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Presentation of D. S. Weile, E. Michielssen, “The control of adaptive

Lance Griffiths’ Ph.D. Qualifying Exam Electrical and Computer Engr. Dept. University of Utah Dec. 5, 2003. Presentation of D. S. Weile, E. Michielssen, “The control of adaptive antenna arrays with genetic algorithms using dominance and diploidy,” IEEE Trans. Antennas and Propagat,,

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Presentation of D. S. Weile, E. Michielssen, “The control of adaptive

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  1. Lance Griffiths’Ph.D. Qualifying ExamElectrical and Computer Engr. Dept.University of UtahDec. 5, 2003

  2. Presentation of D. S. Weile, E. Michielssen, “The control of adaptive antenna arrays with genetic algorithms using dominance and diploidy,” IEEE Trans. Antennas and Propagat,, vol. 49, pp. 1424-1433, 2001 H. Kim, Y.C. Chung, “Passive Optical Network for CDMA-Based Microcellular Communication Systems,” Journal of Lightwave Technology, vol. 12, no. 3, pp. 301-311, 2001 T.W. Hertel, G.S. Smith, “On the Convergence of Common FDTD Feed Models for Antennas,” IEEE Trans. on Antennas and Propagat., vol. 51, no. 8, Aug. 2003

  3. The Control of Adaptive Antenna Arrays With Genetic Algorithms Using Dominance and Diploidy • Introduction • Adaptive Antenna Arrays • Applebaum Criterion • Simple Genetic Algorithms (GA) • Diploid and Dominant GA (D&DGA) • Results from Paper • My Results • Conclusions/ Questions

  4. Introduction Antenna Array: Multiple antennas that are spaced and fed to produce a directional radiation pattern Genetic Algorithm: Optimization technique that simulates natural selection using mating, mutation, and survival of the fittest operators

  5. Adaptive Antenna Arrays In this paper the adaptive array consists of fixed elements with λ/2 spacing. Each elements output is multiplied by a complex weight wi. These elements then go to a voltage summer that combines the outputs.

  6. Applebaum Criterion The Applebaum Criterion is to maximize the Signal to Interference and Noise Ratio (SINR). The noise power (Pn) is defined as being 30 dB below the signal power on each element. The desired power (Pd) is the strength of the signal (defined as 0 dB), times the array gain in the signal direction. The interference power (Pi) is the strength of the sum of the interfering signals (defined at 30 dB) times the array gain in the interferes directions.

  7. Simple Genetic Algorithms (GA) • An ‘individual’ consists of a single haploid string. • Encode parameters of item to optimize using a binary string of 1’s and 0’s. • Mating consists of taking two individuals and exchanging genetic information. • Randomly mutated bits are complemented.

  8. Diploid and Dominant GA (D&DGA) • An ‘individual’ consists of a diploid string. • Encode parameters of item to optimize using a triallelic string of –1’s,1’s, and 0’s. • Mating is more complex using the gametogenesis operator. • Randomly mutated bits now take on one of the three alles.

  9. (D&DGA) continued … The following dominance relation exists, shown in the Punnet Square below. Triallelic crossover using gametogenesis and reproduction

  10. Results from PaperWith following Scenario • 20 element Dolph-Chebychev antenna array • 6 bit phase shifters (using least significant bits of 8 bit phase shifters?) • 5 interferers that alternate every 20 generations located at θ = 36°, 38°, 40°, 42°, and 44° for group 1A and θ = -26°, -28°, -30°, -32°, and -34° for group 1B.

  11. Comparison of D&DGA(solid) and simple GA (dotted) for scenario.

  12. Array Pattern of scenario 1 as given in paper

  13. My Results • 20 element Dolph-Chebychev antenna array • 6 bit phase shifters with freedom +-30° • 5 interferers that alternate every 20 generations located at θ = -36°, -38°, -40°, • -42°, and -44° for group 1A and θ = 26°, 28°, 30°, 32°, and 34° for group 1B.

  14. Scenario 1 with Diploid GA

  15. Array Pattern after 960 Generations

  16. SNR for scenario 1A after 300 generations

  17. Pattern for scenario 1A after 300 generations

  18. SNR for scenario 1B after 300 generations

  19. Pattern for scenario 1B after 300 generations

  20. Conclusions/ Questions • The D&DGA remembers good solutions. • The D&DGA has a better SINR that the simple GA with little extra overhead. • The solutions are far from converging in the changing scenario. • How would the scenario adapt when the interferers are changing randomly?

  21. Passive Optical Network for CDMA-Based Microcellular Communication Systems • Introduction • Requirements • Signal, Noise, and Distortion Sources • System Setup (Star Network) • Results from Paper • My Results • Conclusions/ Questions

  22. Introduction • A passive optical network (PON) converts RF signals to optical signals and back again. • Microcell technology uses a PON to transmit data from several remote base stations (RBSs) to a central base station (CBS). • Current microcell PONs use high cost distributed feedback lasers (DFBs) and Fabry Perot (FP) lasers. • CDMA systems have lower dynamic range requirements than traditional systems. This paper explores the possibility of using lower cost LED transmitters in CDMA systems.

  23. System Requirements • Measurements at a commercial PCS base station showed that received power varied by 15 dB less than 99% of the time under a load of 16 Erlangs. By increasing the load to 30 Erlangs, 18 dB of dynamic range is required. A dynamic range requirement of 28 dB is needed after a 10 dB margin is added. To conform with other publications, this paper used a standard of 30 dB.

  24. Signal, Noise, and Distortion Sources Signal Source The signal power is equal to ( m I0)2 / 2 where m = optical modulation index (OMI) I0 = photocurrent from the signal source

  25. Noise Sources Shot Noise is due to quantum nature of light. Nsh = 2*q*B*M*I0 Receiver Noise is due to thermal energy. NRX = B*i2RX Relative Intensity Noise (RIN) is due to spontaneous emissions in the laser source. NRIN = RIN*B*M* I02 where B = Noise Bandwidth M = Number of transmitters

  26. Distortion Terms Composite Triple Beat (Compression) CDRCTB = IP32 / ( 4 * NIM3* Pin2) NIM3 = # of third order intermodulation tones Pin = input RF power Clipping CDRcl = sqrt(2π)*(1+6μ2)*e(1/(2*μ2)) /μ3 μ = m*sqrt(M/2)

  27. Optical Beat Interference (OBI) OBI = I02 *B*(Nint)*ROBINint ROBIN = (4/π) * Δ VF /(ΔVF2 + δvij2) ΔVF = linewidth of source (in MHz) δvij = separation between sources (in MHz) For LEDs ( all at same wavelength) ROBIN = (4/ π) /ΔVF Nint = M*(M-1)

  28. System Setup (Star Network) Uplink Downlink

  29. Results from Paper CNR vs. OMI for the downstream. The CBS transmits 32 CDMA signals to eight RBSs. Results from my calculations using DFB transmitter.

  30. Upstream Results CNR vs OMI for the upstream. Each RBS transmits eight CDMA signals to accommodate diversity antennas. My results using their simulation setup for the LED source.

  31. My results vs. paper using 4 carriers per channel as in previous slide.

  32. Conclusions/ Questions High capacity CDMA microcell systems can be built using less expensive LED sources Measurements closely match calculations when using a good model Additional network configurations (double star, ring) not presented here are possible to reduce cost or increase reliability.

  33. On the Convergence of Common FDTD Feed Models for Antennas • Introduction • Hard Source Feed • Virtual Line Feed • Simple Feeding Scheme • Improved Feeding Scheme • Difference between Schemes • Effect of neglecting displacement current • Conclusions/ Questions

  34. Introduction • The finite difference time domain method (FDTD) falls under the category of computational electromagnetics. • The object to be simulated is implemented numerically in two rectangular meshes, electric and magnetic. The two grids are offset by half a step. • The simulator then steps through time solving Maxwell’s Equations numerically to compute the electric and magnetic fields at each location (cell) in the grid.

  35. Modeling of a physical feed can be difficult and several models have been developed including: • Gap Feed, (b)Frill Feed, (c) Monopole Transmission Line Feed, (d) Infinitesimal Gap Feed, (e) TEM Feed, (f) Plane Wave Excitation, (g) Microstrip feed, • This paper models two additional feeds, the hard source feed, and the virtual transmission line feed. The convergence of each feed is tested as the cell size is reduced.

  36. Voltage Calculation Voltage Source Hard Source Feed E-field in discreet space Amperes Law to calculate current

  37. Voltage Source Virtual TransmissionLine Feed

  38. Simple Feeding Scheme Basic Feeding Scheme (left) Input Admittance Hard Source (upper right) Input Admittance Virtual Transmission Line feed (lower right)

  39. Improved Feeding Scheme Improved Feeding Scheme (left) Input Admittance Hard Source (upper right) Input Admittance Virtual Transmission Line feed (lower right)

  40. Frequency and Conductance of Hard Source for different discretization levels. Difference Between Schemes Basic Scheme Improved Scheme

  41. Effect of neglecting displacement current Simple feed model using very fine discretization Simple feed model using very fine discretization with neglected capacitance added to hard source model.

  42. Conclusions/ Questions • The Numerical Models converge when spacing is kept constant, and numerical data is taken from a ‘fixed location’. • This paper didn’t address the accuracy of the models, only the convergence. • Due to the method for calculating currents, only wire antennas can be analyzed using these methods.

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