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A Complimentary Self-Structuring Antenna for use in a Vehicle Environment J.E. Ross*1, E.J. Rothwell2, and S. Preschutti1

1Preschutti and Associates, 204 East Calder Way, Suite 401, State College, PA

16801, e-mail: stan@preschutti.com

2Department of Electrical and Computer Engineering, Michigan State University,

• E. Lansing, MI 48824, e-mail: rothwell@egr.msu.edu

Introduction A self-structuring antenna (SSA) is capable of changing its structure, and thus its performance as an antenna, in response to changes in its environment [1]. A microprocessor is used to control the electrical connections between components of a skeletal antenna template using information from a sensor to optimize a desired quantity, such as received signal strength or VSWR. Because the number of possible configurations is large (2N for N switches), efficient algorithms such as genetic algorithms or simulated annealing are used to find acceptable switch states in real time. Until recently the template consisted of wire segments interconnected by switches. This template is useful for automotive applications where the template is placed in the rear window of the automobile [2] since a vertically polarized component to the antenna pattern is present. However, this template cannot be placed in the roof of an automobile since no vertical field is produced. To allow for such an application we propose the use of complimentary antenna elements, such as slots, in the antenna template. We investigate the behavior of such a complimentary SSA (CSSA) both in free space and in an automotive environment. Modeling Tools Performance of the CSSA is determined through numerical modeling using the program GA-NEC (www.johnross.com). This is a general-purpose optimizer for the Numerical Electromagnetics Code (NEC) based on a genetic algorithm (GA). GA-NEC encodes any parameter in a NEC input file as an optimization variable. The GA fitness function is constructed using any combination of NEC output variables, such as impedance, gain, or efficiency, and may be computed based on minimization, maximization, or comparison to a desired response. GA parameters such as mutation rate and cross-over probability may be specified by the user. CSSA Template The CSSA template consists of a number of slots cut in a finite-sized ground plane of dimension 1 by 1.4 m, as shown in Figure 1. The size of the ground plane is chosen to fit within the roof rack area of a 1995 Chevrolet Blazer. The ground plane is modeled as a wire mesh, and the slots are formed by removing mesh segments. The feed point, shown as the segment at the center, is modeled using a standard NEC voltage generator. The

• ther segments lying across the slots represent switches that are modeled as lumped

resistances with 0.1 Ω for the ON state and 100k Ω for the OFF state. This model has 2260 wire elements and 41 switches, and thus 241=2.2 trillion possible configurations (some redundant). No attempt was made to simulate DC power and switch control lines.

Results for Template in Free Space GA-NEC was configured to search for switch configurations that would yield minimum VSWR for a 50 Ω feed system. Using a population size of 20, the GA was allowed to run until a good VSWR was achieved. If a good VSWR was not reached, the run was terminated after 100 generations. The resulting plot of VSWR versus frequency is shown in Figure 2. It is seen that the CSSA is capable of a wide tunable bandwidth with a VSWR of less than 2:1 across the band 40 MHz to 1000 MHz. This performance is consistent with the theoretical and measured results seen previously for the SSA [3]. The VSWR below 40 MHz was poor as would be expected for an electrically small structure. Interaction with structures such as DC power lines, control lines and a vehicle body will tend to extend the lowest usable frequencies below that observed in the free-space

• analysis. The upper frequency limit of 1000 MHz was set by the grid size employed in

the NEC model. Increasing frequency above this limit would require re-gridding the model and modification of the template to include additional detail in the region near the feed point. Results for Template above a Vehicle A 1995 Chevrolet Blazer was meshed with sufficient density to allow analysis up to 300

• MHz. The CSSA template was placed ~76 mm above the top surface of the vehicle, as

shown in Figure 3. There are a total of 10,000 elements in the NEC analysis, severely restricting the number of runs allowed in the GA optimization. Even using the numerical Green’s function option in NEC to compute and store the interaction matrix for the vehicle, a GA run with 20 generations at a population size of 20 took about a week of computer time per frequency point. Nevertheless, minimization of the VSWR was undertaken at several frequencies in the band 30-300 MHz. The minimum VSWR found was 2.1 at 30 MHz, 1.8 at 50 and 100 MHz, 1.2 at 200 MHz and 1.1 at 300 MHz. That these numbers are higher than those found for the CSSA in free space can be attributed to the smaller number of NEC evaluations performed for the CSSA mounted on the vehicle. Figure 3 shows the currents found on the antenna and vehicle at 100 MHz. A good deal

• f coupling to the roof panel and the window pillars is seen. Figure 4 shows the vertical

gain pattern of the CSSA at 50 MHz. The two lobes in the horizontal plane are off the front and back of the vehicle. A greater amount of lobing is found at higher frequencies Conclusions We have demonstrated the feasibility of a complimentary SSA that uses slots as the radiating elements. Good values of VSWR were found over a 25:1 frequency band for the CSSA in free space and over a 10:1 frequency band for the CSSA mounted on a vehicle. References [1]

C.M. Coleman, E.J. Rothwell, J.E. Ross, and L.L. Nagy, “Self-Structuring Antennas,” IEEE Antennas and Propagation Magazine, vol. 44, no. 3, pp.11-22, June 2002.

[2]

B.T. Perry, E.J. Rothwell, L.C. Kempel, J.E. Ross, and L.L. Nagy, “Simulation of a FM Band Self-Structuring Antenna in an Automobile Environment,” IEEE AP-S International Symposium and URSI Radio Science Meeting, Columbus, Ohio, June 23- 26, 2003. [3]

• C. Coleman, B. Perry, E. Rothwell, L. Kempel, J.E. Ross, and L. Nagy, “A Study of

Simple Self-Structuring Antenna Templates,” IEEE AP-S International Symposium and URSI Radio Science Meeting, San Antonio, TX, June 16-21, 2002.

Figure 1. Mesh model of CSSA template.

VSWR vs Frequency for CSSA in Free Space Environment 1 2 3 4 5 6 7 8 9 10 20 40 50 100 150 200 300 400 500 600 700 800 1000 VSWR (50 Ohms)

Figure 2. VSWR versus frequency for a CSSA template in free space.

Figure 3. Vehicle mesh with CSSA mounted on roof. Current shown at 100 MHz.

Figure 4. Vertical gain pattern at 50 MHz of CSSA mounted on roof of vehicle.

1 June 23, 2004

2004 IEEE AP-S/URSI Symposium

A Complementary Self-Structuring Antenna for Use in a Vehicle Environment

APS Session 85 Evolutionary Optimization Techniques in Applied Electromagnetics Wednesday June 23, 10:40 am

E.J. Rothwell, Michigan State University J.E. Ross, John Ross & Associates, LLC

• S. Preschutti, Preschutti & Associates, Inc.

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Overview

SBIR Program Overview of Self-Structuring Antenna (SSA) Modeling Software Complementary Self-Structuring Antenna (CSSA) CSSA Model in Free Space CSSA Model on Vehicle Conclusions

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Navy SBIR NO3-097 “Ultra-Wideband High-Efficiency Low-Profile Antennas” Strategic Objective

“Apply the SSA concept to provide a low-profile wideband mobile antenna suitable for a wide range of military, civilian and foreign communications equipment.”

Design Goals

Frequency range 20 MHz to 4 GHz Power levels up to 200 Watts Emphasize replacement for vertical antennas on vehicles Emphasize omni-directional patterns in horizontal plane

Status

Phase I – Funded July – Dec 2003 Phase I Option / Phase II – Funding pending

Reduce visual signature!

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SSA Introduction

The Self-Structuring Antenna (SSA) is a new class of adaptive antenna that changes its electrical shape in response to the environment by controlling electrical connections between the components of a skeletal “template.”

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SSA automatically adjustments electrical shape to maintain optimum signal quality

SENSOR SELF- STRUCTURING ANTENNA TEMPLATE antenna feed line MICROPROCESSOR feedback control control lines

. . . .

m

SSA Block Diagram

Receiver/Transmitter with feedback signal for VSWR, S-Meter, BER, etc Array of wires or patches interconnected with electronically controlled switches Uses smart (evolutionary) algorithms to select switch positions which

• ptimize feedback signal

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SSA Templates

• The template is comprised of a large number
• f wire segments or patches interconnected by

controllable switches.

• The template can be highly structured or

random and can be placed on a planar or conformal surface.

• For each configuration, the states of the

switches determine the electrical characteristics of the antenna.

• For a template with n switches, there are 2n

possible configurations.

• An asymmetric topology provides more

diversity and less repeated states than a symmetric topology.

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SSA Search Algorithms

Binary search methods:

Exhaustive search – not practical Random search – easy to implement, but very inefficient Simulated annealing (SA) - good Swarm theory / ant colony optimization (ACO) - better Genetic Algorithm (GA) – best

Cost function can include multiple parameters such as:

VSWR Signal strength Bit error rate Link quality Audio/video quality

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What makes SSA Unique?

The performance of each state of the SSA is generally UNKNOWN ahead of time!

• SSA is a Smart Reconfigurable Antenna
• SSA can jump to known “good” states to reduce latency
• “Good” states can be field or factory programmed using search mode for specific

missions

• If pre-programmed states fail in field, SSA can switch to the binary search

algorithm to locate acceptable states.

• SSA can be configured to continually update the list of known good states to

compensate for damage, aging of the antenna or changes in antenna environment and signal conditions.

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SSA Advantages

SSA automatically adapts to signal and environment to maximize the most important quantity; the signal! One SSA can replace several traditional fixed antennas. SSA continues to work even if partially damaged. SSA simply reconfigures remaining portion for best performance. SSA eliminates large up front design costs and reduces design cycle times.

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Recent SSA Prototypes

• 16 x 22 inch template
• 32 switches for ~ 4 Billion

Possible States

• PC board construction
• Electromechanical relays
• Search algorithm and

PCMCIA control boards on notebook PC

• Interfaces to wide band

scanning radio and TV

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SSA Performance

Prototypes have demonstrated: pattern optimization – reduces dropouts wide tunable bandwidth (50~850 MHz) resistance to damage and switch failures improved reception with analog TV receiver and wideband communications receivers

VSWR vs Frequency

1 2 3 4 5 6 50 150 250 350 450 550 650 750

Frequency (MHz)

VSWR

Standard Log-periodic template Edge type 1 Edge Type 2

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SSA Modeling Tools

• GA-Suite - General purpose GA optimizer

and GUI that interfaces to:

• Berkeley Spice
• LLNL NEC-2 and NEC-4
• Ohio State University BSC 4.x
• AntennaCAD - GUI for NEC-2 and NEC-4
• CAD translation
• Geometry verification
• Simulation control
• OpenGL Graphics for visualization
• Developed by John Ross
• Funded by General Motors, Delphi, John

Ross & Associates Generally use following setup:

• Simple GA with elitist strategy
• Single point crossover
• Population size 50 to 100
• Mutation rate = 0.01
• Crossover rate = 0.06
• Maximum generations = 100
• Smart book keeping – eliminates

multiple evaluation of identical chromosomes

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CSSA Concept Development

Navy SBIR requirements

• Low profile antenna with strong

vertical polarization at horizon

• Omni-directional patterns

Switch in Off position lengthens slot path Switch in On position shortens slot path

Horizontal slot radiators symmetric templates & circuitous paths

Complementary SSA

• Replace wires with slots
• Install switches across slots

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CSSA #1 Geometry and Modeling

Blue = conductor Yellow = switch Red = feed point

• Size = 1.0 m x 1.4 m
• 42 Switches
• Slot width = 0.033 m
• Binary switch model

On = 0.1 Ohms Off = 100k Ohms

• Segment length = 0.033 m
• Max freq ~ 900 MHz
• 2260 total segments
• Control and power lines NOT included!
• Optimized VSWR at each frequency!

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CSSA #1 Free-Space Results

• Tunable VSWR bandwidth 40 - 1000 MHz
• Good efficiency above 100 MHz

VSWR versus Frequency

1 1.5 2 2.5 3 3.5 4 4.5 5 40 50 100 150 200 300 400 500 600 700 800 1000 Frequency (MHz) VSWR (50 Ohms)

Efficiency versus Frequency

10 20 30 40 50 60 70 80 90 100 40 50 100 150 200 300 400 500 600 700 800 1000 Frequency (MHz) Efficiency (%

Upper limit set by grid size Lower limit set by template size

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CSSA #1 on Vehicle

75mm above roof center ~ 10,000 total segments Roof rack sized template Vehicle mesh limits analysis to 300 MHz

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CSSA #1 on Vehicle Results

Optimized VSWR at each frequency Population size of 20, ran GA until good VSWR (~ 2:1) achieved 7 days per frequency on 2.8 GHz Pentium 4 with 2 GB RAM

VSWR versus Frequency

1 1.5 2 2.5 3 3.5 4 4.5 5 30 50 100 200 300 Frequency (MHz) VSW R (50 Ohms)

Efficiency versus Frequency

10 20 30 40 50 60 70 80 90 100 30 50 100 200 300 Frequency (MHz) Efficiency (Percent)

Tunable VSWR bandwidth 30 - 300 MHz Good efficiency above 100 MHz Additional run time would likely improve results!

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CSSA #1 on Vehicle: 50 MHz

J H T V

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CSSA #1 on Vehicle: 100 MHz

J H V T

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CSSA #2 Geometry and Modeling

• Same physical size as

CSSA #1 (1 m x 1.4 m)

• Reduced switch count

(26 switches versus 42)

Blue = conductor Yellow = switch Red = feed point

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CSSA #2 in Free Space

• Tunable VSWR bandwidth 50 – 1000

MHz

• Efficiency not as high as CSSA #1
• Low efficiency at 100 MHz - may

need to do statistical study here?

CSSA #2 in Free Space Environment 10 20 30 40 50 60 70 80 90 100 50 75 100 150 200 250 300 350 400 450 500 600 700 800 900 1000

Frequency (MHz) Efficiency (%)

CSSA #2 in Free Space Environment 1 1.5 2 2.5 3 3.5 4 4.5 5 50 75 100 150 200 250 300 350 400 450 500 600 700 800 900 1000

Frequency (MHz) VSWR (50 Ohms)

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Prototype CSSA Layout

16 x 22 inch circuit board

32 Switches Compatible with MSU PC Based system. Fabrication pending receipt of funding Top layer slots shown in inverse (black)

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Summary

Numerical simulations suggest CSSA may exhibit:

Wide tunable bandwidth Strong vertical polarization from low-profile horizontal structure Good efficiency when when template is of reasonable electrical size Good patterns in horizontal plane CSSA may be viable candidate for replacement of whip antennas

Phase II Work

Identification of switches (e.g. MEMS, FET) Completion of interface and control specifications Computer modeling of alternative geometries Design, construction and testing of prototypes with integrated interface/control electronics

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2004 IEEE AP-S/URSI Symposium

Appendix A

Genetic Algorithms

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Genetic Algorithms

GA’s are based on the principles of genetics and Darwin’s concept of natural selection. Advantages

Relatively efficient Not as fast as gradient methods, but much faster than random or exhaustive searches. Does NOT require derivative information. Tends NOT to get stuck in local minima. Does NOT require initial guesses. Can handle discrete or discontinuous parameters and non-linear constraints. Can find “non-intuitive” solutions.

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Genetic Algorithms

Chromosomes

Contain all information necessary to describe an individual. Composed of DNA in nature or a long binary string in a computer model. Chromosomes are composed of genes for the various characteristics to be

• ptimized.

Chromosomes can be any length depending on the number of parameters to be optimized.

Encoding

Defines the way genes are stored in the chromosome and translated to actual problem parameters.

SSA Example 1101 1001 0010 1001

Switch Configurations

Antenna Synthesis Example 1010 0101 0010 1111

Length Diameter Height Radius

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Genetic Algorithms

Fitness

A single numerical quantity describing how well an individual meets predefined design objectives and constraints. Can be computed based on the outputs of multiple analyses using a weighted sum. Definition of good fitness functions is highly problem dependent.

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Genetic Algorithms

Cross-Over

A method of exchanging genetic material between two parents to produce

• ffspring.

Single Point Cross-over

Parent B: 011011 01110 Parent A: 110110 11110 Cross-Over Point Offspring A: 110110 01110 Offspring B: 011011 11110

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Simple GA

Create Random Population Evaluate Fitness of Individuals Create New Population By Mating and Mutation Desired Fitness Reached? Yes

No

Rank Individuals Select mating pairs Stop

• Population size depends on the problem size.
• Fitter individuals have a higher probability of mating

and passing on their genetic information to subsequent generations.

• Less fit individuals have a non-zero probability of

mating to preserve diversity.

• Mating is simulated by combining the chromosomes
• f two individuals at a randomly chosen crossover

point.

• Mutation is simulated by randomly changing a few

bits in the chromosome of the offspring.

• Provides mechanism for exploring new regions of the

solution space.

• Prevents premature convergence to local minima.
• Evaluate fitness of new generation and repeat

process for a specified number of generations or until a desired fitness level is attained.

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2004 IEEE AP-S/URSI Symposium

Appendix B

Backup Slides

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Prototype Template Configurations

Standard Edge Switched Type I Log Periodic Edge Switched Type 2 16 x 22 inches 32 switches

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Potential Issues

• Tuning speed
• Semiconductor / MEMS switches and hardware controllers should dramatically reduce

tuning time.

• Using known states in reconfigurable mode can eliminate searching.
• Power handling
• Switches and transmitter tolerance to bad VSWR states may limit power handling
• May need to do search at reduced power and then switch to full power with good states
• Efficiency
• Switch losses and coupling to power and control lines may reduce efficiency.
• Switches are improving. Control lines can be implemented with fiber optics.
• Power requirements
• May be limitation for mobile applications.
• Cost
• Can be reduced by using standard mass production methods.

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SSA Applications

• Wide band antennas
• SSA exhibits wide tunable impedance bandwidth
• SSA can also provide wide instantaneous bandwidth - more research needed here
• SSA can be made conformal and low profile
• Mobile radio and wireless networking
• SSA can replace several traditional automotive antennas
• SSA could be used to make wearable antennas practical
• SSA could be “plug and play” antenna for computer devices
• Randomly deployed antennas
• SSA could have unique advantages for special forces missions or emergency situations
• High survivability antennas
• SSA can provide optimal signals even when partially damaged
• Big advantage for automotive market due to warranty issues
• Even bigger advantage for military applications

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Technical Summary

SSA is revolutionary antenna concept.

New paradigm for antenna design Reduces NRE costs Reconfigurable for different applications Self-healing Low-profile Wide tunable bandwidth Enabling technology for software defined radios

Applications:

Body worn antennas Vehicular antennas Co-site reduction