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Application of Design of Experiments and Evolutionary Algorithms toSelf-Structuring Electromagnetic Scatterer andOptimizat...
Agenda               MotivationIntroduction               Contribution of this dissertationRFID Antenna   Conventional lim...
MotivationWhy EM problems need optimization techniques?                       The intelligence of optimization            ...
Contribution of This Dissertation                       Conventional                                           Intelligenc...
Agenda               MotivationIntroduction               Contribution of this dissertationRFID Antenna   Conventional lim...
Limitations of Conventional Passive RFID Systems                                                                          ...
Proposed Dual­Antenna Structure for Passive Tags                                   When Zre=ZC*, maximal power    Reader  ...
Further Details                                                The proposed dual-antenna tag                              ...
How to Design Such a Complex Antenna Structure?               Receiving             Backscattering               Co-design...
Our Strategy: Design of Experiments (DOE)                                                             Benchmark structure ...
Step 1: Determine the Interested Sub­Region                                                                        Paramet...
Step 2: Allocate Suitable Treatment Combinations       Full factorial design                           Fractional factoria...
Step 3: Analyze Experimental Results              Main effect                            Two-factor interaction           ...
Step 4: Formulate Response Surface ModelsIt is convenient to cast the significant effects into response surface models!   ...
Step 5: Simultaneously Optimize the Four Objectives    We obtain 4 response                    Model the equality into    ...
Verification 1: Isolation and Antenna Impedances         Zre under short state                               Zre under ope...
Verification 2: Receiving Performance         Experimental setup                                 Experimental results    T...
Verification 3: Backscattering PerformanceExamination of scalar differential RCS (ASK):                                   ...
Verification 4: Enhancement of Detection Range     Examination of vector differential RCS:                                ...
Agenda               MotivationIntroduction               Contribution of this dissertationRFID Antenna   Conventional lim...
Motivation 1: Adaptive RCS Control            RCS Reduction                RCS Enhancement           Shaping, coating, and...
Motivation 2: Self­Structuring Devices            Self-structuring                                    Self-structuring    ...
Our Idea: Self­Structuring Electromagnetic Scatterer                 Self-Structuring electromagnetic scatterer (SSES)    ...
SSES Template                                                                             This template grounds on the sca...
How to Find a Suitable State for Switches?     Different strip lengths are provided by opening/closing the switches,     a...
A Novel Approach: The FFD­Based Method          Use DOE to handle the SSES problem                                        ...
“Effects” of the Switches                         s26                        Variation of σ(θin, θopt)                    ...
Formulate the Effect Estimations into a COPThe designed 1024 experiments give us great information! Estimate the effect an...
Experimental Setup             SSES                                                                                       ...
Original PerformancePEC plate of the same size              Normal Incidence (θin = 0°)                                   ...
RCS Reduction: Normal IncidenceMeasurementenvironment y-polarized incident wave Normal incidence The optimum result was fo...
RCS Reduction: Oblique IncidenceMeasurementenvironment y-polarized incident wave Oblique incidence (θin = 30°) The optimum...
RCS Reduction: Oblique IncidenceMeasurementenvironment y-polarized incident wave Oblique incidence (θin = 30°) The optimum...
RCS Enhancement: Normal Incidence                             Normal incidence (θin = 0°)      θopt = 0°                 θ...
RCS Enhancement: Oblique Incidence                             Oblique incidence (θin = 30°)     θopt = –30°              ...
Evaluation of Algorithms    RCSR for normal incidence                            RCSR for oblique incidence           θopt...
Agenda               MotivationIntroduction               Contribution of this dissertationRFID Antenna   Conventional lim...
Pixelized Design Technique                                                     Handset                          Convention...
Historical Perspective                                                                                                    ...
Why Do We Develop a Pixelized Design Tool?       We attempt to develop a competent pixelized design tool, which can      a...
Implementations of the Pixelized Design Tool                 We use the scripting interface provided by Ansoft,   performi...
The Biggest Challenge of This Technique...                                                 Metal                      Air ...
Investigation of Single­Objective Operation                                      GA and BPSO are implemented for          ...
Validation of Multiobjective Operation                                                                 Objective spacePare...
Example : A MIMO Antenna SystemPareto front of each algorithms      c-MOPSO                            SPEA2              ...
Wide­ and Multi­Band Pixelized Antenna Designs            Internal antenna designs for handset application                ...
Conventional Objective Functions: Max(|S11|j)         Minimize the maximum |S11| of sample frequencies           Motivatio...
Conventional Objective Functions: Sum(|S11|j,dB)          Minimize the sum of |S11|dB among sample frequencies           M...
General Rules of Objective FunctionsIn fact, max(|S11|j) and sum(|S11|j,dB) come from the same general rule!            Su...
Verification of the Statement Interested band: Lower band (700–960 MHz)                 Sample frequency j: 700, 720, 740,...
A Novel Approach: A Multiobjective­Based Method Use multiobjective framework to optimize one single measure  Motivation: T...
The Optimal Design   Primary antenna topology                                  Optimal design                       40 mm ...
Summary of Today’s Presentation                       Significance               Implementation             Performance A ...
Future Work       Efficacy enhancement of the pixelized design tool    The initialization of the design space A good solut...
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YSChen: Dissertation Defense

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YSChen: Dissertation Defense

  1. 1. Application of Design of Experiments and Evolutionary Algorithms toSelf-Structuring Electromagnetic Scatterer andOptimization of Antenna Structures Dissertation Defense, June 4, 2012 Yen-Sheng Chen National Taiwan University, Taiwan
  2. 2. Agenda MotivationIntroduction Contribution of this dissertationRFID Antenna Conventional limitations Application A novel tag structure and its validityRCS Control Adaptive RCS controlApplication Self-structuring electromagnetic scattererDesign Tool An automatic antenna design toolApplication Wide- and multi-band antenna designs 2 of 54
  3. 3. MotivationWhy EM problems need optimization techniques? The intelligence of optimization methods helps engineers develop sophisticated and powerful applications! The procedure terminates at a optimum solution, instead of an acceptable one It is a systematic procedure and gives unambiguous instructions to solve problems 3 of 54
  4. 4. Contribution of This Dissertation Conventional Intelligence of Our idea optimization method limitationsA dual-antenna We propose a new tag structure for Conventional structures DOE systematically structure to have maximum RFID tags are not optimized for both handles multiple reception and maximum reception and detection design considerations differential RCSSelf-structuringelectromagnetic We lack a smart and We propose SSES for FFD efficiently solvesscatterer (SSES) reconfigurable reflective RCS-reduction and surface for RCS control reflectarray applications synthesis problems An automaticantenna design We develop a pixelized Evolutionary The procedure of antenna tool designs is often tedious design tool for practical algorithms act as the design situations kernel of this tool 4 of 54
  5. 5. Agenda MotivationIntroduction Contribution of this dissertationRFID Antenna Conventional limitations Application A novel tag structure and its validityRCS Control Adaptive RCS controlApplication Self-structuring electromagnetic scattererDesign Tool An automatic antenna design toolApplication Wide- and multi-band antenna designs 5 of 54
  6. 6. Limitations of Conventional Passive RFID Systems When ZA=ZC*, maximal power Reader Tag transfer to the digital core ZA Rectifier Digital ZC CoreReceived Signal State 2=Short Backscatter Modulator ZL=0 and ZL=ZC State 1=Match Match/short introduce a smaller level difference in Time the backscattered signalsK. Finkenzeller, RFID Handbook: Radio-Frequency Identification Fundamentals and Applications, 2nd ed.: Wiley, 2004. 6 of 54
  7. 7. Proposed Dual­Antenna Structure for Passive Tags When Zre=ZC*, maximal power Reader Tag continuously supply to the chip Receiving antenna Zre Rectifier Digital ZC CoreReceived Backscattering antenna Signal State 2=Short Zsc Backscatter Modulator ZL=0 and ZL=∞ State 1=Open When Xsc = 0, open/short introduce a Time larger level difference 7 of 54
  8. 8. Further Details The proposed dual-antenna tag The tag IC with multiple RF ports has been commercially used The open/short impedance state can be realized by a switching transistor Each of the antenna has its design considerations, and the mutual coupling should be kept small The co-design of the receiving and backscattering antennas within a very small area is the most challenging task!P. V. Nikitin and K. V. S. Rao, “Performance of RFID tags with multiple RF ports,” in Proc. IEEE-APS Symp., Honolulu, HI, June 2007,pp. 5459–5462. 8 of 54
  9. 9. How to Design Such a Complex Antenna Structure? Receiving Backscattering Co-design of antenna antenna the structure For the continuous For the maximum The performance of and maximum level difference of the antennas should power reception backscattering signal be uncorrelated Zre = Zc* Xsc = 0 Minimize |S21|If we design the antenna structure with trial-and-error approaches... The design process may fail because there are too many design goals There is no guarantee that the best solution has been foundWe need a systematic design method to study this problem! 9 of 54
  10. 10. Our Strategy: Design of Experiments (DOE) Benchmark structure Frequencies: 902–928 MHz 8 decision variables 4 objective functions Choose Zc = 33 – j 112 Ω Meander dipole within a small area: Rin ≈ 10 Ω Response surface Evolutionary algorithms Design of experiments Black-box approach Uncover the black box Search the solution space Treatment combination Build the solution sub-space Every decision variable is Differentiate the significance treated as equally important between decision variables Less human bias More human interpretation Solution space Random initialization Blind search Solution space Designed treatmentR. A. Fisher, “The arrangement of field experiments,” Journal of the Ministry of Agriculture of Great Britain, vol. 33, pp. 503–513, 1926. 10 of 54
  11. 11. Step 1: Determine the Interested Sub­Region Parameter Low (-1) High (+1) w1 3.5 4 d1 0.8 1.2 t1 3 3.5 w2 2.5 3 d2 0.8 1.2 t2 2.75 3.25How to decide the level of each factor? Set l1 = l2 = 7 mm Design frequency: 915 MHz Prior knowledge Combining our EM knowledge and experience Size limitations The 32.8 × 32.8 mm2 area adds constraints to the choice of levels Iterative strategy As we learn more about which factors are important and which levels produce the best result, the region of interest will usually become narrower 11 of 54
  12. 12. Step 2: Allocate Suitable Treatment Combinations Full factorial design Fractional factorial design (FFD) ` Performing only a subset of 2k The treatment combinations combinations; it gains similar are all the 2k enumeration results but loss some accuracy t1 t1 Example: (-,-,+) (-,+,+) Example: (-,+,+) (+,-,+) 23 full design (+,-,+) (+,+,+) 23–1 FFD (-,-,-) (-,+,-) d1 (-,-,-) d1 (+,-,-) w1 (+,+,-) w1 (+,+,-) 26 full design 26–1 FFD (resolution VI) Performing 64 simulations, and it Performing designed 32 simulations gives us the most detailed information 26–2 FFD (resolution IV) Performing designed 16 simulationsWhatever experimental design it is, the factors are varied together, instead of “one-factor-at-a-time” 12 of 54
  13. 13. Step 3: Analyze Experimental Results Main effect Two-factor interaction Higher-order interaction The variation of Rre caused The variation of the main effect of The three-factor interaction between by one single factor t2 toward Xre caused by d2 w2, t2, and d2 = (The two-factor interaction of t2 and d2 w2– = 15.37 t2+ = 180 when w2 is at the high level) – (that of t2– = 150 t2 and d2 when w2 is at the low level ) d2– w2+ = 15.27 t2+ = 142 t2– = 132 d2+ Sparsity-of-effects principle Two-factor interaction = Higher-order interactions are often Main effect of w2 = w2 + – w2 = –0.1 – very insignificant (142–132)/2 – (180–150)/2 = –9.5 These effect estimates should be justified by formal statistical inferences! They are realizations sampled from each effect’s distribution Put insignificant effects in the models will waste resources when trying to optimize unimportant factorsD. C. Montgomery, Design and Analysis of Experiments, New York: Wiley, 2005. 13 of 54
  14. 14. Step 4: Formulate Response Surface ModelsIt is convenient to cast the significant effects into response surface models! k k −1 k k − 2 k −1 k y = β 0 + ∑ β i xi + ∑ ˆ ∑β x x j +∑ ij i ∑ ∑β x x j x j + ... + β ij ...k xi x j ...xk ijl i where βi = Ei /2 i =1 i =1 j = i +1 i =1 j = i +1 l = j +1 For example, ⎛ w − 3.75 ⎞ ⎛ t − 3.25 ⎞ ⎛ w − 3.75 ⎞⎛ t1 − 3.25 ⎞ Rre ( Ω ) = 15.30 + 1.24 ⎜ 1 ˆ ⎟ + 4.75 ⎜ 1 ⎟ + 0.72 ⎜ 1 ⎟⎜ ⎟ ⎝ 0.25 ⎠ ⎝ 0.25 ⎠ ⎝ 0.25 ⎠⎝ 0.25 ⎠ Rre Xre Xsc |S21|Estimates Full R6-FFD R4-FFD Estimates Full R6-FFD R4-FFD Estimates Full R6-FFD R4-FFD Estimates Full R6-FFD R4-FFD I0 15.32 15.30 15.23 I0 151.4 150.94 150.3 I0 -3.56 -4.51 -4.5 I0 -37.39 -37.07 -39.1 w1 1.22 1.24 0.98 w1 24.4 24.72 20.18 d1 -12.3 -12.95 -12.95 d1 -1.61 -2.86 t1 4.79 4.75 4.67 d1 7.16 t1 6.18 6.57 5.44 t1 -5.69 -5.29 -8 d2 -0.33 -0.35 t1 106.5 105.49 105.3 d2 7.17 6.79 6.53 t2 1.87 2.31 w1*t1 0.71 0.72 0.45 d2 -14.32 -13.14 -14.02 t2 71.48 71.71 71.52 w2 -2.93 -2.82 -3.23 d1*t2 -0.24 t2 9.76 w2 16.36 15.33 15.64 d1*t2 1.33 w1*t1 7.46 d1*t1 -2.98 t1*t2 -6.59 -6.26 -8.22 d1*t2 -5.24 w2*t2 3.3 t1*w2 1.29 t1*t2 3.48 t2*w2 -2.03 -1.8 -3.11 d2*t2 -4.73 t1*t2*w2 -3.83 -3.74 -4.49 t1*d2*t2*w2 1.41 14 of 54
  15. 15. Step 5: Simultaneously Optimize the Four Objectives We obtain 4 response Model the equality into Solve the non-linear Rank these solutions surface models a constrained problem programming problem by Derringer’s Min. |S21| s.t. by Matlab desirability functions Rre = 33, Xre = 112, 102 ≤ Xre ≤ 122, A series of solutions Overall desirability Xsc = 0, Min. |S21| −10 ≤ Xsc ≤ 10, are found D = (d1d2d3d4)1/4 Rre ≥ 12 Number w1 d1 t1 w2 d2 Rre t2 Xre Xsc |S21| D 1 0.97 0.48 –0.43 1 –0.39 0.86 17.36 112 –1.04 –38.86 0.77 As large as possible Hit the target As small as possible d1 d2 d3 d4 1 1 1 1 0.67 0.90 0.59 0 Rre 0 Xre 0 Xsc 0 |S21| 12 17.36 20 102 112 122 –10–1.04 10 –38.86–30 –45 17.36 − 12 112 − 102 −1.04 − ( −10 ) −38.86 − ( −30 ) d1 = d2 = d3 = d4 = 20 − 12 112 − 102 0 − ( −10 ) −45 − ( −30 ) = 0.67 =1 = 0.90 = 0.59G. Derringer and R. Suich, “Simultaneous optimization of several response variables,” Journal of Quality Technology, vol. 12, no. 4, pp. 214–219,Oct. 1980. 15 of 54
  16. 16. Verification 1: Isolation and Antenna Impedances Zre under short state Zre under open state |S21| = –46.1 dB @ 915 MHz Simu. Meas. Performance 12.73 + 14.28 + Open j113.09 j116.58 The impedance of the receiving 12.76 + 14.81 + antenna remains unchanged Short j114.11 j107.29 DOE significantly optimizes the isolation and achieve the design goals in a systematic manner 16 of 54
  17. 17. Verification 2: Receiving Performance Experimental setup Experimental results The receiving capability of the receiving antenna is stable!The variation of receiving power is less than 0.2 dBIn contrast, the receiving capability of the conventional tag antenna severely degrades duringthe short-circuited state 17 of 54
  18. 18. Verification 3: Backscattering PerformanceExamination of scalar differential RCS (ASK): Tag antenna The scalar differential RCS of the dual-antenna structure is Tx antenna much larger than the conventional tag design Pr ( 4π ) d 3 4 Rx antenna The reliability is thus improved σ= Pt Gt Gr λ 2 d = 0.75 m Conventional tag structure The proposed dual-antenna tag Open / short Max. RCS = –23.5 dB Min. RCS = –31.9 dB Match Receiving Backscattering antenna antenna Max. RCS = –24.4 dB Min. RCS = –50.1 dB 18 of 54
  19. 19. Verification 4: Enhancement of Detection Range Examination of vector differential RCS: Δ1 >1 If a coherent detection method is used by the readers, Δ2 the detection capability is proportional to: Δ = Et ( Z L1 ) − Et ( Z L 2 ) = Γ ( Z L1 ) − Γ ( Z L 2 ) I m Er The proposed tag structure have better detection since that the impedance states are open and short Associated detection range: The backward detection range is determined by: 1 ⎛ PG 2 λ 2 ⎞ 4 d max =⎜ t t Δσ ⎟ EIRP = 4 W ⎜ ( 4π )3 S ⎟ ⎝ R ⎠ Sensitivity = –80 dBm The associated detection range remains unchanged even if the chip impedance varies with the absorbed power or operation frequencyR. B. Green, “The general theory of antenna scattering,” Ph.D. dissertation, Dept. Elect. Comput. Eng., Ohio State Univ, Columbus, OH, 1963. 19 of 54
  20. 20. Agenda MotivationIntroduction Contribution of this dissertationRFID Antenna Conventional limitations Application A novel tag structure and its validityRCS Control Adaptive RCS controlApplication Self-structuring electromagnetic scattererDesign Tool An automatic antenna design toolApplication Wide- and multi-band antenna designs 20 of 54
  21. 21. Motivation 1: Adaptive RCS Control RCS Reduction RCS Enhancement Shaping, coating, and Phase shifters, varactors, cancellation have been used and switches are used as as RCS-reduction methods RCS-enhancement methods Application: Absorber and Application: Navigation and radar application reflective surfaceControlling RCS properties is so important, but we lack a smart and reconfigurable surface to accommodate both the needs! 21 of 54
  22. 22. Motivation 2: Self­Structuring Devices Self-structuring Self-structuring Reconfigurable antennas (SSA), two-port network, electromagnetic 2000 2009 shutter, 2011 By opening and closing the By opening and closing the By opening and closing the switches, SSA automatically switches, the device can switches, the device can configures itself into acts as filter, attenuator, acts as an open or a closed different missions phase shifter, and matching surface The template extends to network, respectively patch antennas in 2009C. M. Coleman, E. J. Rothwell, J. E. Ross, and L. L. Nagy, “Self-structuring antennas,” IEEE Antennas Propagat. Mag., vol. 44, no. 3, pp. 11–23,June 2002. 22 of 54
  23. 23. Our Idea: Self­Structuring Electromagnetic Scatterer Self-Structuring electromagnetic scatterer (SSES) Receiver-type Definition: A reflective surface which z sensor can adapt itself to new operational θin objectives, such as RCS reduction and RCS enhancement SSES θ opt template By opening and closing the switches, various scattering properties are Microprocessor produced, and the best configuration … x is found by some efficient algorithms N control lines Its potential uses: Bistatic absorber Space-wave phase shifter Reflectarray application Space-wave attenuator Reflector of antenna application Smart antennas 23 of 54
  24. 24. SSES Template This template grounds on the scattering properties of thin strips Different strip lengths provide extra If the strip length is identical, this But if wedirection in certain direction this is notdirection TheThe plane is not in phase Consider direction is in phase interest is in phase direction this in phase phases, so Point L1 L2 L3 L4 L5 L6 w d source Metal plate SSES 10 50 20 20 20 20 10 20 (Unit: mm; operational frequency: 1.5 GHz)K. Barkeshli and J. L. Volakis, “Electromagnetic scattering from thin strips–Part I: Analytical solution for wide and narrow strips,” IEEE Trans.Educ., vol. 47, pp. 100–106, Feb. 2004. 24 of 54
  25. 25. How to Find a Suitable State for Switches? Different strip lengths are provided by opening/closing the switches, and the best state of the switches is determined by binary algorithms Conventional method: GA 1 0 0 0 1 1 … 0 1 GA doesn’t know the problem nature, simply s1 s2 s3 s4 s5 s6 … s29 s30 performing a blind search Objective function: σ(θin, θopt) All the 30 switches have equal chance to share the genetic operators To find the most suitable state of the switches, Initial Fitness Selection it takes 6000 functional evaluations population evaluation (s = 2) (Npop = 120) (Measurement) Elitist Mutation Crossover replace- (pm = 0.1) (pc = 0.5) mentC. M. Coleman, E. J. Rothwell, and J. E. Ross, “Investigation of simulated annealing, ant-colony optimization, and genetic algorithms for self-structuring antennas,” IEEE Trans. Antennas Propagat., vol. 52, no. 4, pp. 1007–1014, Apr. 2004. 25 of 54
  26. 26. A Novel Approach: The FFD­Based Method Use DOE to handle the SSES problem z σ(θin, θopt) The SSES problem can be viewed as a process θ opt θin Input factors: 30 switches Chosen level of input factors: 2 states Output response: σ(θin, θopt) x Why considering the problem as a process? By performing a properly-designed experiment, the effect of each switch and their interactions can be obtained What is a “properly-designed” experiment? A resolution-V fractional factorial design Minimum aberration Xu’s 230–20 design Minimum number of experimental trialsH. Xu, “Algorithmic construction of efficient fractional factorial designs with large run sizes,” Technometrics, vol. 51, no. 3, pp. 262–277,Aug. 2009. 26 of 54
  27. 27. “Effects” of the Switches s26 Variation of σ(θin, θopt) Main effect …… s27 (30) E1 E2 E26 E27 E28 E30 s28 Two-factor … … s29 interactions (435) E12 E13 E14 E15 E26,27 E26,28 E29,30 s30 Three-factor interactions …… (4060) E123 Sparsity-of-effects principle E28,29,30 E124 E125 E26,27,28 Three-factorEffect Two-factor interaction Main interaction TheThe on/off of σ(θof θ27 ) The change statesin, s opt on/off state of 28 would Xu’s 230–20 design provides us … produced by two-factor in affect the a change would affect the main Higher-order interactions unique estimation of all theinteraction s26, and vice s26 s27 the on/off state of and effect of between s26 versa main effects and interactions Thirty-factor interaction 27 of 54
  28. 28. Formulate the Effect Estimations into a COPThe designed 1024 experiments give us great information! Estimate the effect and interactions Calculate the estimation of 30 main effects Ei and the 435 two-factor interactions Eij Significance inference A combinatorial optimization Only Identify the influential effects problem (COP) This helps us investigate the efficiency of GA Minimize z = ∑ Ei si + ∑∑ Eij si s j i i j Subject to si ∈ {−1, 1} , i = 1, 2,...30 Solve the associated COP Use shotgun hill climbing to solve the COP Stop when z does not improve for 100 iterations 28 of 54
  29. 29. Experimental Setup SSES NI connected cable template Post-processing of the RCS Bistatic RCS pattern Pr ( 4π ) Rt Rr 2 2 3 σ= Polarization: VV Pt Gt Gr λ 2 Front aspects –90°≤ θt ≤ 90° Frequency: 1.5 GHz Sampling intervals: discrete steps Rt = Rr = 2 m; Gt = Gr = 6 dBi of 5°The experimental setup was co-worked with Yao-Chia Chan in 2011. 29 of 54
  30. 30. Original PerformancePEC plate of the same size Normal Incidence (θin = 0°) Max: 8.56 dB z Oblique Incidence (θin = 30°) σ(θin, θopt) θ opt Max: 6.85 dB θ in x 30 of 54
  31. 31. RCS Reduction: Normal IncidenceMeasurementenvironment y-polarized incident wave Normal incidence The optimum result was found after 1024 runs Results: RCS reduction > 51 dB for θopt = 10°–90° When θopt = 0°, the 140°-phase is insufficient to produce a null 31 of 54
  32. 32. RCS Reduction: Oblique IncidenceMeasurementenvironment y-polarized incident wave Oblique incidence (θin = 30°) The optimum result was found Results: after 1024 runs RCS reduction > 52 dB for θopt = 10°–90° 32 of 54
  33. 33. RCS Reduction: Oblique IncidenceMeasurementenvironment y-polarized incident wave Oblique incidence (θin = 30°) The optimum result was found Results: after 1024 runs RCS reduction > 46 dB for θopt = 0°–(–90°) When θopt = –30°, the SSES failed to produce a null 33 of 54
  34. 34. RCS Enhancement: Normal Incidence Normal incidence (θin = 0°) θopt = 0° θopt = 10° θopt = 15° θopt = 20° RCSE=4.36 dB RCSE=3.46 dB RCSE=2.39 dB RCSE=1.06 dB Direct to a direction Normal large angleThe case θopt = 20° failed to steer beam to the The external Constructivedesired direction interferenceis phase insufficientWe will develop a better element in the future 34 of 54
  35. 35. RCS Enhancement: Oblique Incidence Oblique incidence (θin = 30°) θopt = –30° θopt = 0° θopt = 15° θopt = 30° RCSE=3.3 dB RCSE=0.3 dB RCSE=1.9 dB RCSE=3.96 dBWhen the incident wave comes from an oblique direction, SSES can steerbeam to desired anglesIt’s useful for reflectarray application fed with an offset antenna 35 of 54
  36. 36. Evaluation of Algorithms RCSR for normal incidence RCSR for oblique incidence θopt = 30° θopt = 60°The FFD-based method has similar performance as those found by GA, but it reduce 83% of processing time, why? The number of influential switches in SSES problem is only 10–20, but insignificant switches share the genetic operators with equal chances As the number of switches used increases, the efficiency of GA would degrade more drastically 36 of 54
  37. 37. Agenda MotivationIntroduction Contribution of this dissertationRFID Antenna Conventional limitations Application A novel tag structure and its validityRCS Control Adaptive RCS controlApplication Self-structuring electromagnetic scattererDesign Tool An automatic antenna design toolApplication Wide- and multi-band antenna designs 37 of 54
  38. 38. Pixelized Design Technique Handset Conventional design approach Pixelized design approach System environment ground Antenna pixels 0 1 1 1 1 L0 3 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 w3 0 1 0 0 0 0 0 0 0 0 1 0 Specified design space 0 1 0 0 0 0 d 01 0 0 0 1 0 Design 0 1 0 0 0 1 0 0L11 L2 space 1 0 w4 0 0 1 0 LCD 0 1 0 0 0 0 1 1 0 1 1 w w FR4 EncodeMinimize 2max(|S11|j)bitstream, The antenna topologyat the 1pixel states into a is Iron bar and perform= 890 and 1940 MHz as GA freqj search algorithms such automatically found Assign proper objective Identify the design space function(s) Form a solution domain of Perform binary optimization size 2N by pixelization algorithmM. P. Bendsøe and N. Kikuchi, “Generating optimal topologies in structural design using a homogenization method,” Comput. Methods in Appl.Mech. Eng., vol. 71, pp. 197–224, 1988. 38 of 54
  39. 39. Historical Perspective Pixels Pixels (ON/OFF) Ground (ON/OFF) Ground plane plane Patch antennas Planar monopole antennas In 1997, the pixelized design Some literatures extended the technique was first applied to technique to wideband planar patch antenna designs monopole antenna designs After that, most of the researches Both GA and PSO have been focused on wideband or multiband shown that they are useful patch antenna designs algorithms for this problem These literatures focused on addressing particular test examples, instead of extensively applying it to practical design situationsJ. M. Johnson and Y. Rahmat-Samii, “A novel integration of genetic algorithms and method of moments (GA/MoM) for antenna design,” 1997Applied Computational Electromagnetics Society Symposium Proceedings, Volume 2, Monterey, CA, March 17–21, pp. 1374–1381, 1997. 39 of 54
  40. 40. Why Do We Develop a Pixelized Design Tool? We attempt to develop a competent pixelized design tool, which can automatically design antennas and replace the conventional procedure We lack a detailed investigation 5 on performance enhancement Practical design situations give specified design space 1 4 The literatures focused on few design examples 2The technique can developinnovative antenna shapes 3 An automatic design tool can shorten the design cycle 40 of 54
  41. 41. Implementations of the Pixelized Design Tool We use the scripting interface provided by Ansoft, performing a batch of predefined simulations and retrieve the simulated results Matlab HFSS Control and Functional optimization Visual Basic Boundary evaluations conditions 1/0 Generate of materials Assign configurations Launch HFSS *.vbs Perform a binary Simulation according optimization algorithm to *.vbs Evaluate performance Generate Export analyzed results measure Data *.m Any result at matrix HFSS’s UIThis pixelized design tool is co-worked and compiled by Yao-Chia Chan in 2011–2012. 41 of 54
  42. 42. The Biggest Challenge of This Technique... Metal Air Air Metal Air Pixels Pixels An elaborate discretization A non-uniform discretization Require a huge number of pixels Incorporate priori knowledge to the discretization The solution domain is sensitive to design changes when the topology is The number of decision variable close to optimum significantly drops But it is insensitive to design changes The problem difficulty becomes when the topology is far from much easier optimumA. Erentok and O. Sigmund, “Topology optimization of sub-wavelength antennas,” IEEE Trans. Antennas Propagat., vol. 59, no. 1, pp. 58–69,Jan. 2011. 42 of 54
  43. 43. Investigation of Single­Objective Operation GA and BPSO are implemented for handling distinct problem natures Degrees of exploration and exploitation Tradeoff between effort and efficacy Solution space Solution space To find a satisfactory performance, it Optimum requires about 20 hours for 2000 so far functional evaluations Exploration Exploitation The ideal population size in pixelized In pixelized design problems, the design problems are found to be degree of exploration needs to be around 32–64 emphasize a little bitA. Colorni, M. Dorigo, F. Maffioli, V. Maniezzo, G. Righini, and M. Trubian, “Heuristics from nature for hard combinatorial optimizationproblems,” International Transactions in Operational Research, vol. 3, no. 1, pp. 1–21, 1996. 43 of 54
  44. 44. Validation of Multiobjective Operation Objective spacePareto optimization f2 (Objective #2)A Pareto-based multiobjective evolutionaryalgorithms identifies the nondominated set Pareto frontFour optimizers are implemented:NSGA-2, SPEA2, NSPSO, c-MOPSO f1 (Objective #1)All the performance measures in the user interface of HFSS can be extracted,such as S parameters, antenna gain, and radiation efficiency Minimize |S11| and |S21| at 900 MHzThe operations are validated by: Air Pixels Pixels #: 57 44 of 54
  45. 45. Example : A MIMO Antenna SystemPareto front of each algorithms c-MOPSO SPEA2 Benefit The decoupling is achieved by modification of the antenna structure Limitation More human intervention is required to achieve wideband operation 45 of 54
  46. 46. Wide­ and Multi­Band Pixelized Antenna Designs Internal antenna designs for handset application 40 mm Handset environment Design space 15 mm International standards An internal antenna design of F LTE700 DCS available area 40 × 15 mm2 F: Feed point GSM850 PCS GSM900 UMTS Ground 85 mm Air plane Two wide operational bands Pixels 0.8-mm-thick Lower band: 698–960 MHz Pixels #: 57 FR4 substrate Higher band: 1.71–2.17 GHz Conventional wide- and multi-band antenna designs use two objective functions: Max(|S11|j) and sum(|S11|j,dB) 46 of 54
  47. 47. Conventional Objective Functions: Max(|S11|j) Minimize the maximum |S11| of sample frequencies Motivation: Since the worst |S11| is improved iteration after iteration, a safe performance should be obtained and the BW should be enlarged Strategy: Sampling the center frequencies of two bands or uniformly sampling in two bands Sampling at two center frequencies Due to the Bode-Fano criterion, a good matching at a single frequency leads to severe impedance variation at the adjacent frequencies Interested bands: 698–960 and 1710–2170 MHz Uniformly sampling at two bands Sample |S11| Winner frequency 1 2 3 4 5 Candidate1 0.7 0.7 0.7 0.7 0.8 ^__^ Candidate2 0.1 0.1 0.1 0.1 0.9 >.<N. Jin and Y. Rahmat-Samii, “Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithm for multiband andwide-band patch antenna designs,” IEEE Trans. Antennas Propagat., vol. 53, no. 11, pp. 3459–3468, Nov. 2005. 47 of 54
  48. 48. Conventional Objective Functions: Sum(|S11|j,dB) Minimize the sum of |S11|dB among sample frequencies Motivation: Since the area of the physical quantity, namely |S11|dB, is minimized, the BW should be enlarged Strategy: Sampling the center frequencies of two bands or uniformly sampling in two bands Sampling at two center frequencies and Uniformly sampling at two bands Interested bands: 698–960 and 1710–2170 MHz The higher band is typically easier to achieve the exceedingly superior |S11|dB in the higher band nullify other worse values The algorithm is guided to exploit an improper solution sub-domainZ. Li, Y. E. Erdemli, J. L. Volakis, and P. Y. Papalambros, “Design optimization of conformal antennas by integrating stochastic algorithmswith the hybrid finite-element method,” IEEE Trans. Antennas Propagat., vol. 50, no. 5, pp. 676–684, May 2002. 48 of 54
  49. 49. General Rules of Objective FunctionsIn fact, max(|S11|j) and sum(|S11|j,dB) come from the same general rule! Sum(|S11|kj ) Sum[ (logk|S11|)j ] max... |S11|10... |S11|5... |S11|2 |S11| log2|S11|... log5|S11|... log10|S11|... min As k increase (for sum(|S11|kj )) As k increase (for sum[(logk|S11|)j ]) Wide band Narrow band Poor matching Good matching Concerning the worst case Concerning the best case 49 of 54
  50. 50. Verification of the Statement Interested band: Lower band (700–960 MHz) Sample frequency j: 700, 720, 740, ...,960 MHz Sum(|S11|kj ) Sum[ (logk|S11|)j ]Another verification for Sum(|S11|kj ) Laptop environment Design space: 60 × 8 mm2 # of pixels: 53 824–960 and 1710–2170 MHz Uniformly sampling 3 points in each band 50 of 54
  51. 51. A Novel Approach: A Multiobjective­Based Method Use multiobjective framework to optimize one single measure Motivation: Treating the wide and multiple bands as different objectives; within objective we intend to have good matching, and between objectives we intend to have wide BW Within objective: Apply sum[(logk|S11|)j ] with large k over chosen sample frequencies Between objectives: Apply sum(|S11|kj ) with large k for summarizing a final performance measure Within objective Between objectives fi = Sum[(log7|S11|)j ] F = Max(fi) ~ ~ 698 960 1700 Lower band 2200 Higher band (MHz) 51 of 54
  52. 52. The Optimal Design Primary antenna topology Optimal design 40 mm Conductor15 mm Air F Ground plane Second-stage pixelized design was performed on the junctions and edges of the primary design Benefit The wide and dual band is achieved automatically The proposed approach outperforms conventional Interested bands: objective functions 698–960 and 1710–2170 MHz Limitation The |S11| for the lower band is only < –4.3 dB Incorporating a multi-resonance structure for the lower band into the discretization might help 52 of 54
  53. 53. Summary of Today’s Presentation Significance Implementation Performance A dual-antenna The new tag structure DOE achieves multiple Isolation > 40 dBstructure for tags optimizes both reception design considerations Continuous reception and signal backscattering within 0.1 × 0.1 λ02 Larger detection range SSES It’s the first reconfigurable A prototyping SSES RCSR > 50 dB reflective surface for system was successfully RCSE > 3.3 dB (normal) adaptive RCS control built at NTU FFD outperforms GA A pixelized The tool can cover It handles multiobjective Various evolutionary design tool 698–960 and 1710– tasks and wide- and multi- algorithms were 2170 MHz within 40 × band antenna designs implemented 15 mm2 automatically Optimization techniques act as the brain of these applications! 53 of 54
  54. 54. Future Work Efficacy enhancement of the pixelized design tool The initialization of the design space A good solution domain should be constructed by: Design space Physics of resonance requirement Meander technique in the available area Wide- and multi-band mechanisms so that the solution domain is very diverse with promising solutions! Dynamic-parameter mechanisms in GAs Parallel processing of functional evaluationsWe attempt to build a robust and practical design tool, replacing the conventional design procedures! 54 of 54

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