Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery

Dynamic Beamforming Optimization for Anti-Jamming and Hardware Fault Recovery

Jonathan Becker Ph.D. Candidate, Electrical and Computer Engineering

Carnegie Mellon University

Thesis Advisor: Prof. Jason Lohn Thesis Committee: Prof. Ole Mengshoel, Prof. Patrick Tague,

Dr. Derek Linden (CTO, X5 Systems, Inc.)

About Me

Jonathan Becker 15 years of research & industry experience in machine learning, stochastic optimization, antenna design, RFID wireless sensing, and RF / microwave engineering design.

8 papers in the related fields.

Carnegie Mellon University / Ph.D. (2009-2014) Advisor: Prof. Jason Lohn University of Southern California / MSEE 2004 Cal Poly San Luis Obispo / BSEE with CS Minor 1999 Work Experience Disney / Wireless Displacement Sensing (2012-2013) EDO / Interference Cancellation Systems (2001-2006) Teradyne / High-bandwidth IC tester interfaces (1999-2001)

2

Main Goal

The main goal of this research is to develop foundational models of and stochastic algorithms for anti-jamming beamforming in the presence of static and mobile signals and hardware faults.

3

Dynamic Beamforming Optimization With Fault Recovery: Motivation

Jammer Desired

Jammer

Wireless Comm. Blocked

Array Failure

BF Fault

X X

Anti-Jamming Beamforming

4

HW Fault Recovery

via Alg. BF Fault

No HW Redundancy

Limited Spectrum

BF

Volume Constrained

X X

Fault Tolerance Importance in Anti-Jamming Beamforming

5 [1] H.H. Khatib, “Theater wideband communications,” IEEE MILCOM 97 Proceedings, pp. 378-382, 2-5 Nov. 1997.

•  Failure to anti-jam can cause a ripple effect down the communication path. •  Reconfiguration of array weights during recovery provides anti-jamming

beamforming by definition Array Failure

Outline

•  Motivation •  Previous Research •  Research Problems and Solutions •  Research Approach •  Experiments and Results •  Conclusion

6

Previous Research

7 [1] D. Linden. “Optimizing signal strength in-situ using an evolvable antenna system,” NASA/DOD EH Conf., 2002.

Reed Relay Switch

Feed Point

RF Traces

GA optimized mainbeam gain by turning switches on and off

Previous Beamforming Research •  Haupt used 128 antennas to null one jammer •  Array tuned to signal of interest prior

8

Haupt’s antenna array One bank of attenuators and phase shifters

7 Degrees of Freedom. [1] R. Haupt and H. Southall, “Experimental adaptive nulling with a genetic

algorithm,” Microwave Journal, vol. 42, no. 1, pp. 78–89, 1999.

Previous Fault Tolerance Research

9 [1] Lee et. al., “A built-in performance-monitoring/fault isolation and correction (PM/FIC) system for active phased arrays, IEEE Transactions on Antennas and Propagation, Nov. 1993.

•  8 X 10 antenna active array for radar (mainbeam scanning) •  Injection of external signal for fault detection •  Complex circuitry needed to detect faults & re-tune array

Transmission Line Injection

Control Circuitry

Previous Fault Tolerance Research •  Han et. al. showed GA’s ability to resynthesize

beam pattern after transmit/receive module failed

10 J. H. Han, S. H. Lim, and N. H. Myung, “Array antenna TRM failure compensation using adaptively weighted bean pattern mask based on genetic algorithm,” IEEE Antennas and Wireless Propagation Letters, 2012.

GA reconfigured weights using pattern mask based fitness function

AF = Array Factor

Previous Fault Detection Research •  Oliveri et. al. developed a fault detection approach

based on Bayesian Compressive Sensing

11 Oliveri et. al., “Reliable Diagnosis of Large Linear Arrays – A Bayesian Compressive Sensing Approach,” IEEE Transactions on Antennas and Propagation, October 2012.

f = argmaxf

P f F( )!"

#$

“Difference” field pattern P f F( ) =

P F f( )P f( )P F( )

Solve using Bayes Theorem

Sparse “failure” vector

Outline


12

Research Problems in Anti-Jamming Beamforming

Research problems: 1.  Signal directions often time-varying

and unknown a priori, so canonical beamforming techniques used with Radar arrays not applicable.

2.  How to search a large, combinatorial parameter space with multimodal fitness landscape?

3.  How well do stochastic algorithms adapt to mobile signals?

•  Available frequency spectrum is a scarce resource. •  Increased interference will occur as the wireless spectrum saturates. •  Antenna arrays used to focus electromagnetic energy on a desired

signal of interest & minimize energy towards interfering signals

Goal: Perform anti-jamming in presence of static & mobile signals

13

BF

X X

Anti-Jamming Beamforming

Fault Recovery Research Problems

Research problems: 1.  Recovery from hardware failures

and localized faults in the array 2.  How do stochastic algorithms treat

hardware faults vs. mobile signals?

3.  What happens if a hardware component fails before algorithmic convergence? After convergence?

•  Hardware redundancy addresses antenna array reliability at expense of more volume, mass, cost.

•  Volume, mass, and cost constraints create lack of hardware redundancy. •  Faulted hardware components cause loss of anti-jamming functionality.

Goal: Perform HW fault recovery with stochastic algorithms

Fault Recovery

BF

X X

Fault

14

Anti-Jamming Beamforming Arrays

15

Shape radiation pattern using multiple antennas and hardware

amplitude / phase weights

Null shifted to 30°

Stochastic Search Algorithms

16

Approach Features Drawbacks Least Mean

Squares Adaptive feedback Local search with poor multi-modal performance

Conjugate Gradient Method

Searches parameter space using conjugate directions

Signal directions needed, poor multi-modal performance, O(N2)

Genetic Algorithms

Population based global search

Run-time is problem dependent

Simulated Annealing

Evaluates solutions sequentially

Convergence is cooling schedule dependent

Outline


17

Triallelic Diploid Genetic Algorithm

18

SINROut tn,p!" #$=PS,Out tn,p!" #$

Pj,Out tn,p!" #$+ Noj=1

J

∑Fitness Function:

SINR = Signal to Interference and Noise Ratio

Simple Genetic Algorithm

19

SINROut tn,p!" #$=PS,Out tn,p!" #$

Pj,Out tn,p!" #$+ Noj=1

J


SINR = Signal to Interference and Noise Ratio

Simulated Annealing Block Diagram

20

SINROut tn[ ] =PS,Out tn[ ]

Pj,Out tn[ ] + Noj=1

J


Hill Climbing Block Diagram

21

SINROut tn[ ] =PS,Out tn[ ]

Pj,Out tn[ ] + Noj=1

J


Wireless Channel Model

22

Symbol Meaning J Number

jammers

Q Number reflections

N Number antennas

•  Array creates single weighted sum of signals and reflections

•  Signal directions unknown a priori •  Sum of signals and multipath

reflections should not exceed number of antennas in the array

•  Array response calculation important for simulation fidelity

N antennas

Antenna Array Response

Array Factor Model of Antenna Arrays P(R,θ,ϕ)

•  Models antennas as infinitesimal dipoles •  Far-field computation in O(N) time •  Ignores antenna mutual coupling and reflections off objects near antennas

23

AF θ,φ( ) = aiejβaR⋅di

i=1

N

∑ai = aie

− jψi

ai ∈ℜ and 0 < ai ≤1

ψi ∈ℜ and 0 ≤ψ < 2π

Complex array weights

Element Positions Spherical Unit Vector

Method of Moments (MOM) Model of Antenna Arrays

•  Models antennas as combination of small wire segments •  Mutual coupling included in calculation of far-field radiation patterns •  Ignores reflections off objects near antennas

24 Fields calculated in O(N3) time.

Goal: Given known port excitations, solve integral equations to calculate currents on each wire

Solution: Divide each wire into segments and estimate unknown currents as sum of weighted basis functions

Segmentation on N antennas

I = Z!" #$−1V

Result: Ultimately obtain vector equations of form

Post-processing: Calculate far-fields

D. B. Davidson, Computational Electromagnetics for RF and Microwave Engineering, 2nd ed. New York, NY: Cambridge University Press, 2011

Matrix Inversion

Antenna Arrays with Nearby Objects

•  Physical arrays include metallic objects near antennas •  Incorporate reflections into MOM by including metallic objects in model •  Model objects as Perfect Electric Conducting (PEC) planes

25 Fields calculated in O(N3) time.

Optimal Array Weights with Mutual Coupling Compensation

26

MC−1 aM ,opt =MC

−1aAF,optaAF,opt

Inverse of Coupling Matrix

MC found using MOM compared to array factor calculation

Optimized weights using Array Factor

calculations

Coupling compensated optimized weights

[1] T. Zhang and W. Ser, “Robust beampattern synthesis for antenna arrays with mutual coupling effect,” IEEE Transactions on Antennas and Propagation, vol. 59, no. 8, pp. 2889–2895, 2011

[2] P. J. Bevelacqua, “Antenna arrays: Performance limits and geometry optimization,” Ph.D. dissertation, Arizona State University, May 2008.

[3] M. Joler, “Self-recoverable antenna arrays,” IET Microwaves Antennas Propagation, vol. 6, no. 14, pp. 1608–1615, 2012.

Optimal Array Weights with Mutual Coupling and Hardware Reflection Compensation

27

MCR−1 aMR,opt =MCR

−1 aAF,optaAF,opt

Inverse of Coupling Matrix

New Method: Hardware reflections compensation not discussed in literature

Optimized weights using Array Factor

Coupling + Reflection compensated optimized

weights

MR−1 aMR,opt =MR

−1aM ,optaM ,opt

Inverse of Reflection Matrix

Coupling + Reflection compensated optimized

weights MCR =MCMR

MOM output

Equivalence of Stochastic Algorithms with Different Antenna Array Models

28

Need to calculate an inverse matrix for each transformation

Most Reliable but O(N3) Least Reliable but O(N)

Solution: Calculate in O(N) time aMR

WIPL-D / AntNet Integration

29

Array Layout Input File

WIPL-D AntNet

VSA lg ∝ aA lg ∈CN×1

Beamformed Fields

[1] D.S. Weile and D.S. Linden, “AntNet: A fast network analysis add-on for WIPL-D, 27th International Review of Progress in Applied Computational Electromagnetics, March 2011

VSnom = 1 ∈ℜN×1

O(N3) O(N)

Simulate Once Run Multiple Times

(Saved in file)

Chosen by Algorithm

Nominal Port Far Fields &

S/Y/Z matrices

HFSS and MOM Models of Array

30

•  HFSS = High Frequency Structure Simulator •  HFSS is based on the Finite Element Method (FEM)

•  Divides structure into small tetrahedra with boundary conditions •  MOM: divides wires into small segments, planes into small triangles

FEM (HFSS) MOM (WIPL-D)

Comparison of HFSS and WIPL-D to in-Situ Measurements

31

•  Good agreement between simulations and in-situ measurements

•  Good agreement in jammer directions •  Extra nulls via nonlinear hardware

effects not captured by HFSS & MOM

HFSS results similar to WIPL results in both cases

Diagnosis Model for Hardware Fault Detection

32

Problem: Events overlap making it insufficient to diagnose what caused the algorithm to fail in anti-jamming by tracking the fitness function alone. Solution: Add array weight tracking to understand why the algorithm failed.

Diagnosis Model for Hardware Fault Detection

•  H0: Algorithm converged: No Faults, no TVDOAs. •  H1: Algorithm unconverged: No Faults, no TVDOAs. •  H2: Algorithm converged: Faults and/or TVDOAs present. •  H3: Algorithm unconverged: Faults and/or TVDOAs present.

33

Good states

Not good states

Diagnosis not necessary

Diagnosis not possible

∂µa

∂tn( ) > 0and ∂µF

∂tn( ) > 0→HW Fault ∂µa

∂tn( ) ≤ 0and ∂µF

∂tn( ) > 0→TVDOA

Assumes fading averaged out

Antenna Fault Localization: Array Factor Method

AF θ,φ k( ) = aiejβaR⋅di

i=1

k−1

∑ + aiejβaR⋅di

i=k+1

N

∑

ai = aie− jψi

ai ∈ℜ and 0 < ai ≤1

ψi ∈ℜ and 0 ≤ψ < 2π

Probability that an antenna fault occurred in branch k:

PFault k Failure( ) = 1ξmax xcorr ARF θ,φ k( ),ARM θ,φ( )!" #$

ARF θ,φ k( )=EF θ,φ( ) ⋅AF θ,φ k( )

Note :ξ = normalizing factor s. t. 0 ≤ PFault k Failure( ) ≤1 34

Complex array weights

Array Radiated Fields

Element Factor

Array Factor

K = argmaxk

PFault k Failure( ){ }Assuming 1 fault, most likely fault branch:

Antenna Fault Localization with Array Factor Multiple antenna fault detection possible by counting number of faults with more calculations due to possible combinations:

PFault k Failure( ) = 1ξmax xcorr ARF θ,φ k( ),ARM θ,φ( )!" #$

Note :ξ = normalizing factor s. t. 0 ≤ PFault k Failure( ) ≤1

Single fault:

K faults: PFault k1,,kK[ ] Failure( ) = 1ξmax xcorr ARF θ,φ k1,,kK[ ]( ),ARM θ,φ( )!

"#$

Total AF Correlations = NJ

!

"#

$

%&

J=1

N−1

∑

# Antennas K Total AF Correlations Total AF Corr., 10% Sparsity 4 14 4 8 254 8

16 65534 136 32 > 4 trillion 41448

Total AF Correlationswith Sparsity S s. t. S •N!" #$≥1

= NJ

&

'(

)

*+

J=1

S•N!" #$

∑

35

Antenna Fault Localization: Improvements Pros:

•  O (K) for single fault using array factor (AF) •  Useful for small arrays

Cons: •  Correlation fidelity questionable since AF neglects mutual coupling

–  Higher fidelity requires MOM or FEM at O(N3) cost

•  Less useful for modeling damaged components (i.e, stuck-at faults)

36

Solution: Replace AF calculations with AntNet post-processed MOM calculations

[1] D.S. Weile and D.S. Linden, “AntNet: A fast network analysis add-on for WIPL-D,” in the 27th International Review of Progress in Applied Computational Electromagnetics, March 2011

V = Z Z + Zo( )−1VS

Eψ θ,φ k( ) = Vii=1

k−1

∑ Eψi θ,φ( )+ ViEψ

i θ,φ( )i=k+1

N

∑ , ψ ∈ θ,φ{ }

PFault k Failure( ) = 1ξmax xcorr Eψ θ,φ k( ),ARM θ,φ( )!" #$

Outline


37

Experiment Setup

38

Port 1 Port 2 VNA

VNA = Vector Network Analyzer

SOI Jammers

SOI = Signal of Interest

SOI Jam 1 Jam 2 Jam 3

Adaptive Beamforming Array

39

Goal: Show that stochastic algorithms can perform anti-jamming beamforming in the presence of static or mobile

signals and hardware faults

Step Attenuators Phase Shifters

Antennas

Hardware Controllers

Power Combiner

Adaptive Beamforming Array in Anechoic Chamber

40 Anechoic chamber approximates free-space conditions

(at far end of chamber)

Array Diagram and Hardware Settings

41

Att5 (8 dB) Att4 (4 dB) Att3 (2 dB) Att2 (1 dB) Att1 (½ dB)

0 1 0 0 1

Ph5 Ph4 Ph3 Ph2 Ph1

1 1 0 0 1

A21 A31 A41 P2 P3 P4

5 BITS 5 BITS 5 BITS 5 BITS 5 BITS 5 BITS

BIT 30 BIT 15 BIT 1

Example: 4.5 dB Attenuation out of 15.5 dB max

Range: 0 to 360° 11.6° / bit Example: 151°

A22 = 0 dBA32 = 0 dBA42 = 0 dB

230 Combinations

Multimodal SINR Fitness Landscape

42

Collected from 30 independent in-situ SGA runs with two jammers

Multimodal behavior clear with several peaks having SINR ≥ 30 dB

Table of Simulations and Experiments Performed

Anti-Jamming Fault Recovery (Anti-Jamming) Static Mobile Static Mobile

Algorithm 2 jam 3 jam 2 jam 2 jam 3 jam 2 jam SGA S, E S, E S S S S TDGA S, E S S,E S, E S, E S, E SA S, E S, E S S S S HC S S S S S S

43

SGA = Simple Genetic Algorithm TDGA = Triallelic Diploid GA SA = Simulated Annealing HC = Hill Climbing S = Simulation E = In-situ experiments

First Second Third

Simulated Anti-Jamming with SGA and TDGA: Two Static Jammers

44

SGA TDGA Pe

rfor

man

ce

Ham

min

g

•  TDGA produced better converged SINR values than SGA •  TDGA mean-Hamming distance decayed slower than SGA •  Mean SINR with 95% confidence interval indicate average convergence by 15

generations for both SGA and TDGA

In-Situ Anti-Jamming with SGA & TDGA: Two Static Jammers

45

SGA TDGA Pe

rfor

man

ce

Ham

min

g

•  In-Situ TDGA produced better minimum converged SINR values than SGA •  Difference between min/max SINR smaller for TDGA than SGA •  Simulated SINR values were conservative compared to in-situ results.

SGA and TDGA Radiation Patterns, Simulations and In-Situ Compared: Two Static Jammers

46

SGA

TDG

A

Simulation In-Situ

Simulation and in-situ radiation patterns are similar at convergence

Simulated Anti-Jamming with SA & HCA: Two Static Jammers

47

SA HCA Pe

rfor

man

ce

Rad

iatio

n

•  SA and HCA obtain similar converged azimuth radiation plots •  Both SA and HCA by chance find ~20 dB SINR solutions early but on

average converge much slower than GAs per 95% confidence intervals

In-Situ Anti-Jamming with SA: Two Static Jammers

48

Perf

orm

ance

R

adia

tion

•  Average convergence time agrees with simulations. •  Final in-situ SINR values higher than SINR predicted by simulation

Anti-Jamming Two Static Jammers: Algorithm Comparison

Best Case SINR (dB)

In-Situ {Sim}

Worst Case SINR (dB)

In-Situ {Sim}

95% Conf. Interval (dB) Gauss (Student-t)

In-Situ {Sim}

Average Converge Time (# Gen / Eval) In-Situ {Sim}

SGA 67.8 {27.7}

28.3 {20.7}

3.3 (3.4) {0.65 (0.68)}

15 Gen (3000 Eval) {15 Gen (3000 Eval)}

TDGA 55.5 {28.0}

31.1 {22.5}

2.4 (2.5) {0.55 (0.57)}

30 Gen (6200 Eval) {15 Gen (3000 Eval)}

SA 48.2 {26.1)

13.8 {21.1}

2.52 (2.62) {0.51 (0.53)}

7800 Eval (39 Gen) {7140 Eval (~36 Gen)}

HCA {26.2} {18.2} {0.61 (0.63)} {7140 Eval (~36 Gen)}

49

•  Simulations were conservative in predicting final SINR values •  SGA and TDGA converged to higher SINR values than SA and HCA •  In-Situ 95% confidence intervals were higher than predicted by simulations

due to hardware tolerances. •  SA and HCA on average converged slower than SGA and TDGA

SGA and TDGA Two Jammer Fault Recovery Performance and Hamming Distance Plots: Simulations

50

SGA TDGA Pe

rfor

man

ce

Ham

min

g

•  SGA and TDGA simulations predict recovery, but simulations are conservative. •  Mean Hamming distance for TDGA decays slower than SGA

TDGA In-Situ Fault Recovery Performance and Hamming Distance Plots

51

Perf

orm

ance

H

amm

ing

•  TDGA in-situ experiments recovered with higher final values than simulations. •  95% confidence intervals indicate TDGA recovered from a fault

SGA and TDGA Fault Recovery Azimuth Plots, Simulations

52

SGA

TDGA

Similar final radiation patterns with conservative fault-recovery predicted.

TDGA In-Situ Fault Recovery Azimuth Plot

53 [1] J. Becker, J.D. Lohn, and D. Linden, “Towards a self-healing, anti-jamming

adaptive beamforming array,” in 2013 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), September 2013, pp. 1–4.

•  TDGA in-situ pattern showed recovery of anti-jamming function. •  Some SOI gain recovered after the fault. •  Null directed at Jammer 2 (J2) deeper than pre-fault null.

Why TDGA Self Heals: An Example

54

Went from High to Low

Mean Population Fitness Down

Long term genetic memory and +1’s to -1’s dominance allows healing

SA and HCA Fault Recovery Performance and Azimuth Radiation Plots: Simulations

55

SA HCA

Perf

orm

ance

R

adia

tion

•  Temperature schedules repeated 5 times to allow for fault recovery •  Both SA and HCA showed fault recovery with maximum ~20 dB SINR post-fault

Two Static Jammer Fault Recovery: Algorithm Comparison

Best Case SINR (dB) post-Fault In-Situ {Sim}

Worst Case SINR (dB) post-Fault In-Situ {Sim}

95% Conf. Interval (dB)

Gauss (Student-t) In-Situ {Sim}

Average Converge Time (# Gen / Eval)

In-Situ {Sim}

SGA {18.7} {13.4} {0.48 (0.50)} {15 Gen (3000 Eval)}

TDGA 47.0 {18.6}

1.12 {13.6}

4.77 (6.74) {0.57 (0.59)}

30 Gen (6200 Eval) {15 Gen (3000 Eval)}

SA {20.5} {13.9} {0.60 (0.63)} Repeated Cooling Schedules

HCA {20.3} {15.6} {0.43 (0.45) } Repeated Cooling Schedules

56

Fault Condition: two step attenuators in one path set to full values.

•  SGA and TDGA simulations produced similar post-fault SINR values •  SA and HCA simulations produced slightly better SINR results than GA •  Algorithm simulations produced similar 95% confidence intervals but TDGA in-

situ 95% confidence intervals much larger due to hardware tolerances

SGA Tracking Two Jammers from {45°, 200°} to {120°, 300°}

57 •  SGA moves nulls to track the jammers. •  Previous solution sometimes repeated resulting in lower SINR fitness.

TDGA Tracking Two Jammers from {45°, 200°} to {120°, 300°}

58 TDGA behaves in fashion similar to SGA.

SGA and TDGA Two Mobile Jammers Constantly Moving: Simulations

59

SGA TDGA Pe

rfor

man

ce

Ham

min

g

•  SGA and TDGA performance graph follow similar sinusoidal pattern. •  TDGA mean-Hamming distance higher than SGA indicating more diversity in

TDGA populations.

Azimuth Radiation Plots for SGA and TDGA Two Mobile Jammers Constantly Moving: Simulations

60

SGA

TDGA

Both SGA and TDGA track both jammers with second jammer having deeper null.

Stochastic Algorithms Investigated Name Advantages Disadvantages

Simple Genetic Algorithm (SGA)

Able to search parameter space in parallel

Complexity problem dependent, short-term genetic memory

Triallelic Diploid Genetic Algorithm (TDGA)

Able to search parameter space in parallel, long-term genetic memory

Complexity problem dependent, added step to convert TD strings into binary haploid strings

Simulated Annealing (SA)

Temperature dependent mutation allows initial exploration of search space with eventual exploitation of solutions

Convergence time temperature schedule dependent, 2X slower than GAs

Hill Climbing Algorithm (HCA)

Simple to implement, finds solutions comparable to GAs and SA

Tends to get stuck at local optima, 2X slower than GAs

61

Results Discussion

•  Incorporating physical objects into MOM model of array increased model reliability and fidelity compared to in-situ measurements.

•  Need to track both fitness function values and complex weights for a useful diagnostic model to detect faults in non-ideal environments.

•  Hardware faults can be localized by correlating in-situ measurements with MOM calculations to provide most-likely faulty antenna branch.

•  Showed that stochastic algorithms can perform anti-jamming beamforming with hardware fault recovery

–  Simulations gave conservative results in SINR values compared to in-situ measurements

–  Simulated Annealing and Hill Climbing Algorithms slower than GAs at anti-jamming static signals.

•  GAs able to thwart continuously moving jammers

62

Conclusions and Contributions •  New analytical models with experimental results showing that small

antenna arrays can thwart interference sources with unknown positions. •  First time demonstration of in-situ optimization with an algorithm

dynamically optimizing a beamformer to thwart interference sources with unknown positions, inside of an anechoic chamber.

•  First time demonstration of stochastic algorithms that provided recovery from hardware failures and localized faults in the array with reconfiguration of array weights to provide anti-jamming of interference sources having unknown positions.

•  Comparison of multiple stochastic algorithms in performing both anti-jamming and hardware fault recovery.

•  Showed that stochastic algorithms can be used to continuously track and mitigate interfering signals that continuously move in an additive white Gaussian noise wireless channel.

63

Future Work •  Real-time fault recovery and anti-jamming in wireless link •  Wideband 8-antenna array with individual antenna modules

64

PN = Pseudo-random Noise USRP = Universal Software

Radio Protocol

Selected Publications 1.  J. Lohn, J. M. Becker, and D. Linden, “An evolved anti-jamming adaptive beam-forming

network,” Genetic Programming and Evolvable Machines, vol. 12, no. 3, pp. 217–234, 2011. 2.  J. Becker, J. Lohn, and D. Linden, “An anti-jamming beamformer optimized using evolvable

hardware,” in Proc. 2011 IEEE Intl. Conf. on Microwaves, Communications, Antennas, and Electronic Systems, IEEE COMCAS 2011, November 2011, pp. 1–5.

3.  J. Becker, J. D. Lohn, and D. Linden, “An in-situ optimized anti-jamming beamformer for mobile signals,” in 2012 IEEE International Symposium on Antennas and Propagation, IEEE APS 2012, July 2012, pp. 1–2.

4.  J. Becker, J. Lohn, and D. Linden, “Evaluation of genetic algorithms in mitigating wireless interference in situ at 2.4 GHz,” in WiOpt 2013 Indoor and Outdoor Small Cells Workshop, May 2013, pp. 1–8.

5.  J. Becker, J. D. Lohn, and D. Linden, “Algorithm comparison for in-situ beamforming,” in 2013 IEEE Intl. Symp. on Antennas and Propagation, IEEE APS 2013, July 2013, pp. 1–2.

6.  J. Becker, J. D. Lohn, and D. Linden, “Towards a self-healing, anti-jamming adaptive beamforming array,” in 2013 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), September 2013, pp. 1–4.

65

Acknowledgements •  I thank my committee members for their support:

–  Professor Jason Lohn –  Professor Ole Mengshoel –  Professor Patrick Tague –  Dr. Derek Linden, CTO X5 Systems Inc.

•  I would also like to thank these individuals who assisted me over the years: Prof. Martin Griss, Prof. Bob Iannuchi, Prof. Ted Selker, Dr. James Downey, Dr. Reggie Cooper, Prof. Joshua Griffin, Dr. Matthew Trotter, Prof. Joy Zhang, Prof. Pei Zhang, Prof. Emeritus James Hoburg, Prof. James Bain, Dr. Joey Fernandez, Dr. Faisal Luqman, Dr. Heng-Tze Cheng, Dr. Joel Harley, Jon Smereka

•  This research was funded in part by: –  Cylab at Carnegie Mellon University under grant DAAD19-02-1-0389 from the Army Research

Office –  The Electrical and Computer Engineering Department at Carnegie Mellon University

66

Thank you

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