(Luận án tiến sĩ) research and development of adaptive beamformers for interference suppression in smart antennas

VIETNAM NATIONAL UNIVESITY, HANOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY

TONG VAN LUYEN

RESEARCH AND DEVELOPMENT
OF ADAPTIVE BEAMFORMERS
FOR INTERFERENCE SUPPRESSION
IN SMART ANTENNAS

Dissertation for the Degree of Doctor of Philosophy
in Communication Engineering

Hanoi - 2018


VIETNAM NATIONAL UNIVESITY, HANOI
UNIVERSITY OF ENGINEERING AND TECHNOLOGY

TONG VAN LUYEN
RESEARCH AND DEVELOPMENT
OF ADAPTIVE BEAMFORMERS
FOR INTERFERENCE SUPPRESSION
IN SMART ANTENNAS
Dissertation for the Degree of Doctor of Philosophy
in Communication Engineering
Major: Communication Engineering
Code: 9510302.02

Supervised by
Assoc. Prof. Dr.-Ing. Truong Vu Bang Giang

Hanoi - 2018


Declaration
I confirm that:
-

This dissertation represents my own work;

-

The contribution of my supervisor and others to the research and to the
dissertation was consistent with normal supervisory practice;

-

External contributions to the research are acknowledged.
Date: September 26th, 2018

Tong Van Luyen

i


Acknowledgement
First of all, I would like to express my sincere thanks to my supervisor, Assoc.
Prof. Dr.-Ing. Truong Vu Bang Giang, for his supervision, his support and
assessment comments in the work, and what he has done for me at VNU University
of Engineering and Technology. He believed me in my scientific ability, challenged

my work, and encouraged me to pursue my ideas during the time we worked
together.
I would like to thank Faculty of Electronic Engineering, Hanoi University of
Industry, and Faculty of Electronics and Telecommunications, VNU University of
Engineering and Technology for their support for me to do PhD course.
My special thanks to M.S. Nguyen Minh Tran for his discussions and
comments, and his technical support in our lab to my dissertation.
I highly appreciate the help from Dr. Hoang Manh Kha, Dr. Dao Thanh Hai,
and thank them for their helpful discussions in nature-inspired optimization, and
their kind encourages to the success of this work.
I would like to thank M.S. Pham Thi Quynh Trang for her kind support at both
the simulation technique in my dissertation and the work in my office.
I am grateful to my dear colleagues, Nguyen Viet Tuyen, Duong Thi Hang, Bo
Quoc Bao, Vu Thi Phuong Quynh, and the other colleagues of HaUI Faculty of
Electronic Engineering, for their practical support during my work.
Finally, my beloved thanks and my deepest gratitude to my parents of both
sides, my wife Duyen, my daughter My Quyen, and my son Minh Duc for their love
and encouragement. Thanks to your sharing and sacrifice and to you I dedicate this
dissertation.

ii


Contents
Declaration ................................................................................................................. i
Acknowledgement .................................................................................................... ii
Contents.................................................................................................................... iii
List of Abbreviations.................................................................................................1
List of the Symbols and Notations ...........................................................................2
List of Figures ............................................................................................................3

List of Tables .............................................................................................................6
Introduction ...............................................................................................................7
I. Rationale for the Study .................................................................................................. 7
II. Objectives, Subjects, Scope, and Methodology of the Study ..................................... 10
II.1. Objectives ........................................................................................................... 10
II.2. Subjects, Scope, and Methodology ..................................................................... 11
III. Significance of the Study .......................................................................................... 11
IV. Dissertation Outline .................................................................................................. 13

Chapter 1: Overview of Beamforming ..................................................................14
1.1. Beamforming for Smart Antennas ........................................................................... 14
1.2. Mathematic Basis of Smart Antennas ...................................................................... 18
1.2.1. Geometric Relations ......................................................................................... 18
1.2.2. The Model of Smart Antennas with Linear Arrays .......................................... 20
1.3. Optimal Beamforming Techniques .......................................................................... 23
1.3.1. Classical Optimization Techniques .................................................................. 24
1.3.2. Nature-inspired Optimization Techniques ....................................................... 25
1.4. Chapter Conclusions ................................................................................................ 30

Chapter 2: General Process to Develop BA-based Adaptive Beamformers for
Interference Suppression ........................................................................................31
2.1. Problem Determination ............................................................................................ 31
2.2. Array Factor Building .............................................................................................. 32
2.3. Pattern Nulling Techniques ...................................................................................... 33
2.3.1. Amplitude-only Control ................................................................................... 33
2.3.2. Phase-only Control ........................................................................................... 34
2.3.3. Complex-weight Control .................................................................................. 34
2.4. Formation of Objective Function ............................................................................. 35
2.5. Building of BA-based Adaptive Beamforming Algorithms .................................... 37
2.6. Development of Adaptive Beamformers ................................................................. 38

2.7. Proposals of General Process to Build Adaptive Beamformers .............................. 40
2.8. Chapter Conclusions ................................................................................................ 41

iii


Chapter 3: Developments of BA-based Adaptive Beamformers for Interference
Suppression ..............................................................................................................42
3.1. Common Items of BA-based Adaptive Beamformers ............................................. 42
3.2. The Beamformer Based on Phase-only Control ...................................................... 45
3.2.1. Diagram of the Beamformer............................................................................. 45
3.2.2. Penalty Parameter in the Objective Function ................................................ 46
3.2.3. Numerical Results and Discussions ................................................................. 46
3.2.4. Summary .......................................................................................................... 50
3.3. The Beamformer Based on Amplitude-only Control ............................................... 51
3.3.1. Diagram of the Beamformer............................................................................. 51
3.3.2. Numerical Results and Discussions ................................................................. 51
3.3.3. Summary .......................................................................................................... 56
3.4. The Beamformer Based on Complex-weight Control ............................................. 57
3.4.1. Diagram of the Beamformer............................................................................. 57
3.4.2. Numerical Results and Discussions ................................................................. 58
3.4.3. Summary .......................................................................................................... 64
3.5. Effect of Mutual Coupling ....................................................................................... 64
3.6. Summary .................................................................................................................. 67
3.7. Chapter Conclusions ................................................................................................ 72

Conclusions and Future Works .............................................................................73
List of Publications ..................................................................................................76
Bibliography ............................................................................................................77
Appendix ..................................................................................................................81

A. Smart Antennas .......................................................................................................... 81
A.1. Antenna Arrays ................................................................................................... 81
A.2. Classification of Beamforming........................................................................... 86
A.3. Application Model of Smart Antennas ............................................................... 89
B. Classical Optimization Techniques ............................................................................ 91
B.1. Optimal Criteria .................................................................................................. 91
B.2. Adaptive Beamforming Algorithms ................................................................... 92
B.3. Dolph-Chebyshev Weighting Method ................................................................ 95
C. Software for Modeling Adaptive Beamforming in Smart Antennas .......................... 99
C.1. Application Model ............................................................................................ 100
C.2. Simulation Results ............................................................................................ 100
D. Supported Simulation Results .................................................................................. 105
D.1. Additional Results for Patterns with Single and Multiple Nulls. ..................... 105
D.2. Some Sets of Weights for the Investigated Scenarios ...................................... 110

iv


List of Abbreviations
ABF
ADC
AF
AMP_BA_ABF
APSO
BA
CW_BA_ABF
DBF
DOA
DSP
FNBW

GA
HPBW
LMS
MC
MMSE
MSE
NDL
PHA_BA_ABF
PSO
RF
RLS
SDMA
SLL
SMI
SNOI
SOI
ULA

Adaptive Beamformer
Analog-to-Digital Converter
Array Factor
Amplitude-only Control and Bat Algorithm-based Adaptive
Beamformer
Accelerated Particle Swarm Optimization
Bat Algorithm
Complex-weight Control and Bat Algorithm-based Adaptive
Beamformer
Digital Beamforming
Direction-Of-Arrival
Digital Signal Processor

First-Null Beamwidth
Genetic Algorithm
Half-Power Beamwidth
Least Mean Square
Mutual Coupling
Minimum Mean Square Error
Mean Square Error
Null Depth Level
Phase-only Control and Bat Algorithm-based Adaptive
Beamformer
Particle Swarm Optimization
Radio Frequency
Recursive Least Square
Space Division Multiple Access
Sidelobe Level
Sample Matrix Inversion
Signal-Not-Of-Interest
Signal-Of-Interest
Uniform Linear Array

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List of the Symbols and Notations
I
Q

In-phase channel in of binary baseband signals
Quadrature-phase channel in of binary baseband signals
Sum

The real vector space (n-dimensional space of the variables)
Subset of or equal to
An element of
Elevation angle in the coordinate system for antenna analysis
Azimuth angle in the coordinate system for antenna analysis
Wavelength
Unit vector on the axis
⃗⃗⃗
Differential value of
Wavenumber
Vector and its components
Z, Zij
Maxtrix and its components
*
x
Complex conjugate of x
Transposition of a matrix
Hermitian transpose of a matix
Cross correlation of
and
Covariance of
̃
Estimation of X
Real part of
Imaginary part of
Cosine integral
Sine integral
Infinity
3.1415926535897932385
Bat algorithm:

Position of bat (i) corresponding to a solution of the weights for
array elements
Velocity of bat (i)
Frequency of bat (i)
Loudness of bat (i)
Rate of emission pulse of bat (i)

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List of Figures
Figure 1.1. Beamforming for smart antnenas. ..........................................................15
Figure 1.2. Applications of beamforming. ................................................................15
Figure 1.3. Block diagram of analog beamforming in smart antennas. ....................16
Figure 1.4. Block diagram of DBF in smart antennas. .............................................16
Figure 1.5. Simple block diagram of adaptive beamformer at the receiving end. ....18
Figure 1.6. The analyzed linear array. ......................................................................19
Figure 1.7. Linear-array smart antennas at the receiving end. ..................................20
Figure 1.8. Radiation pattern of 20-element ULA. ...................................................22
Figure 1.9. Flowchart of Bat algorithm. ....................................................................29
Figure 2.1. Geometry of ULAs of 2N elements. .......................................................32
Figure 2.2. Block diagram of adaptive beamformers for interference suppression. .38
Figure 2.3. Flowchart of the proposed beamformers. ...............................................39
Figure 2.4. General process to build adaptive beamformers.....................................41
Figure 3.1. Diagram of PHA_BA_ABF....................................................................45
Figure 3.2. NDL and maximum SLL with different in the case of pattern with
single null. .................................................................................................................46
Figure 3.3. Objective function comparisons of BA, PSO, and GA. .........................47
Figure 3.4. Optimized pattern with a single null at 14°. ...........................................48
Figure 3.5. Optimized pattern with three nulls at -48°, 20°, and 40°. ......................49

Figure 3.6. Optimized pattern with a broad null from 30° to 40°. ............................49
Figure 3.7. Diagram of AMP_BA_ABF. ..................................................................51
Figure 3.8. Objective function comparisons of BA, PSO, and GA. .........................52
Figure 3.9. Optimized pattern with single symmetric null at 14°. ............................53
Figure 3.10. Optimized patterns with three symmetric multiple nulls at 14°, 26°, and
33°. ............................................................................................................................54
Figure 3.11. Optimized patterns with a symmetric broad null from 20° to 50°,
unchanged main lobe beamwidth and peak SLL = -18.3 dB. ...................................55
Figure 3.12. Optimized pattern with a symmetric broad null from 20° to 50°,
broaden main lobe beamwidth and SLL ≤ -30 dB ....................................................56
Figure 3.13. Diagram of CW_BA_ABF. ..................................................................57
Figure 3.14. Objective function of BA with different population sizes....................59
Figure 3.15. Objective function between BA and APSO. ........................................59
Figure 3.16. Optimized patterns with single null at 14°. ..........................................60
Figure 3.17. Optimized pattern with three nulls at -33°, -26°, and -14°. ..................61
Figure 3.18. Optimized pattern with three nulls at -40°, 20°, and 40°. ....................62
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Figure 3.19. Optimized pattern with a broad null from -50° to -20°. .......................62
Figure 3.20. Optimized pattern with a broad null ([-30°, -20°] and [45°, 60°]). ......63
Figure 3.21. Optimized pattern with a broad null ([-30°, -20°] and [45°, 60°]) and
SLL of -30 dB. ..........................................................................................................64
Figure 3.22. Optimized pattern (nulls: -48°, 20°, 40°) with mutual coupling. .........65
Figure A.1. Radiation pattern of a twenty-element ULA. ........................................82
Figure A.2. Coordinate system for antenna analysis. ...............................................82
Figure A.3. Different array geometries for smart antennas: (a) uniform linear array,
(b) circular array, (c) two-dimensional grid array and (d) three-dimensional grid
array. ..........................................................................................................................86
Figure A.4. Switched-beam system. .........................................................................87

Figure A.5. Comparison of (a) switched-beam system, and (b) adaptive array
system. .......................................................................................................................88
Figure A.6. Relative coverage area comparison among sectorized systems,
switched-beam systems, and adaptive array systems in (a) low interference
environment, and (b) high interference environment................................................88
Figure A.7. Functional block diagram of a smart antenna using DOA-based adaptive
beamforming algorithms. ..........................................................................................89
Figure A.8. Radiation pattern of a smart antenna. ....................................................90
Figure A.9. Functional block diagram of a smart antenna using training-based
adaptive beamforming algorithms. ...........................................................................90
Figure B.1. Geometry of ULA antennas of 2N elements..........................................99
Figure B.2. Normalized array factor for 20-element Chebyshev arrays with
sidelobes at -30 dB. ...................................................................................................99
Figure C.1. The main lobes of the 8-element ULA have been steered to the desired
directions as θ = 49°, -30°, 30°, 60°. ......................................................................101
Figure C.2. Five nulls have been set at elevation angles of -55°, -35°, -15°, 20°, and
45°. ..........................................................................................................................101
Figure C.3. The main beam is steered to θ = 30° and 5 nulls are set at θ = -55°, -35°,
-15°, 0°,45° at the same time...................................................................................102
Figure C.4. The optimized pattern with all side lobe levels are suppress to -30dB by
Dolph-Chebyshev weighting method. .....................................................................103
Figure C.5. The optimized pattern by applying both LMS algorithm and DolphChebyshev weighting method. ................................................................................103
Figure C.6. The optimized pattern of 1×8 ULA using LMS algorithm. .................104
Figure C.7. The optimized pattern of 1×8 ULA using both LMS algorithm and
Dolph-Chebyshev weighting method. .....................................................................104
Figure D.1. Pattern with a single symmetric null in the range of θ:
a) (-90°, 90°); b) (13°, 16°). ....................................................................................105
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Figure D.2. Pattern with three symmetric nulls in the range of θ:
a) (-90°, 90°); b) (12°, 35°). ....................................................................................106
Figure D.3. Pattern with a single null in the range of θ:
a) (-90°, 90°); b) (13°, 16°). ....................................................................................106
Figure D.4. Pattern with three nulls in the range of θ:
a) (-90°, 90°); b) (-50°, -46°); c) (18°, 22°); d) (38°, 42°). .....................................107
Figure D.5. Pattern with a single symmetric null in the range of θ:
a) (-90°, 90°); b) (13°, 16°). ....................................................................................108
Figure D.6. Pattern with three nulls in the range of θ:
a) (-90°, 90°); b) (-34°, -13°). .................................................................................109
Figure D.7. Pattern with three nulls in the range of θ:
a) (-90°, 90°); b) (-42°, 42°). ...................................................................................109

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List of Tables
Table 3.1. Common parameters for all proposed beamformers. ...............................43
Table 3.2. NDL and maximum SLL of the patterns in all scenarios with or without
mutual coupling. ........................................................................................................66
Table 3.3. Summary of the proposals. ......................................................................67
Table 3.4. Comparisons between the proposals in this dissertation and the proposal
in. ...............................................................................................................................71
Table B.1. Resulting weights computed by Dolph-Chebyshev weighting method ..98
Table D.1. Some sets of weights consisting amplitudes (an) and phases (δn) of the
patterns shown in Figures 3.4-3.6. ..........................................................................110
Table D.2. Some sets of weights for the patterns shown in Figures 3.9-3.12.........110
Table D.3. Some sets of weights for the patterns shown in Figures 3.16-3.21.......111

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Introduction

I. Rationale for the Study
Beamforming is a signal processing technique in sensor arrays to directionally
transmit or receive signals in space-time. In order to do that, the signals
corresponding to array elements are combined in the interest of boosting the desired
signals in particular directions and minimizing the undesired signals (interferences)
in the others. Beamforming can be applied for both transmitting and receiving ends
in order to achieve spatial selectivity, thus, it is also called spatial filtering
technique. In fact, it can be used for radio or sound waves and has been widely
applied for various applications such as Radar, Sonar, Wireless communications,
Radio Astronomy, Seismology, and Topography [6, 18, 26, 56].
Over the last decades, wireless technology has been developed at a remarkable
rate, which has brought new and high-quality services at lower costs. This has
resulted in an increase in airtime usage, and in the number of subscribers. As a
result, this leads to new challenges for next generations of wireless communications
networks. The most practical solution to this problem is to use spatial processing
[11]. As Andrew Viterbi, one of Qualcomm’s founders, stated: “Spatial processing
remains as the most promising, if not the last frontier, in the evolution of multiple
access systems” [42]. Spatial processing lies at the heart of adaptive antennas or
smart antenna systems that employ beamforming. As a result, space division
multiple access (SDMA), one of the most complicated applications of smart antenna
technology, is indispensable to the development of cellular radio systems [11].
The advances of beamforming in cellular phone standards and other wireless
communication ones over the generations have resulted in the achievement of high
density cells and higher throughput [1, 4, 14, 16, 23, 38, 45, 52, 63]. In fact,

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beamforming has been used in all the second, the third, and the fourth generation
cellular standards. Additionally, beamforming is being deployed in indoor networks
such as Wi-Fi. Even though it is still unsure which frequency band will be utilized
for 5G technology, beamforming is going to play a major role in the future [16].
As mentioned above, beamforming for smart antennas plays a vital role in
wireless communication systems, especially for new generation ones. Actually,
smart antennas exhibit various benefits in coverage, data rate, spectrum efficiency,
interference suppression, which are all the vital factors of wireless communication
systems [21, 48-50]. Therefore, it has received enormous interest worldwide [11].
Nowadays, the increasing number of wireless devices causes serious pollution
in the electromagnetic propagation environment. In this context, smart antennas
with pattern nulling capabilities emerge as a promising solution for interference
suppression applications. Beamformers offer smart antennas the capability of
interference suppression by: (i) steering the main lobe to the desired signal; (ii)
suppressing sidelobes at directions of interferences; (iii) or placing nulls at
directions of interferences [10, 11, 20, 26, 43, 44]. In the cases of (i) and (ii), when
the desired signal boosted at the main lobe is still weaker than the interferences
received at sidelobes, the desired signal is overwhelmed by the interference. In
order to solve this problem, pattern nulling is regarded as one of the best solutions
for interference suppression, because it allows smart antennas to adaptively place
nulls at directions of interferences while maintaining the main lobe at the direction
of desired signal and suppressing sidelobes. However, this has resulted in an
increase in the complexity of computation and the requirement of the effective
optimization tools [11, 19, 20, 52].
In order to implement the pattern nulling by using adaptive beamformers, two
main aspects including pattern nulling control and optimization techniques have
been addressed.
Firstly, several pattern nulling control techniques such as controlling the

amplitude-only, the phase-only, position-only, and the complex-weight (both the
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amplitude and the phase) have been widely studied and implemented. All these
techniques have their own advantages and limitations [18, 20, 47]. Among those,
the complex-weight control has been considered as the most flexible and efficient
technique because it allows adjusting amplitude and phase simultaneously [13, 20,
27, 64]. Nonetheless, it is the most complicated and expensive technique due to the
fact that each array element must have a controller, a phase shifter and an
attenuator. More critically, the computational time will be a considerable issue in
large array antennas. Indeed, the problem for the phase-only and position-only
controls is inherently nonlinear [30]. The position-only control [3, 12, 29] requires a
mechanical driving system such as servomotors for adjusting the array element
position. This makes the system more complicated, and causes difficulty in
accuracy control. Phase-only control is less complex and more attractive for the
phased arrays since the required control is available at no extra cost [2, 33, 34, 46].
The amplitude-only control is simple compared to the others as it only changes the
amplitude excited at each array element [5, 30, 37, 54].
Secondly, in recent years, optimization techniques have been widely applied in
beamforming for antenna array pattern synthesis including pattern nulling. The
classical optimization techniques used for the array pattern synthesis are likely to be
stuck in local minima if the initial guesses are not reasonably close to the final
solution. Most of the classical optimization techniques and analytical approaches
also suffer from the lack of flexible solutions for a given antenna pattern synthesis
problem. To overcome these issues, various nature-inspired optimization algorithms
based on computational intelligence approaches have been developed. These
algorithms such as ant colony optimization [13], bacterial foraging algorithm [30],
differential evolution [54], clonal selection [5], bees algorithm [28], especially the
genetic algorithm (GA) [15, 25, 35, 47, 64] and particle swarm optimization (PSO)

[15, 31, 39] have been proved to be better and more flexible than the classical ones.
These nature-inspired optimization algorithms have been proposed and implemented with their own benefits and limitations in pattern nulling. In general, there
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are still some challenges for the pattern nulling based on these nature-inspired
algorithms as: (i) computation speed and performance; (ii) the lack of detailed
analysis about the general process to obtain pattern nulling, which leads to the
difficulty in understanding, applying and developing applications. These issues are
the motivation for further research in this field.
Recently, Bat algorithm (BA) is a new nature-inspired computation technique
based on the bat behavior of using echolocation to detect prey, avoid obstacles, and
locate their roosting crevices in the dark. It has been successfully used to solve
various kinds of engineering problems. BA is better than PSO and GA optimization
in terms of convergence, robustness and precision [59, 61]. This algorithm was
applied for the first time for beamforming in 2016 [40]. Authors of [40] showed
that the BA is a promising optimization tool for adaptive beamforming in terms of
computation time. Nevertheless, this work was still in preliminary phase and thus, it
lacked adequate analysis on the application of BA in beamforming.
Therefore, the development of adaptive beamformers for interference
suppression is obviously still a challenge for researchers regarding the improvement
in computational speed and capability of pattern nulling. To tackle these challenges,
this dissertation will concentrate on proposing a general process to build BA-based
adaptive beamformers to suppress interference for ULAs in smart antennas. This
general process is then implemented to develop three types of BA-based adaptive
beamformers to suppress interference for ULAs using: (i) amplitude-only, (ii)
phase-only, and (iii) complex-weight control techniques.

II. Objectives, Subjects, Scope, and Methodology of the Study
II.1. Objectives

-

To research and propose a general process to build BA-based adaptive
beamformers to suppress interference for ULAs in smart antennas.

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-

To implement the general process to develop three types of BA-based
adaptive beamformers to suppress interference for ULAs using: (i)
amplitude-only, (ii) phase-only, and (iii) complex-weight control
techniques.

II.2. Subjects, Scope, and Methodology
This study focuses on:
-

Pattern analysis of antenna arrays;

-

Adaptive beamforming techniques for antenna arrays;

-

Global

optimization

algorithms

(nature-inspired

optimization

algorithms such as genetic algorithm (GA), accelerated particle swarm
optimization (APSO), and Bat algorithm (BA));
-

Interference suppression using beamformers.

The methodology of the study includes:
-

Synthesis and analysis of: antenna array pattern using adaptive
beamforming in smart antennas; and nature-inspired optimization;

-

Modeling of proposed beamformers in terms of interference
suppression using smart antennas;

-

Simulation and evaluation of the proposals in particular scenarios.

III. Significance of the Study
The significance of the study in science and in practice is as follows:

Scientific significance:
-

Proposal of a general process to build BA-based adaptive beamformers
for interference suppression applications in smart antennas;

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-

Proposals of three high performance BA-based adaptive beamformers
for suppressing interference, which use amplitude-only, phase-only,
and complex-weight control techniques, respectively.

Practical significance:
-

The proposals have been implemented to develop three different
beamformers for 20-element ULA with isotropic or dipole element.
Additionally, the mutual coupling has also been investigated in the case
of dipole element and phase-only control. According to the numerical
results, the proposed beamformers have shown the ability to suppress
sidelobes, to maintain predefined beamwidth, and to place precisely
single, multiple, and broad nulls at an arbitrary direction of
interferences. Furthermore, those beamformers are much faster and
more effective in terms of null steering and side lobe suppression in
pattern synthesis than GA and APSO-based ones.

-

These proposals can be applied to design and implement adaptive
beamformers for interference suppression applications in radar and
wireless communication networks.

The scientific contributions of dissertation are:
(1)

Proposal of a general process to build BA-based adaptive beamformers
to suppress interferences for ULAs in smart antennas.

(2)

Successful implementation of the general process to develop three types
of BA-based adaptive beamformers to suppress interferences for ULAs
using

amplitude-only,

phase-only,

techniques, respectively.

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and

complex-weight

control

IV. Dissertation Outline
The dissertation consists of an introduction, three chapters, and a conclusion,
in which:
-

Chapter 1 presents a general review on beamforming: an overview of
beamforming; beamforming techniques including mathematical basis,
optimization techniques. These are related to the contents of this
dissertation.

-

Chapter 2 presents the first proposal, a general process to build BA-based
adaptive beamformers for pattern nulling of ULAs. This process includes
six steps from problem determination to developments of adaptive
beamformers.

-

Chapter 3 presents the second proposal by applying the process given in
Chapter 2. This proposal includes three different BA-based adaptive
beamformers for pattern nulling of ULAs, of which pattern nulling controls
are amplitude-only, phase-only, and complex-weight (both the amplitude
and the phase), respectively. These beamformers have been successfully
implemented and verified in terms of pattern nulling synthesis.

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Chapter 1
Overview of Beamforming
This chapter presents an overview of beamforming, its applications for smart
antennas in wireless communication systems, technical basis of beamforming
including application models, mathematical basis, optimization techniques that are
related to the contents of this dissertation.

1.1. Beamforming for Smart Antennas
In smart antennas, beamforming is used along with antenna array to form an
equivalent directional antenna system [6, 18, 26, 56]. This directional antenna
system (smart antenna systems or shortly written, smart antennas) is able to focus
on the radiation power or spatially receive power in a particular direction in space.
This spatial radiation or power reception of smart antennas, also called “beam”, is
achieved by electrical control using beamforming, in which the desired signals in
particular directions are boosted and the interferences in the others are minimized.
Therefore, beamforming has been widely used in many applications such as radar,
sonar, and wireless communication systems. In wireless communication system, it
is deployed to enhance the performance by increasing the efficiency of radio
spectrum utilization, interference suppression, and power saving [11, 14, 16-18, 23,
24, 26].
In beamforming, the signal corresponding to each element has been
controlled by a specific principle. This control aims to form and steer the beam of
the array in such a way as: (i) form and steer the main beam to a desired direction;
(ii) suppress the sidelobes; (iii) and set nulls at undesired directions. The beam of
the array has been formed and controlled according to the requirements of the
specific applications [11, 18, 26].
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Figure 1.1. Beamforming for smart antnenas [8].

Figure 1.2. Applications of beamforming [18].

In general, common controlling parameters are the amplitude, the phase, or
both the amplitude and the phase of excitations corresponding to the elements.
These controlled parameters are also called “weights”. Beamformers at the
receiving end apply this set of weights for the signals from elements to gain the
controlled signals, then, combine all these signals to a desired output.
In analog beamforming, the weight (

) of each array element is controlled in

the analog domain (Radio Frequency). Phase shifters and attenuators are used to
adjust the phase ( ) and the amplitude ( ) of each antenna path, respectively.
Based on specific rules, these controls, or beamforming techniques are applied to
form and steer the beam of the antenna arrays to meet particular requirements. A

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simple block diagram of analog beamforming in smart antennas is given in the
Figure 1.3 [26, 58].
Antenna array
𝑤0

𝑎0 𝑒 𝑗𝛿0

𝛿0

..
.

Power Combiner/
Power Splitter

0
𝑎0

..
.
𝑤𝑁−1

𝑎𝑁−1 𝑒 𝑗𝛿𝑁−1

Transceiver

N-1
𝛿𝑁−1

𝑎𝑁−1

Figure 1.3. Block diagram of analog beamforming in smart antennas [58].

Digital beamforming (DBF) controls the weight of each array element in the
digital domain. DBF is a marriage between antenna and digital technologies. It has
been used to construct the smart antenna systems as presented in Figure 1.3
including three major components: the antenna array, the digital transceivers, and
the digital signal processor (DSP) [11, 26].
Antenna array

I

..
.

Q

In

Digital
Signal
processor

Transceiver

0

..
.
I

N-1

Transceiver

Out

Q

Figure 1.4. Block diagram of DBF in smart antennas [26].

As shown in Figure 1.4, the received signals (Radio frequency signals - RF
signals) are detected and digitized at the element level. Keeping RF information in

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the form of a digital stream gives access to a large domain of signal processing
techniques, as well as algorithms that can be used to extract information from the
spatial domain data. Particularly, digital beamformers digitize and convert the
receiving signals into two streams of binary baseband signals (i.e., in-phase (I) and
quadrature-phase (Q) channels). Included within these baseband signals are the
amplitudes and phases of signals received at each elements of the array. DBF is
carried out by weighting these digital signals, thereby adjusting their amplitudes and
phases such a way that when added together they form the desired beam. This
process can be carried out using a special-purpose DSP [26].
Adaptive beamforming is capable of automatically adapting its response to
different situations. It has been applied for adaptive array systems to provide more
degrees of freedom since they have the ability to adapt in real time the radiation
pattern to the RF signal environment. In other words, they can direct the main beam
toward the pilot signal or Signal-Of-Interest (SOI) while suppressing the antenna
pattern in the direction of the interferers or Signals-Not-Of-Interest (SNOIs). To put
it simply, adaptive array systems can customize an appropriate radiation pattern for
each individual user. This is far superior to the performance of a switched-beam
system (see Appendix A for more details) [11].
A simple structure of adaptive beamformers (ABF) in the receiving end is
displayed in Figure 1.5. ABF carries out weighting the receiving signals, thereby
adjusting their amplitudes or phases in such a way that when added together they
form desired output. They are able to adaptively adjust the value of weights (

)

to point the beam in any wanted direction and to manipulate its shape to optimize
the system performance. Because of their flexibility, adaptive beamformers have
been utilized in various applications [26].
Additionally, some basic concepts and characteristics have been introduced in
Appendix A for more information about smart antennas. In order to support the
study, mathematical basis and optimization technique of adaptive beamforming in
smart antenna will be introduced in the next sections.
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Antenna array
Desired direction
(signal)

x0

0

w0

x1

1

w1

..
.

y

..
.
xN-1

wN-1

..
.

Undesired
directions
(interferences)

N-1

∑

..
.

Adaptive
weight update

Figure 1.5. Simple block diagram of adaptive beamformer at the receiving end [26].

1.2. Mathematic Basis of Smart Antennas
In smart antenna, although there are different array geometries, the principle

of signal processing techniques shares some common points. Therefore, for
simplicity, only linear arrays will be analyzed in this section.

1.2.1. Geometric Relations
Figure 1.6 shows a linear array where N elements are positioned along the α
axis with uniform inter-element spacing, d, and the first element (element 0) is at
the origin of the coordinate system. The direction of incoming waves has been
defined by elevation angel θ and azimuth angle φ in spherical coordinates [11, 17,
26]. To make it simple, we assume that:
-

The inter-element spacing is small enough to have no significant difference
of amplitude of incoming waves and therefore amplitudes of receiving
signals at different elements are considered as the same.

-

There is no mutual coupling effect.

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-

Incoming waves at each element with a particular plane wave is considered
as a radio signal. As a result, there are a limited number of radio signals
impinging the array.
The distance from element n (the coordinate is (xn, yn, zn)) to the origin of the

coordinate system is defined as

⃗⃗⃗

⃗⃗⃗

⃗⃗

(1.1)

where: ⃗⃗⃗ is the unit vector on direction of the incident waves at element 0 at the
origin of the coordinate system and is represented by
⃗⃗⃗

⃗⃗⃗

⃗⃗

(1.2)

Consequently, the wavefront arrives at element n sooner than at element 0 and
the differential distance is calculated as:
(1.3)
Therefore, the phase difference between the signal at element n and element 0
is
(1.4)
where:

= 2π/λ is the wavenumber and λ is wavelength.
z
Plane wave
to element 0

Plane wave
to element n

nt
de
ci

In

ΔRn

w
es
av



y
0



1

d

2

x

...

n
N-1

Figure 1.6. The analyzed linear array [17].

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α