the same

time rejecting intercellular interference from mobiles in neighbouring cells [2]. By contrast

comparatively little research and development has been done on incorporating smart antenna

technology into the tiny handsets that are currently in use. Indeed, the problem of introducing

multiple antennas into the confines of a handset, and being able to optimise the antenna

pattern with the movements of a user in the signal scattering environment that characterises

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the handset environment is not for the faint-hearted. Fortunately, there are always a few who

will address difficult problems if the potential gains in network performance are sufficiently

enticing. At the current time a few companies and research organisations [3][4][5] have

experimental results that encourage us to believe that the smart antenna handset will be

realisable and available in the future. The RA has asked MAC Ltd to place emphasis on

identifying the performance enhancements of using smart antennas on hand-held mobile

phones as opposed to larger mobile phones. This is because hand-held phones are expected to

be the principal mobile equipment used.

The term, smart antenna, refers to signal processing performed on the received or transmitted

signals at an array of antennas. This processing yields a resulting signal that has an enhanced

quality when compared to the signal associated with a single antenna. There are three types of

systems that use multiple antenna arrays: diversity combining, beamforming, and multiple

input multiple output (MIMO). Experiments involving diversity combining were reported as

early as 1927 [6]. Adaptive beamforming was developed in the 1960s for sonar and radar [7]

and it was not until the early 1980s that the application of beamforming for cellular systems

was seriously considered [8]. MIMO technology is the latest and most disruptive of

technologies; disruptive because the MIMO technology offers the prospects of huge gains

in bits per second per Hertz, a revolutionary step forward in spectrum provisioning. Much is

owed to the pioneering work of Winters [8] and the conceptual work of Foschini [9].

Provision for MIMO technology has been made in the 3G specification, although it is

unlikely that the 3G networks will use it for some years because of its complexity. We will

now briefly discuss each of these multi-antenna technologies.

2.1 Diversity Combining

In this report we are concerned with the DL in mobile cellular systems. Multipath fading on

the DL is caused by the transmitted signal from the BS following multiple paths to the

receive antenna [6]. The signals emerging from each path normally have different amplitudes,

phases and polarisations such that when vectorially added together at the receiver the

resultant received signal is characterised by rapid changes with time and receiver position.

We may consider the antenna patterns of the individual antennas to be time-invariant and

their received signals to have a low correlation. Diversity combining exploits the differences

in the received signals, due to multipath, at two or more antennas to mitigate fading and

improve the overall quality of the signal. For example, for a two antenna switched diversity

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reception combiner, the best signal is selected. The probability of both signals at the receive

antennas being in a deep fade at the same time is small compared to one of the received

signals being in a fade. For higher order switched diversity, when there are many antennas,

there is always a high probability that one of the received signals is not faded. In this situation

the fading channel, after diversity reception, resembles a Gaussian channel [10]. This gives a

diversity gain, defined as the reduction in the required average input signal-to-interference

plus noise ratio (SINR) at each antenna for a given bit error rate (BER) with fading. There are

three principal forms of diversity: spatial, polarisation, and angle (or pattern) diversity [11].

Spatial diversity has been mentioned above. Placing the antennas sufficiently far apart in

space enables the combiner to exploit the fact that the received signals at the antennas are

essentially uncorrelated. Polarisation diversity is somewhat limited as there are only two

orthogonal polarisations, and therefore it is limited to second-order diversity. Again the

fading of the signals on each antenna is required to be uncorrelated. In angle diversity there is

a set of antennas pointing in different directions, and in the scattering environments found in

cellular radio the signals on the different antennas will exhibit different fading characteristics.

Notice that if we have only one receiver antenna the result is fading that will have a

deleterious effect on performance, but if we have multiple antennas and arrange them such

that the fading on each antenna is independent, then we thank nature for providing signals in

numerous forms, which allows us to extract a single signal when the fading is minimal.

The signals received at each of the antenna elements can be combined in different ways to

mitigate the effects of fading [12]. We referred above to the simplest diversity combining

method, selection combining (SC), where the strongest signal is chosen from the array of

antenna outputs. A superior, but more complex, method is maximal ratio combining (MRC).

Here, the first step is to cophase the signals at the output of each antenna, and then the signals

are weighted proportionately to their individual SINRs. This results in the individual SINRs

being summed to give a maximised SINR. A simpler version of MRC, called equal gain

combining (EGC), gives all of the input signals the same weighting before summing the

individual SINRs.

Traditionally, the accepted antenna spacing between the elements of an antenna array is

between a half and one wavelength. The optimum spacing between antenna elements in a

two-element broadside linear array is about 0.7 [13], where is the wavelength of the signal. This increases to about 0.8 for a four-element array. The UMTS Terrestrial Radio

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Access (UTRA) FDD DL frequency band is 2110 - 2170 MHz in the UK. This corresponds

to a wavelength of 14 cm. At this frequency range a two-element broadside linear array needs

a separation of about 10 cm and is too wide to fit into a small handset. However, Winters [11]

points out that as a handset is typically surrounded by scatterers, antenna separations as small

as /4 will still enable the signals at the antennas to have a low correlation. This means that, at the UTRA FDD frequencies, antenna separations of less than 4 cm can be used. Private

correspondence with experts in the field has suggested that antennas could be spaced as close

together as an eighth of a wavelength.

We observe that in these classical diversity schemes the interference is not explicitly handled,

nor is the required signal explicitly catered for. The idea is that the antennas will receive the

desired signal plus noise and interference, and given this situation, the combiner will attempt

to maximise the SINR. There is no feedback to the antennas to change their antenna patterns

to yield further improvements. Steering antenna beams in the direction of the wanted signal,

while forming nulls in the antenna pattern to essentially ignore the interfering signals comes

under the category of beamforming techniques; a subject we will address next.

2.2 Beamforming

Perhaps the simplest method of providing a narrow beam to a receiver is to use an array of

antennas, in which each antenna has a fixed narrow beam pointing in a specific direction. For

example, there may be 12 radial beams each covering a sector of 30 degrees, and a handset

uses the beam serving the sector in which it is positioned. Because of the narrowness of the

sectors the interference is, in general, decreased. This type of arrangement is referred to as a

switched antenna system and is usually deployed at a BS [14]. Beamforming arrays can be

deployed at a BS to provide multiple beams, where each beam independently tracks different

roaming handset receivers. These arrays can be linear, circular, or planar [15]. Usually half

wavelength spaced antenna elements create the spatially selective beams that allow multiple

user signals to be supported within the same bandwidth at the same time. More advanced

systems adjust their antenna radiation pattern formed from a set of antennas to steer beams at

the strong multipath signals of the wanted signal, and steer nulls at the significant interferers,

and in doing so optimise the SINR. For N antenna elements, the receiver can effectively

combat N-1 interferers [11]. In these systems the antenna pattern must be continually

modified as the user roams in order to optimise the performance. In principle these antennas

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can be used in the BSs and in the handsets. However, at present they are used exclusively in

the BS domain, but we will be considering their application in handsets.

2.3 MIMO technology

In recent years a great deal of research has gone into developing multiple input multiple

output antenna systems, in which different data may be transmitted from each of the multiple

antennas. There are numerous types of these systems with radically different aims, and we

will refer to all of them as MIMO systems. Some people attach a specific definition to

MIMO, one that achieves a high data rate per user, ie, the so-called Bell Laboratories Layered

Space-Time (BLAST) technology from Lucent. However, using our definition, one type of

MIMO system is space-time coding (STC). Here the receiver antenna elements are spaced

sufficiently far apart to ensure that the fading on each element is statistically independent.

There are two basic types: space-time block codes [16], and space-time trellis codes [17].

Both provide transmit diversity and sometimes receiver diversity, enhancing the data integrity

without increasing a users data throughput. As a simple example of space-time block codes,

consider the case of two antennas at the BS transmitter and two receiver antennas at the

handset. Instead of transmitting the same data on both antennas we may transmit different

data on each antenna. Since both transmissions will arrive at the two receiver antennas we

need to be able to untangle the two sets of data. We can do this using the method proposed by

Alamouti [16]. In this method we create a frame having two time slots, and we have data x

and data y to transmit. For the first time slot we form the complex conjugate of minus the

data x, namely -xc, and transmit this from antenna A1, while at the same time we transmit

data y from antenna A2. In the next time slot we transmit from these two antennas x and +yc,

respectively. On the provisos that we sound the two channels to get their impulse responses,

and that the channels are not too dispersive, we can recover both x and y. Although the data

rate has not increased, we have used both time and space diversity to significantly improve

data integrity.

Another MIMO system is space division multiple access (SDMA) [18]. Consider the case of

two mobiles, each having one antenna, while the BS has two antennas. The transmissions

from the two mobiles travel via different paths as they are in different locations, and as far as

the BS is concerned it is dealing with four different channel impulse responses, two from

each of the two mobiles. Armed with accurate estimates of these channel impulse responses,

the signals from both mobiles, transmitting in the same band at the same time, can be

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determined. SDMA has the ability in this example to double the throughput and improve data

integrity.

MIMO systems are able to increase dramatically a users data rate to such an extent that it

represents a fundamental improvement in spectral efficiency. This concept was postulated by

Foschini [19], and is known as BLAST. In conventional diversity systems the Shannon

capacity of a system grows with the log of the number of antennas used [9]. In BLAST, if

there are M transmit antennas and N receive antennas that have independently fading signals,

the capacity of the system grows linearly (rather than logarithmically) with the smallest

number of antennas, min(M,N) [19]. This represents a vast improvement over traditional

smart antennas. For example, Lucent has demonstrated that a transmission rate of 1.2 Mbps

can be realised in 30 kHz using eight transmit and 12 receive antennas in an indoor

environment [2]. This corresponds to 40 bits per second per Hertz. Higher efficiencies have

been reported, although the efficiency is dependent on there being many radio paths, ie, on

the richness of the multipath environment.

There are several problems that face the BLAST technology [20]. Much of the research has

assumed perfectly uncorrelated channel models whereas, in reality, signals at different

receiver antennas will be partially correlated. MIMO systems do not yield high values of bits

per second per Hertz if correlation between the antennas received signals is too high. The

capacity gains described above also assume that the complexity of the signal processing

required is acceptable. In practice there often has to be a trade-off between complexity and

performance. There is also the problem of antenna separation at the handset. To maximise the

data throughput the antennas need to be sufficiently separated to ensure low cross-correlation

between the received signals. This is difficult to achieve on a small handset at the frequencies

used for 3G. However, it has been estimated that MIMO will work at an antenna spacing of

0.25 to 0.3 of a wavelength [21]. At 2 GHz the wavelength is 15 cm, requiring an antenna

separation of about 5 cm. There is speculation that, due to the scattering of the signals around

the body or head, 1/8 may be possible. Gesbert, Ekman and Christophersen [22] have considered placing antennas within an array one wavelength wide. Their work showed that

six antennas provided a capacity about 6.5 times greater than that provided by just one

antenna. Increasing the number of antennas further does not give further improvement. This

antenna is still considered to be too wide for a hand-held mobile phone.

21/83

In this literature search we did not find any recent work that advocated BLAST for small

hand-held antennas. Measurement campaigns using BLAST have been performed in New

York City but these were for at least five or more antennas at the transmitter and the receiver

[23][24][25]. However, based on the potential capacity gains, future research may prove

exceedingly fruitful, and Lucent has proposed that its BLAST technology be used for 4G.

The work of Lucent Technologies, Stanford University and Iospan Wireless (now owned by

Intel) should be followed closely as they are all actively engaged in developing this

technology.

2.4 Recent Developments

Using smart antenna technology in the handset traditionally has not been considered feasible,

but a report by the World Technology Evaluation Centre (WTEC) highlights the work that

various companies are currently pursuing [3]. Philips engineers observed that 50% of the

power consumed in the handset is due to the radio frequency (RF) electronics. To conserve

battery life and cost while changing from the simple, single antenna to a smart antenna array

requires that the cost and signal processing power per antenna must significantly decrease.

The company is also interested in dual polarisation diversity, but because the diversity order

is only two, other forms of diversity will still be required. ATR, an antenna company based in

Japan, is investigating the integration of antennas into the RF electronics chip. Problems with

antenna gain are expected when they do this, and the effect of the hand on the terminal is of

concern. Nokia is studying multiple antennas in handsets and combating the effect of the

hand on the multiple antenna pattern, perhaps by using only those antennas not affected by

the presence of the hand, or by compensating for it by adjusting the antenna impedance.

Nearly every company the WTEC visited is doing significant research on smart antenna

technology, although most of the effort is still for adaptive multiple antennas to be used at the

BSs, rather than in the handsets. What appears to be a universal opinion is that smart antenna

technology is necessary for the enhancement of future wireless cellular networks. It appears

that companies and research organisations are beginning to overcome the basic concerns of

size, cost and power consumption that multiple antennas in handsets present.

Current research and development focus on using smart antennas in handset receivers to

maximise the signal quality on the DL. Specifically, the handset receiver is required to

optimise the SINR. The UTRA FDD common pilot channel (CPICH) may be conveniently

employed to characterise the forward channel and thus produce a better adaptive array [26].

22/83

In FDD transmissions the handset transmitter does not know the current characteristics of the

UL propagation environment, and therefore the handset transmitter does not know the

optimum transmission configuration to use for its smart antenna array (should it have one).

This problem is overcome if the time division duplex (TDD) version of UMTS is used.

Although companies are researching the use of smart antenna technology in handsets, it has

been difficult to access information directly from these companies. Most of the published

material we have acquired has been obtained directly from universities or industry-university

partnership consortia, or through the traditional sources of journal and conference papers. The

web was another source of high-level information. We also contacted known experts in the

field by email and telephone to gain their insights and opinions on the current state of smart

technology for handsets. Several companies were also contacted directly for their views, and

although their opinions often contained confidences that we cannot reveal, the discussions

were nevertheless helpful. We would like to thank Dr David James of ArrayCom for visiting

us and giving us a stimulating lecture on his companys adaptive array technology; Dr

Reinaldo Valenzuela of Lucent Technologies for comments and articles; Dr Arogyaswami

Paulraj of Stanford University for his information; Dr Simon Saunders of the University of

Surrey for a bibliography; and Professor Jrgen Bach Anderson of Aalborg University,

Denmark, for his comments and references. Finally, we would like to thank Erling Erlingsson

and Colin Ribton from Antenova for an informative description of their small antenna

technology and for providing a useful bibliography.

We will now detail the main avenues that were pursued in the literature search.

2.4.1 Allgon Mobile Communications

Allgon Mobile Communications is a company based in Sweden that is collaborating with the

Hong Kong University of Science and Technology in the field of adaptive antenna

technology for mobile handsets. They have investigated the use of two handsets having two-

branch antenna systems: one had a quarter-wave monopole and a shorted patch antenna;

while the other employed a monopole and a planar antenna. Two methodologies were used,

the first being a computer simulation and the second using a measurement process.

In their first methodology the antenna patterns were measured and used in a computer model

of the antennas. A theoretical model of the incoming multipath scattered signals was also

created. In the simulation the antenna radiation patterns were moved through the incoming

23/83

signals. The signals received at the antennas were then stored and further processed to

determine the performance of the interference rejection combining (IRC), EGC, and SC

algorithms.

Their second technique was experimental, employing the two handsets in an indoor

environment and in the presence of a phantom head and hand. Different measurement routes

were used to simulate the effect of signals coming from different transmitters and the

received complex antenna signals were sampled as the mobiles, with their phantoms,

traversed these routes. The propagation frequency used was 1805 MHz [27]. The mobile

receivers were not in line-of-sight (LOS) of the transmitter, and the cross-correlation of the

received signals at the two antennas was low. The received signals at the antennas were then

processed off-line in non real time to identify the performance of the three diversity

algorithms.

In both the simulation and measurement techniques the signals were weighted to achieve a

mean SIR of 15 dB at the antennas in the presence of a single interferer. The measurements

were repeated using two interferers where the first interferer was kept at 15 dB below the

carrier signal power at the handset, whilst the second interferer was set to be 20 dB below the

carrier signal power. By these means data were acquired and used to evaluate the

performance of diversity in the presence of one or two interferers. The main results revealed a

diversity gain of 7 dB and 9 dB at the 99% reliability level when SC and EGC, respectively,

were used in the presence of a single interferer. This means that for 99% of the time the SIR

due to SC was 7 to 9 dB better than that due to a single antenna. However, the diversity gain

increased to 23 dB for IRC. In the presence of the second interferer, SC and EGC performed

about the same as with a single interferer, but IRC now gave a diversity gain of 16 dB.

2.4.2 Virginia Polytechnic Institute and State University

The Virginia Polytechnic Institute and State University in the USA has been investigating

smart antennas. Dietrich [12] has compared different diversity techniques using two-antenna

arrays. Propagation measurements were performed using a 2.05 GHz unmodulated carrier

wave, and diversity gains of 7 to 9 dB for the 99% reliability level were reported. Dietrich

has also expanded on the work of Braun et al. [27] to achieve diversity gains greater than

20 dB in the presence of a single interferer.

24/83

Sponsored by the US Navy and Texas Instruments, the Mobile Portable Radio Research

Group (MPRG) and the Virginia Tech Antenna Group (VTAG) [28] performed handset

transmit diversity measurements for an indoor channel at 2.05 GHz. A 5 MHz bandwidth

carrier was used with binary phase shift keying modulation. In a LOS environment diversity

gains of 2 to 7 dB were found for a 99% reliability level. This gain increased to 10 dB for non

line-of-sight (NLOS) conditions.

Although these measurements are promising, we will not include transmit diversity in the UL

in our investigations. This is because transmit diversity needs a feedback loop in the

measurement campaign. Closed loop mode transmit diversity is supported at the BS of a

UTRA FDD system [29], but does not appear to be supported at the mobile.

The Virginia Tech Very Large Scale Integration (VLSI) for Telecommunications Laboratory

(VTVT) has performed simulations for both diversity combining and adaptive combining in a

simulated UTRA FDD network [26][30][31]. Two antennas, separated by a quarter of a

wavelength, were assumed. The interference from another adjacent channel was also

modelled at the receiver. In these simulations there was an improvement of up to

approximately 5.5 dB relative to that of a single omnidirectional antenna. However, further

study is needed to investigate the impact of specific model parameters on the results. This

work was done in conjunction with LG Electronics.

2.4.3 University of Surrey Centre for Communication Systems Research

The Centre for Communication Systems Research (CCSR) at the University of Surrey has

been developing an intelligent quadrifilar helical antenna (IQHA) [32]. The work was

performed in conjunction with Nokia Mobile Phones, UK, and the Mobile Virtual Centre of

Excellence (VCE). This dual-band handset antenna deploys adaptive technologies that can

switch between a hemispherical pattern for satellite communications and a toroidal pattern for

terrestrial communications.

SC, MRC and EGC diversity schemes were compared. In a LOS environment the diversity

gain was negligible [33], eg, the diversity gain for the 99% reliability level was

approximately 0.5 dB using EGC. The performance was better in a NLOS environment as the

mean diversity gain increased to about 13 dB for EGC. Further improvements may be

anticipated as the research is continuing.

25/83

2.4.4 Philips Research Laboratories, Eindhoven, Holland

Dolmans and Leyton from the Philips Research Laboratories, Eindhoven, Netherlands, have

developed an adaptive dual antenna handset for the Digital European Cordless

Telecommunication (DECT) system for use in indoor environments [34]. EGC diversity was

used with two antennas, and in the NLOS case they reported a diversity gain of 9 dB with a

99% reliability level.

2.4.5 Antenova Limited

Antenova Limited, an antenna company, is developing a new generation of antennas with

high dielectric, smaller size, and higher efficiency than conventional antennas, and with an

immunity to detuning while having directional and steerable properties [5][35]. The RA

considered the work of Antenova to be significant to this project and asked MAC Ltd to have

discussions with the company. Accordingly MAC Ltd had a meeting with Antenova on 8th

November 2002, when Antenova informed us that it is able to produce isolated antennas that

can be positioned within millimetres of each other. These antennas are able to form beams

with a nominal beamwidth of about 80 degrees. At the time of writing Antenova was

expected to release imminently a dual antenna wideband CDMA (WCDMA) demonstrator

with an anticipated gain over conventional antennas of about 6 dB for the 99% reliability

level. Antenovas antennas have also been used in trials with Innovics Wireless Trailblazer

product that has recently been announced [4]. This has an anticipated diversity gain of 7 dB

over conventional receivers, for a 99% reliability level.

It was interesting to note that Antenova had performed a literature search similar to this one

and they observed that much of the research into adaptive antennas for handsets reveals that,

for diversity combining using a dual antenna system, a gain of about 6 to 9 dB over a

conventional antenna for the 99% reliability level is realised. These references are included in

the extended bibliography at the end of the report. Antenova found just two research

programs that achieved greater gains. These were the work of Braun and Dietrich that we

have already discussed in Sections 2.4.1 and 2.4.2.

These gains are slightly lower than what is theoretically expected. Saunders [36] concludes

that for the 99% reliability level case, gains of about 10 dB are achievable when two

uncorrelated, equal mean power Rayleigh faded signals are combined using SC. EGC and

MRC are slightly better by about one and two decibels, respectively.

26/83

2.5 The Interaction of the Antenna with the Body

We are all familiar with the sensitivity of the received signal to small movements of the

handset, a condition caused by the radically different phase and amplitude of the received

multipath components in a scattering environment. In addition there are the significant

changes that can occur in the received signal power between people, the effect of people on

the radio environment, whether a person wears glasses, and so on. Pederson et al. [37]

investigated this variation of the mean effective gain (MEG) for 200 people receiving

GSM1800 signals in an indoor environment. The handsets had a retractable three-quarter

wavelength whip, a retractable helical antenna, and a back-mounted patch antenna. Their

main findings are that the variation in MEG between people can be up to 10 dB; the

difference between the absence and presence of a persons head is some 10 dB for a helical

antenna, 6 dB for a whip antenna, and 3 dB for the directive patch antenna; the effect of a

persons height and whether they wear glasses is small; and there was an effect depending

whether a person is right- or left-handed.

Arai et al. [38] studied the relative antenna gain as a function of handset size and a persons

size in indoor and outdoor environments. They found that the amount the antenna protrudes

above the head, which is a function of a persons size and antenna type, resulted in a gain

variation of some 3 dB between users.

Scanlon and Evans [39] have investigated body-worn antennas that can have their efficiency

degraded by the body absorbing power and causing changes to the radiation pattern. It is

interesting to note that the body interacts with electromagnetic energy as a lossy dielectric,

decreasing the wavelength of the propagating wave. High water content tissues, like blood

and muscle, are more absorptive than fat. There will be less loss if the antenna is further from

the body, placing the antenna in a jacket instead of a shirt pocket saves about 4 dB. The

influence of the body is greater at 3G frequencies than at GSM frequencies, and it is

advisable that there is sufficient spacing between the antenna and the body to decrease body

attenuation losses. Nevertheless, radiation pattern fragmentation will occur.

And there are many other factors that add to received signal sensitivity. For example,

Flomerics [40] has found that for Bluetooth, a plastic enclosure can attenuate a RF signal by

up to 37%, and further, the tuning can be shifted out of the Bluetooth frequency range.

Designers know how to accommodate these effects, but it is another problem they face.

27/83

Indeed, as Morishita et al. point out [41], designers take into consideration the conducting

material of the handset case near the antenna as part of the antenna radiator, as well as the

loss of performance due to the proximity of the body. The antenna design must also mitigate

the specific absorption rate (SAR), particularly into the head. As counteracting measures we

may anticipate the antenna structure to be software controlled to optimise the SAR, the signal

loss due to body absorption, and the frequency de-tuning by the body. Another prudent

approach is that advocated by Leisten and Rosenberger of Sarantel [42] in whcih the antennas

are dielectrically loaded to control the resonance of their near fields and to use a feed

topology that isolates the antenna from the handset ground. This makes the performance of

the antenna more predictable, and independent of the presence of the hand and other parts of

the body. Antenova has also opted for high dielectric antennas, as mentioned previously. The

use of dielectric loading antennas is a feature we may expect to be used extensively in future

small handsets. All of the above is not, with exception of Antenova, for the complex case of

multiple adaptive antennas in a handset, but for the current conventional ones. When we start

to consider these body effects in smart antennas the problems are exacerbated.

The significance of the findings detailed above is that they emphasise that solutions to the

problem we face in this report are analytically intractable, and that statistical approaches have

to be used as there are just too many unknowns. We may anticipate that two people having

the same handset with an adaptive array in the same place may receive significantly different

signals, even if the scattering environment is unchanged due to the absence of other people

and vehicles.

2.6 Conclusions

In the course of this three week literature search we have identified some of the main players

and their research in the area of smart antennas for 3G handsets. We have discussed some of

these developments but the reader may wish to consult the references directly and the

extensive bibliography found in Appendix B is provided for further information.

We found that diversity combining appears to be achieving about 7 to 9 dB diversity gain

when two antennas are used in a handset. It has been harder to find research related to

adaptive array antenna technology for small mobile handsets. This does not necessarily mean

that no research is underway, but companies are reluctant, for commercial reasons, to disclose

their research programmes. Of the two sources that were found, there was common

28/83

agreement that overall antenna gains in the order of 20 dB were achievable using interference

rejection in an adaptive antenna array. MIMO systems were found to be a very active

research area, but the interest seems to be focused on its use at BSs; or on large terminals,

such as laptop computers with wireless interfaces, rather than on small handsets. We note that

for MIMO technology to be used, with or without handsets, the BSs would need to deploy

multiple antennas and transmit different data from each antenna. MIMO technology therefore

represents a quantum step in complexity, but is a technology for the future. The RA is

currently more interested in discovering the gains that would accrue using adaptive antennas

in the handset when the first set of UMTS BSs are deployed. These BSs are most likely to

transmit using one antenna, so it was agreed with the RA that MIMO technology would not

be included in our simulation studies.

Although the literature was not found to be well endowed with relevant data, we found

sufficient information to formulate a statistical model of a smart antenna that represents the

gain in the SINR that might be realisable using handsets with adaptive antennas instead of the

conventional single antenna. We will now report on the model we used in our studies.

3 Development of a Statistical Model for a Smart Antenna Conventional antennas on mobile handsets are omnidirectional in the horizontal plane and,

when vertical dipoles or monopoles are used, have vertical polarisation [43]. We will assume

that the gain towards the BS from these antennas is always at 0 dBi. Smart antennas,

however, try to optimise the SINR performance for a given environment. This means they

have an effective gain over traditional omnidirectional antennas. It is not possible to use a

typical antenna pattern of a smart antenna because the pattern is constantly changing

depending on the environment and presence of interferers. Neither can one easily predict the

antenna pattern at a given time because the direction of arrival of all of the incoming carrier

and interfering signals needs to be known. It is practically impossible to model this in a

simulation because even a ray-tracing model will not correctly account for all signal paths.

The alternative to using an accurate time-varying antenna pattern is to use a statistical model

of the antenna gain. We will derive a probability density function (PDF) for the effective

antenna gain (EAG), in which the EAG is the gain in decibels of the required signal that

results from combining the signals from the elements of the antenna array in a smart way.

29/83

3.1 Deriving the Log-Normal Distribution

From the literature it is reasonable to assume that the SINR performance of a single antenna

approximately follows a log-normal distribution. This is also true for the SINR performance

of the combined signal at the output of an adaptive antenna array. The EAG at a given point

in space is the difference between the SINR of the adaptive array output and the SINR from

the single antennas. By the central limit theorem the statistical distribution of the EAG will

also be log-normally distributed. From Kreyszig [44] we see that the mean of a sum of

random variables equals the sum of the means. Similarly, the mean of the difference between

two random variables will be the difference between their means. The variance of the sum of

independent random variables is given by the sum of the variances of these variables [44].

Given the normal distributions of the single antenna and the adaptive antenna, we can use the

above two properties to calculate the mean and the variance of the EAG. The standard

deviation of the distribution can then be derived from the variance.

3.1.1 The Non Line-of-Sight Case

We saw in Section 2.4.1 that Allgon Mobile Communications has produced good results

when interference rejection combining (IRC) is used in an indoor environment. Figure 1

shows the cumulative distribution function (CDF) of the SINR recorded after various

combining techniques were implemented in the case of one interferer. The SINRs achieved

from the individual antennas, A and B, are also shown, where Antenna A was a quarter wave

monopole and Antenna B was a shorted patch antenna on a printed circuit board. For

example, Antenna A gave a SINR better than about 24 dB for 99% of the time. This is

highlighted by the filled circle in Figure 1. Implementing selection combining (SC) or equal

gain combining (EGC) clearly gives an improvement in the performance of the SINR. For

example, when SC is used the SINR is better than about 17 dB for 99% of the time,

highlighted by the unfilled solid circle in Figure 1. This is a 7 dB improvement over Antenna

A. IRC clearly surpasses the diversity combining techniques, with a SINR of about 0 dB as

shown by the unfilled dashed circle in Figure 1. This is 24 dB greater than that obtained using

Antenna A. In Figure 2 we show the equivalent results when two interferers are used. Notice

that the IRC technique does not perform so well. The 99% reliability level SINR is now only

10 dB.

30/83

Figure 1 CDF of output SINR in the presence of one interferer that is 15 dB below the signal power. From Figure 4 (left) of Braun et al. [27].

31/83

The distributions given in Figure 1 and Figure 2 were reproduced in Microsoft Excel in order

to derive their means and standard deviations. These values are given in Table 1 for Antenna

A, Antenna B, and the IRC SINR output. These means and standard deviations were then

used to plot CDFs of the log-normal distributions. One can see from Figure 3 that log-normal

distributions, ie, normal distributions when the variable is expressed in decibels, are good

approximations of the measurement data and our assumption made above is valid.

Figure 2 CDF of output SINR in the presence of two interferers. The first interferer is 15 dB below the signal power. The second interferer is 20 dB below the signal power. From Figure 4 (right) of Braun et al. [27].

32/83

One Interferer (SIR = 15 dB)

Two Interferers (SIR = 15, 20 dB)

Mean Standard Deviation Mean

Standard Deviation

Antenna A and B SINR (dB) -3 10.4 -5 9.1

IRC SINR (dB) 18 7.8 8 8.2

EAG (dB) 21 13 13 12.3

Table 1 The mean and standard deviation of the output SINRs and the EAG in the presence of one or two interferers. Based on Braun et al. [27].

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-30 -20 -10 0 10 20

SINRout (dB)

CD

F

Antenna A Antenna BIRC Log-Normal CDF of Antenna A and BLog-Normal CDF of IRC

Figure 3 The CDF of output SINR in the presence of two interferers has been reproduced based on Figure 4 (right) from Braun et al. [27]. Log-normal distributions have been superimposed using the output SINR mean and standard deviation given in Table 1.

33/83

From the central limit theorem the difference between the two normal distributions, the EAG,

will produce a third normal distribution. The mean of the new distribution, , is given by,

21 = , (1)

where 1 and 2 are the means of the original distributions. The new standard deviation, , is found by summing the variances of the two distributions, ie,

( ) ( )2221 += , (2)

where 1 and 2 are the original standard deviations. Table 1 shows the mean and standard

deviation of the resulting log-normal distribution of the EAG. We can see that the mean EAG

is 21 dB and the standard deviation is 13 dB when there is only one interferer, but if two

interferers are present the mean gain decreases by 8 dB to 13 dB. At 12.3 dB the standard

deviation has changed little from before.

In a later paper Braun et al. [45] compared the EAGs for different environments. They found

that for one prototype handset the diversity gain was about 1 dB less in an urban environment

than in an indoor environment. A second prototypes diversity gain was 0.3 dB worse in the

urban environment. The diversity gain performance in an urban environment is not as good as

an indoor environment because there tends to be less scattering in an urban environment. The

signals outside are more correlated and diversity combining has less of an effect. We will

assume that the worst case example is true for all cases and that urban environments have a

mean EAG that is 1 dB less than the indoor case, ie, 21 - 1 = 20 dB in the one interferer case

and 13 - 1 = 12 dB in the case of two interferers.

Dietrich [12] noted that the work of Braun et al. required a priori knowledge of the desired

signal, which was used as a reference signal. The uncorrupted desired signal was available in

the reported experiments but is not available in practice. Dietrich went on to produce the

results of an adaptive beamforming technology for a hand-held antenna array consisting of

four antenna elements. Measurements were made in an indoor NLOS environment using a

phantom head and hand at 2.05 GHz. Figure 4 shows the CDF of the SINR performance of

the individual antennas (Ch1-4) and the improved performance due to beamforming in the

presence of a single interferer. Note that log-normally distributed CDFs match the data well.

34/83

Using Equations 1 and 2 the mean and standard deviation of the average antenna output

SINR and the IRC output SINR were used to calculate the mean and standard deviation of the

EAG for the Dietrich adaptive antenna. It was found that the EAG has a mean of 25 dB and a

standard deviation of 9 dB. The mean compares well with the 21 dB mean EAG found by

Braun et al. The standard deviation is smaller than Brauns 13 dB.

Dietrich extended the measurement programme to a microcellular environment in which it

was found that the mean SINR improvement, ie, EAG, for a hand-held in different outdoor

environments was 11.9 dB. This is low compared to Dietrichs indoor result of 25 dB. Even if

we were to use Brauns 1 dB offset for urban environments we would expect Dietrichs

outdoor mean to be closer to 25 1 = 24 dB. Dietrich suggests that his outdoor mean values

are poor because the signal-to-noise ratio (SNR) is dominating over the SINR when the

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-30 -20 -10 0 10 20 30 40

SINRout (dB)

CD

F

Ch1 Ch2Ch3 Ch4IRC Log-Normal CDF of Chs1-4Log-Normal CDF of IRC

Figure 4 The CDF of output SINR in the presence of one interferer has been reproduced based on Figure 9-2 from Dietrich [12]. Log-normal distributions have been superimposed using the output SINR mean and standard deviations.

35/83

transmitter powers are only 27 dBm, and thus the smart antenna does not perform so well. In

a real UTRA FDD network we may expect transmit powers of the order of 43 dBm [46] and

the SNR may not be so bad. For this reason we will assume that the actual EAG performance

in an urban environment, according to Dietrich, is 24 dB.

We will now assume that the overall urban NLOS log-normal distribution is characterised

with a mean and variance that is the statistical average of the distributions obtained from

Braun and Dietrich. It can be shown that the mean value, A,B, in a statistical distribution is given by,

BA

BBAABA nn

nn+

+=

, , (3)

where nA and nB are the number of samples from two distributions A and B, and A, and B, are the means of the individual distributions, respectively. If we assume that the number of

samples in each distribution is the same, then the overall mean, , is,

222

2420=

+= dB. (4)

Similarly, it can be shown that the variance of the combination of two distributions is given

by,

( ) ( ) 2

,

22

,varvarvar BA

BA

BBBAAABA nn

nn +

+++= , (5)

where varA,B is the overall variance and varA and varB are the variances of the individual

distributions, A and B, respectively. Using Equation 5, and assuming that nA = nB, the

standard deviation,, of the combined distribution is,

4.11222

2492013 22222=

+++= dB. (6)

In conclusion we can state that the log-normal distribution of the EAG in the NLOS case has

a mean of 22 dB and a standard deviation of 11.4 dB in the presence of one significant

interferer.

36/83

3.1.2 The Line-of-Sight Case

It is expected that smart antennas will behave differently in a LOS environment than in a

NLOS environment as there will be less multipath in the former case and the dominant signal

will exhibit smaller angular spread.

Dietrich [12] performed measurements in an urban LOS environment. Measurements were

taken by the receiver as it was moved over two different paths. For each of the two paths two

array configurations were used. In the co-polarised array configuration the individual antenna

elements in the array all had the same polarisation, whereas multi-polarised array

configurations had different polarisations for the individual antenna elements. The

measurements were taken with the transmitter in two different locations. Table 2 shows the

results for the eight sets of measurements. The mean SINR gain is the difference between the

mean SINR when the signals from the antenna elements are combined and the mean SINR of

the signals received at the individual antenna elements. The one percent SINR gain is the

difference between the one percent cumulative probability SINR when the signals from the

antenna elements are combined and the one percent cumulative probability SINR of the

signals received at the individual antenna elements. Using these results we have estimated the

standard deviation of the log-normal distribution, which is also shown in Table 2. Observe

that the mean and standard deviations varied considerably from one measurement path to

another.

Transmitter A Transmitter B

Path Array Configuration

Mean SINR

gain (dB)

1% SINR gain (dB)

Estimated Standard Deviation

(dB)

Mean SINR

gain (dB)

1% SINR gain (dB)

Estimated Standard Deviation

(dB)

1 co-polarised 20.2 25.7 2.4 4.8 32.1 11.7

1 multi-polarised 17.9 27.0 3.9 4.9 23.1 7.8

2 co-polarised 4.5 25.7 9.1 21.1 32.1 4.7

2 multi-polarised 5.1 27.7 9.7 21.1 28.8 3.3

Table 2 Results of peer-to-peer hand-held measurements, from Dietrich [12].

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Using Equation 3 (extended to eight data sample sets) the overall mean is given by,

5.128

1.211.219.48.41.55.49.172.20=

+++++++= dB. (7)

Notice that the mean EAG is significantly lower in the LOS case than in the NLOS case.

Similarly, using an extended version of Equation 5, the overall standard deviation is 10.6 dB.

This is similar to the NLOS value.

3.1.3 Number of Interferers

In Table 1 we saw that the EAG was different depending on the number of significant

interferers that were present. The standard deviation dropped by only about 1 dB but the

mean EAG in the presence of two interferers was 8 dB less than when there was only one

interferer. Unfortunately, we found no reports in our literature search on the effect of more

than two interferers on the performance of the smart antennas. As there is little literature on

the performance of handset smart antennas in the presence of multiple interferers, we need to

make some assumptions about the effect of multiple interferers based on what we actually

know. In an urban environment the cell density is usually sufficiently high that there will

always be at least one significant interfering BS at each mobile. Other interferers will only be

significant if they are of comparable strength to the first interferer. If the other interferers

have a low power, relative to the strongest interferer, then the smart antenna will focus on

nulling the strongest interferer and ignore the others. However, if the other interferers are of a

comparable strength to the strongest interferer, the performance of the smart antenna will

deteriorate. The antenna now needs to suppress two interference sources. We will define a

significant interferer as any interferer that is within 8 dB of the strongest interferer. Allgon

has shown that a separation of 5 dB between two interference levels significantly reduces the

performance of the smart antenna. We have added a 3 dB margin to this value because we

expect interferers that are even weaker than 5 dB from the strongest interferer to still

harmfully effect the performance of the adaptive antenna.

The total number of nulls or beams that can be steered in an M element array is M1 [11]. In

a real network the number of significant interferers will vary depending on the precise

location of the handset. Figure 5 gives an indication of the number of significant interferers

within 8 dB of the strongest interferer in a typical dense urban network.

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The plot has been created using MACcdma and displayed in MAC Ltds network coverage

planning tool, the NP WorkPlace. One can see that there are no areas with zero interferers or

just one significant interferer. There is one interferer within 8 dB of the strongest interferer,

ie, a total of two interferers, within a small portion of the map area. These areas are shown in

green. A larger area of the map, shown in yellow, has three interferers. The remaining map

area, in orange, shows where there are a total of four or more interferers. In these cases we

assume that the number of interferers is greater than the number of nulls that can be steered.

The smart antenna is swamped by too many interferers and, given an array of only four

elements, it cannot adapt well enough. We will assume that the smart antenna is also

performing maximal ratio combining (MRC) and continually comparing the SINR

performance (or bit error rate performance) provided by the MRC and IRC techniques. When

IRC performs badly, the smart antenna can resort to the MRC approach. The performance

gain of MRC remains fairly constant independently of the number of interferers because

MRC does not try to reject interferers. Rather, Rayleigh fading due to multipath is being

overcome. Dietrich performed measurements for diversity gain using MRC in LOS and

NLOS environments. He found [12] that the 99% reliability level using two antennas was

Figure 5 Number of interferers in a snapshot of a typical dense urban network.

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6.4 dB and 8.8 dB in the LOS and NLOS case, respectively. Dietrich did not provide the

mean or the standard deviation of the diversity gain. Neither did he provide plots. Since we

are not able to reproduce the precise distribution we will assume that the mean diversity gain

is equivalent to the diversity gain at the 99% reliability level. We will assume that the

standard deviation continues to decrease by 1 dB with each additional interferer.

No literature could be found on the effect of three interferers on the performance of the smart

antenna. In this case we will assume that the smart antennas performance is halfway between

that of two interferers and four or more interferers. Table 3 summarises the means and

standard deviations that will be used in the statistical model. These values will be used in the

MACcdma simulation described in Section 4. Note that if we assume an 8 dB drop in the

performance of the adaptive antenna in a LOS environment when the number of interferers

increases from one to two, we would get a mean EAG of 4.5 dB. Since this is below the

performance of MRC we will assume that MRC is used and the mean EAG is 6.4 dB.

Line-of-sight Non Line-of-sight Number of Interferers Mean (dB) Standard Deviation (dB) Mean (dB)

Standard Deviation (dB)

1 12.5 10.6 22 11.4

2 6.4 9.6 14 10.4

3 6.4 8.6 11.4 9.4

4 or more 6.4 7.6 8.8 8.4

3.2 Velocity of the Handset

The VTVT Laboratory investigated the performance of diversity combining and adaptive

combining at various velocities in a WCDMA simulation [31]. They concluded that there was

no degradation in performance with increasing velocity of the mobile when a diversity

combining technique was used with two antennas [26]. However, they also found that the

performance of an adaptive antenna improved slightly as the handset velocity decreased [30].

There was about a 1 dB improvement in the required SIR level for a BER of 5% when the

mobiles velocity decreased from 30 km/hr to 2 km/hr. This improvement increased to about

3 dB for a BER of 2%. In urban environments, where vehicles do not tend to drive at high

Table 3 Mean and standard deviation for the EAG in the presence of different numbers of interferers.

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speeds, we would expect the mobile velocity to vary between walking speed and about

70 km/hr. MACcdma does not model the velocity of individual handsets so we will assume

that they are all stationary or at walking speed, thus the EAGs given in Table 3 can be used.

Although greater speeds may experience a small degradation in the performance of the smart

antenna, it is expected that future designs will perform better and be able to perform fast

enough to respond to the rapidly changing environment.

3.3 Differences between Wideband and Narrowband Signals

The measurements performed by both Braun and Dietrich used narrowband signals, whereas

UTRA FDD uses a wideband signal of 5 MHz. Dietrich suggests that the performance of an

antenna array in wideband systems would be the same for most systems when the delay

spread is less than the chip or symbol period [12]. However, if the delay spread is too large,

the receiver will not be able to cope because the delay spread causes the symbols to merge

into one another. In this case, the adaptive antenna will be unable to choose the optimum

weights for the individual antenna elements. A RAKE receiver at the UTRA FDD receive

end performs diversity combining of the delayed paths to help remove this inter-symbol

interference, in addition to despreading the received signals. However, it does not try to

suppress interferers. We will assume that the EAG is the same value at 5 MHz as it is for a

narrowband carrier wave. Little has been published in this area but future wideband

measurement programs at Virginia Tech should reveal the true story.

4 Simulation Procedures and Parameters MAC Ltd has developed a WCDMA simulation tool, known as MACcdma, that allows a

network planner to investigate the quality of a WCDMA network given a particular radio

coverage plan and traffic profile. Before MACcdma can be used we must first create a

cellular network and generate signal strength coverage predictions in the area of concern.

This was performed using MAC Ltds proprietary radio planning tool, the NP Work Place

and we begin this section with a description of this process. Next, MACcdma was used to

assess the effect of using a smart antenna in a WCDMA network as opposed to a standard

omnidirectional antenna. In order to generate useful results we devised a realistic network

model using performance metrics and system specifications that are expected to exist in a real

network. Various 3G technologies have been specified but we focused on the UTRA FDD

system since the UK operators will be deploying this technology. Finally, we go on to

highlight the details of the particular simulations that were run. Spe