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 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 List of Figures List of Tables .6 Introduction .7 I Rationale for the Study 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 1/112 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) 2/112 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 3/112 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 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 4/112 The resulting AF will have the minimum firstl-null beamwidth for the specified SLL, and the sidelobes will all be equal in magnitude For example, a ULA applied Dolph-Chebyshev weighting method, so-called Chebyshev array, has been depicted in Figure B.1 This Chebyshev array has 20 elements and half-wavelength spacing (d=λ/2) Consequently, the resulting weight vector is shown in Table B.1 Table B.1 Resulting weights computed by Dolph-Chebyshev weighting method Array element (k) ±1 ±2 ±3 ±4 ±5 ±6 ±7 ±8 ±9 ±10 Weights computed by Dolph-Chebyshev method 0.325609 0.285577 0.391037 0.504613 0.620341 0.731470 0.831024 0.912427 0.970100 1.000000 The resulting normalized AF is plotted in Figure B.2 Note that as desired, the sidelobes are equal in magnitude and 30 dB down form the peak of the main beam The FNBW is approximately 16.9 degrees and is the minimum possible beamwidth obtainable for the specified SLL (-30dB in this case) 98/112 z Incident waves  x -N d -2 d/2 d/2 -1 d N Figure B.1 Geometry of ULA antennas of 2N elements [26] Figure B.2 Normalized array factor for 20-element Chebyshev arrays with sidelobes at -30 dB C Software for Modeling Adaptive Beamforming in Smart Antennas Applying the fundamentals of adaptive beamforming, a software of adaptive beamforming in smart antennas has been built in order to investigate the basis of adaptive beamforming and to support the study Full analysis, explanation, and the 99/112 simulation results of the software have been presented in publications [VJISAP.7], [VJISAP.8], [REV-ECIT.9], some of which are presented hereafter C.1 Application Model The application model of adaptive beamforming in smart antennas has been built based on the smart antennas presented in Figure A.9 in section A.3 The detail parameters are as follows: - Antenna arrays:  Types of array: 1×8 element ULA and 8×8 element-Rectangular Uniform Planar Array;  Antenna elements are isotropic, inter-element spacing is a half of wavelength (λ/2);  There is no mutual coupling between the array elements - Adaptive beamforming algorithms:  LMS algorithm: μ = 0.001; AWGN (mean = and variance = 0.1); and number of iterations: 500;  Dolph-Chebyshev weighting method;  Combination algorithms: LMS and Dolph-Chebyshev weighting method C.2 Simulation Results C.2.1 Uniform Linear Arrays C.2.1.1 Pattern with Steered Main Beam Figure C.1 demonstrates the capability of LMS-based beamformer to steer the main beam of 8-element ULA toward the desired directions with elevation angles at -49°, -30°, 30°, 60°, respectively As shown in Figure C.1, the beamformer is able to correctly steer the main beam of the ULA to the predefined directions The bigger desired angle (the farther away from angle θ = 0°) is steered, the wider the 100/112 beamwidth is obtained This is because of the characteristics of geometry of ULA When the steered angle is up to about 60°, the main beam broadens suddenly Thus, this limits the effective space of steering of the smart antennas Figure C.1 The main lobes of the 8-element ULA have been steered to the desired directions as θ = 49°, -30°, 30°, 60° Figure C.2 Five nulls have been set at elevation angles of -55°, -35°, -15°, 20°, and 45° 101/112 C.2.1.2 Pattern Imposed with Nulls While the main lobe can be steered, the undesired interferences can be eliminated by placing nulls simultaneously For example, the beamformer can be applied to set five nulls in elevation angles at -55°, -35°, -15°, 20°, 45°, which are demonstrated in Figure C.2 However, this can cause broadening the main lobe or increasing some sidelobe levels C.2.1.3 Pattern with Steered Main Beam and Nulls Figure C.3 shows the capability of the beamformer to steer the main beam and place nulls at the same time Figure C.3 The main beam is steered to θ = 30° and nulls are set at θ = -55°, -35°, -15°, 0°,45° at the same time C.2.1.4 Pattern with Sidelobes Suppression Applying the Dolph-Chebyshev weighting method, the beamformer can suppress all sidelobes to a same pre-set level, e.g SLL of -30dB The simulation result has been depicted in Figure C.4 C.2.1.5 Pattern with Steered Main Beam and Suppressed Sidelobes In order to steer the main lobe and suppress the side lobes simultaneously, LMS algorithm and Dolph-Chebyshev weighting method are utilized as a 102/112 combination adaptive beamforming algorithm This combination algorithm can be used to steer the main beam to the direction at θ = 30° and suppress the side lobe to -20 dB at the same time The simulation results for this case have been given in Figure C.5 It can be seen that the pattern has main beam at θ = 30° and side lobe levels are about -20dB, which is lower than that of using only LMS algorithm However, the beamwidth is broadened compared to that of applying only LMS algorithm Figure C.4 The optimized pattern with all side lobe levels are suppress to -30dB by Dolph-Chebyshev weighting method Figure C.5 The optimized pattern by applying both LMS algorithm and Dolph-Chebyshev weighting method 103/112 C.2.2 Planar Arrays To demonstrate the ability of the beamformer, one investigation case, of which the optimized pattern of the arrays have main beam at the desired direction (elevation angle θ = 30°, azimuth angle Φ = 30°) and interference at (θ = 0°, Φ = 0°), has been performed First of all, only LMS algorithm has been applied for the beamformer to optimize the array pattern The result has been illustrated in Figure B.6 Then, both LMS algorithm and Dolph-Chebyshev weighting method are applied for the beamformer and the optimization has been given in the Figure C.7 As shown in these figures, the results have been obtained the same as in the case of ULAs but with one more dimension: azimuth angle That is, with planar arrays, the main beam Elevation (θ) can be steered towards any points in effective area of its half space Azimuth (Φ) Elevation (θ) Elevation(θ) Figure C.6 The optimized pattern of 1×8 ULA using LMS algorithm Azimuth (Φ) Figure C.7 The optimized pattern of 1×8 ULA using both LMS algorithm and DolphChebyshev weighting method 104/112 D Supported Simulation Results D.1 Additional Results for Patterns with Single and Multiple Nulls For more information, the radiation patterns of all investigated cases including single and multiple nulls (in sections: 3.2.2.2 and 3.2.2.3; 3.3.2.2 and 3.3.2.3; 3.4.2.2, 3.4.2.3, and 3.4.2.4), have been investigated and demonstrated with the theta angle resolution of 0.1 degree D.1.1 BA-based Beamformers Using Amplitude-only Control The optimized pattern with a single symmetrical null at ±14° and multiple nulls at ±(14°, 26°, 33°) have been demonstrated in Figure D.1, and Figure D.2, respectively It can be seen from these results that the nulls have been exactly placed in the predefined locations Figure D.1 Pattern with a single symmetric null in the range of θ: a) (-90°, 90°); b) (13°, 16°) 105/112 Figure D.2 Pattern with three symmetric nulls in the range of θ: a) (-90°, 90°); b) (12°, 35°) Figure D.3 Pattern with a single null in the range of θ: a) (-90°, 90°); b) (13°, 16°) 106/112 D.1.2 BA-based Beamformers Using Phase-only Control The optimized patterns imposed with a single null at 14° have been demonstrated in Figure D.3 for two cases: without (ideal) or with mutual coupling The results show that a single null has been placed exactly at desired location in both cases a) b) c) d) Figure D.4 Pattern with three nulls in the range of θ: a) (-90°, 90°); b) (-50°, -46°); c) (18°, 22°); d) (38°, 42°) In addition, the optimized patterns imposed with multiple nulls (at -48°, 20°, 40°) have been demonstrated in Figure D.4 for two cases: without (ideal) or with mutual coupling The results show that three nulls with shallower levels have been 107/112 placed at the predefined locations due to the effect of mutual coupling Besides, the mutual coupling slightly changes the location of the null at -48° to -47.5° D.1.3 BA-based Beamformers Using Complex-weight Control The optimized patterns with a single symmetrical null (at 140) and multiple nulls {at (-33°, -26°, -14°) or (-40°, 20°, 40°)} have been demonstrated in Figure D.5, Figure D.6, and Figure D.7, respectively It can be seen from these results that the nulls have been exactly placed in the predefined locations Figure D.5 Pattern with a single symmetric null in the range of θ: a) (-90°, 90°); b) (13°, 16°) 108/112 Figure D.6 Pattern with three nulls in the range of θ: a) (-90°, 90°); b) (-34°, -13°) Figure D.7 Pattern with three nulls in the range of θ: a) (-90°, 90°); b) (-42°, 42°) 109/112 D.2 Some Sets of Weights for the Investigated Scenarios For more detail reference of data in sections 3.2.3, 3.3.3, and 3.4.2, some sets of weights computed by proposed beamformers are given as follows: D.2.1 Sets of Weights for PHA_BA_ABF Table D.1 Some sets of weights consisting amplitudes (an) and phases (δn) of the patterns shown in Figures 3.4-3.6 Array element Fixed weights of -30dB Chebyshev array n an ±1 Element phases δn (in radian) computed by PHA_BA_ABF Figure 3.4 Figure 3.5 Figure 3.6 1.00000 0.016937 0.059705 0.125650 ±2 0.97010 0.041680 0.050175 0.002765 ±3 0.91243 0.033901 0.084976 0.166180 ±4 0.83102 0.017478 0.105600 0.173400 ±5 0.73147 0.012968 0.028366 0.183750 ±6 0.62034 0.060651 0.029833 0.308910 ±7 0.50461 0.078696 0.011482 0.500000 ±8 0.39104 0.065665 0.126320 0.046827 ±9 0.28558 0.061811 0.016902 0.498720 ±10 0.32561 0.093191 0.148000 0.353560 D.2.2 Sets of Weights for AMP_BA_ABF Table D.2 Some sets of weights for the patterns shown in Figures 3.9-3.12 Array element Fixed weights of -30dB Chebyshev array n an Figures 3.9 Figures 3.10 Figures 3.11 Figures 3.12 ±1 1.00000 1.000000 1.00000 1.00000 1.00000 ±2 0.97010 0.986907 0.97726 1.02739 0.95960 ±3 0.91243 0.956814 0.98038 0.98937 0.87663 Element amplitude an computed by AMP_BA_ABF 110/112 Array element Fixed weights of -30dB Chebyshev array n an Figures 3.9 Figures 3.10 Figures 3.11 Figures 3.12 ±4 0.83102 0.895477 0.92770 0.99218 0.75413 ±5 0.73147 0.794862 0.76346 0.82692 0.59683 ±6 0.62034 0.663968 0.61546 0.72266 0.43304 ±7 0.50461 0.514819 0.55486 0.46497 0.27091 ±8 0.39104 0.372158 0.43325 0.32789 0.15151 ±9 0.28558 0.254584 0.21658 0.13267 0.06112 ±10 0.32561 0.314159 0.25241 0.06870 0.02146 Element amplitude an computed by AMP_BA_ABF D.2.3 Sets of Weights for CW_BA_ABF Table D.3 Some sets of weights for the patterns shown in Figures 3.16-3.21 Array element n Element complex weight ( Figures 3.16 an δn ) computed by CW_BA_ABF Figures 3.17 an δn Figures 3.18 an δn ±1 0.9829 0.0057 0.9636 0.0037 0.8896 0.0702 ±2 0.9562 0.0094 0.9484 0.0195 0.8882 0.0452 ±3 0.9195 0.0174 0.9314 0.0335 0.8289 0.0035 ±4 0.8485 0.0203 0.8767 0.0058 0.7724 0.0162 ±5 0.7457 0.0038 0.7208 0.0218 0.7583 0.0142 ±6 0.6336 0.0281 0.5889 0.0181 0.6431 0.0561 ±7 0.5037 0.0523 0.5102 0.0048 0.4551 0.0358 ±8 0.3730 0.0252 0.4123 0.0899 0.4206 0.0530 ±9 0.2629 0.0023 0.2379 0.0092 0.2432 0.0497 ±10 0.3117 0.0444 0.2727 0.0822 0.2452 0.0397 111/112 Array element n Element complex weight ( Figures 3.19 an δn ) computed by CW_BA_ABF Figures 3.20 an δn Figures 3.21 an δn ±1 0.9962 0.0129 0.9205 0.0059 0.9663 0.0291 ±2 0.9258 0.0147 0.9717 0.0196 0.9591 0.0254 ±3 0.9325 0.0576 0.8562 0.0250 0.8543 0.0070 ±4 0.8865 0.0259 0.8400 0.0510 0.7153 0.0010 ±5 0.7834 0.0446 0.7218 0.0419 0.5716 0.0494 ±6 0.6610 0.0618 0.6228 0.0927 0.4457 0.0248 ±7 0.5125 0.0834 0.4872 0.0039 0.2859 0.0014 ±8 0.3638 0.0138 0.3213 0.0879 0.1618 0.0335 ±9 0.1704 0.0094 0.2185 0.0396 0.0936 0.0125 ±10 0.0911 0.0980 0.1178 0.0670 0.0412 0.0073 112/112 ... 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... 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... 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