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
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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
• Motivation • Previous Research • Research Problems and Solutions • Research Approach • Experiments and Results • Conclusion
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
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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
• Motivation • Previous Research • Research Problems and Solutions • Research Approach • Experiments and Results • Conclusion
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!" #$+ Noj=1
J
∑Fitness Function:
SINR = Signal to Interference and Noise Ratio
Simulated Annealing Block Diagram
20
SINROut tn[ ] =PS,Out tn[ ]
Pj,Out tn[ ] + Noj=1
J
∑Fitness Function:
Hill Climbing Block Diagram
21
SINROut tn[ ] =PS,Out tn[ ]
Pj,Out tn[ ] + Noj=1
J
∑Fitness Function:
Wireless Channel Model
22
Symbol Meaning J Number
jammers
Q Number reflections
N Number antennas
• Array creates single weighted sum of signals and reflections
• Signal directions unknown a priori • Sum of signals and multipath
reflections should not exceed number of antennas in the array
• Array response calculation important for simulation fidelity
N antennas
Antenna Array Response
Array Factor Model of Antenna Arrays P(R,θ,ϕ)
• Models antennas as infinitesimal dipoles • Far-field computation in O(N) time • Ignores antenna mutual coupling and reflections off objects near antennas
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AF θ,φ( ) = aiejβaR⋅di
i=1
N
∑ai = aie
− jψi
ai ∈ℜ and 0 < ai ≤1
ψi ∈ℜ and 0 ≤ψ < 2π
Complex array weights
Element Positions Spherical Unit Vector
Method of Moments (MOM) Model of Antenna Arrays
• Models antennas as combination of small wire segments • Mutual coupling included in calculation of far-field radiation patterns • Ignores reflections off objects near antennas
24 Fields calculated in O(N3) time.
Goal: Given known port excitations, solve integral equations to calculate currents on each wire
Solution: Divide each wire into segments and estimate unknown currents as sum of weighted basis functions
Segmentation on N antennas
I = Z!" #$−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
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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
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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
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• 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
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• 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
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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.
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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!" #$≥1
= NJ
&
'(
)
*+
J=1
S•N!" #$
∑
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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)
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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
• Motivation • Previous Research • Research Problems and Solutions • Research Approach • Experiments and Results • Conclusion
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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
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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)}
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• 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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