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Design of Interference-Aware Communication Systems
WNCG “Dallas or Bust” Roadtrip
Wireless Networking and Communications
Group
24 Mar 2011
Prof. Brian L. Evans
Cockrell School of Engineering
Completed Projects – Prof. Evans
System Contribution SW release Prototype Companies
ADSL equalization MATLAB DSP/C Freescale, TI
MIMO testbed LabVIEW LabVIEW/PXI Oil & Gas
Wimax/LTE resource allocation LabVIEW DSP/C Freescale, TI
Camera image acquisition MATLAB DSP/C Intel, Ricoh
Display image halftoning MATLAB C HP, Xerox
video halftoning MATLAB Qualcomm
CAD tools fixed point conv. MATLAB FPGA Intel, NI
DSP Digital Signal Processor LTE Long-Term Evolution (cellular)
MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation
2
17 PhD and 8 MS alumni
On-Going Projects – Prof. Evans
System Contributions SW release Prototype Companies
Powerline Comm.
noise reduction; testbed
LabVIEW LabVIEW and C/C++ in PXI
Freescale, IBM, SRC, TI
Wimax/WiFi RFI mitigation MATLAB LabVIEW/PXI Intel
RF Test noise reduction LabVIEW LabVIEW/PXI NI
Underwater Comm.
MIMO testbed;space-time meth.
MATLAB Lake Travis testbed
Navy
CAD Tools dist. computing. Linux/C++ Navy sonar Navy, NI
DSP Digital Signal Processor PXI PCI Extensions for Instrumentation
MIMO Multi-Input Multi-Output RFI Radio Frequency Interference
3
8 PhD and 4 MS students
Radio Frequency Interference (RFI)
Wireless Networking and Communications
Group
4
WirelessCommunication Sources
• Closely located sources• Coexisting protocols
Non-Communication SourcesElectromagnetic radiation
Computational Platform• Clock circuitry• Power amplifiers• Co-located transceivers
antenna
baseband processor
(Wi-Fi)
(Wimax Basestation)
(Wimax Mobile)
(Bluetooth)
(Microwave) (Wi-Fi) (Wimax)
RFI Modeling & Mitigation
Problem: RFI degrades communication performance
Approach: Statistical modeling of RFI as impulsive noise
Solution: Receiver design
Listen to environment
Build statistical model
Use model to mitigate RFI
Goal: Improve communication
10-100x reduction in bit error rate (done)
10x improvement in network throughput (on-going)
Wireless Networking and Communications
Group
5
Project began January 2007
RFI Modeling
Wireless Networking and Communications
Group
6
• Sensor networks• Ad hoc networks
• Dense Wi-Fi networks
• Cluster of hotspots (e.g. marketplace)
• In-cell and out-of-cell femtocell users
• Out-of-cell femtocell users
• Cellular networks• Hotspots (e.g. café)
Symmetric Alpha Stable
Ad hoc and cellular networks
•Single antenna•Instantaneous statistics
Femtocell networks
•Single antenna•Instantaneous statistics
Gaussian Mixture Model
RFI Mitigation
Communication performance
Wireless Networking and Communications
Group
7
Pulse Shaping
Pre-filteringMatched
FilterDetection
Rule
Interference + Thermal noise
-10 -5 0 5 10 15 20
10-3
10-2
10-1
SNR [in dB]
Vecto
r S
ym
bol E
rror
Rate
Optimal ML Receiver (for Gaussian noise)
Optimal ML Receiver (for Middleton Class A)
Sub-Optimal ML Receiver (Four-Piece)
Sub-Optimal ML Receiver (Two-Piece)
-40 -35 -30 -25 -20 -15 -10 -5
10-3
10-2
10-1
100
Signal to Noise Ratio (SNR) [in dB]
Sym
bol E
rror
Rate
Correlation Receiver
Bayesian Detection
Myriad Pre-filtering
Single carrier, single antenna (SISO) Single carrier, two antenna (2x2 MIMO)
~ 20 dB~ 8 dB
10 – 100x reduction in bit error rate
RFI Modeling & Mitigation Software
Freely distributable toolbox in MATLAB
Simulation of RFI modeling/mitigation
RFI generation
Measured RFI fitting
Filtering and detection methods
Demos for RFI modeling and mitigation
Example uses
System simulation (e.g. Wimax or powerline communications)
Fit RFI measurements to statistical models
Wireless Networking and Communications
Group
8
Version 1.6 beta Dec. 2010: http://users.ece.utexas.edu/~bevans/projects/rfi/software
Snapshot of a demo
Voltage Levels in Power Grid
Medium-Voltage
Low-Voltage
High-Voltage
Source: Électricité
Réseau Dist. France
(ERDF)
9
“Last mile” powerline communications on low/medium voltage line
Concentrator
Powerline Communications (PLC)
Concentrator controls mediumto subscriber meters Plays role of basestation
Applications Automatic meter reading (right)
Smart energy management
Device-specific billing(plug-in hybrid)
Goal: Improve reliability & rate Mitigate impulsive noise
Multichannel transmission
Source: Powerline Intelligent
Metering Evolution (PRIME)
Alliance Draft v1.3E
1
0
Noise in Powerline Communications
Superposition of five noise sources [Zimmermann, 2000]
Different types of power spectral densities (PSDs)
Colored Background Noise:• PSD decreases with frequency
• Superposition of numerous noise sources
with lower intensity
• Time varying (order of minutes and hours)
Narrowband Noise:• Sinusoidal with modulated amplitudes
• Affects several subbands
• Caused by medium and shortwave
broadcast channels
Periodic Impulsive Noise Asynchronous to Main:• 50-200kHz
• Caused by switching power supplies
• Approximated by narrowbands
Periodic Impulsive Noise Synchronous to Main:• 50-100Hz, Short duration impulses
• PSD decreases with frequency
• Caused by power convertors
Asynchronous Impulsive Noise:• Caused by switching transients
• Arbitrary interarrivals with micro-
millisecond durations
• 50dB above background noise
Broadband Powerline Communications: Network Design
Can be lumped together as
Generalized Background Noise
1
1
Powerline Noise Modeling & Mitigation
Problem: Impulsive noise is primaryimpairment in powerline communications
Approach: Statistical modeling
Solution: Receiver design
Listen to environment
Build statistical model
Use model to mitigate RFI
Goal: Improve communication
10-100x reduction in bit error rate
10x improvement in network throughput
Wireless Networking and Communications
Group
1
2
Preliminary Noise Measurement
13
0 10 20 30 40 50 60 70 80 90
-125
-120
-115
-110
-105
-100
-95
-90
-85
-80
-75
Frequency (kHz)
Pow
er/
frequency (
dB
/Hz)
Power Spectral Density Estimate
Preliminary Noise Measurement
14
0 10 20 30 40 50 60 70 80 90
-125
-120
-115
-110
-105
-100
-95
-90
-85
-80
-75
Frequency (kHz)
Pow
er/
frequency (
dB
/Hz)
Power Spectral Density Estimate
Colored Background
Noise
Preliminary Noise Measurement
15
0 10 20 30 40 50 60 70 80 90
-125
-120
-115
-110
-105
-100
-95
-90
-85
-80
-75
Frequency (kHz)
Pow
er/
frequency (
dB
/Hz)
Power Spectral Density Estimate
Colored Background
Noise
Narrowband Noise
Preliminary Noise Measurement
16
0 10 20 30 40 50 60 70 80 90
-125
-120
-115
-110
-105
-100
-95
-90
-85
-80
-75
Frequency (kHz)
Pow
er/
frequency (
dB
/Hz)
Power Spectral Density Estimate
Colored Background
Noise
Narrowband Noise
Periodic and
Asynchronous Noise
Powerline Communications Testbed
Integrate ideas from multiple standards (e.g. PRIME) Quantify communication performance vs complexity tradeoffs
Extend our existing real-time DSL testbed (deployed in field)
Adaptive signal processing methods Channel modeling, impulsive noise filters & equalizers
Medium access control layer scheduling Effective and adaptive resource allocation
1
7
GUIGUI
Thank you for your attention!1
8
Backup
Designing Interference-Aware Receivers
Wireless Networking and Communications
Group
2
0
RTS / CTS: Request / Clear to send
Guard zone
Example: Dense WiFi Networks
Medium Access Control (MAC) Layer
• Interference sense and avoid• Optimize MAC parameters
(e.g. guard zone size, transmit power)
Physical (PHY) Layer
• Receiver pre-filtering• Receiver detection• Forward error correction
Statistical Modeling of RFI
• Derive analytically• Estimate parameters at receiver
Statistical Models (isotropic, zero centered)
Symmetric Alpha Stable [Furutsu & Ishida, 1961] [Sousa, 1992]
Characteristic function
Gaussian Mixture Model [Sorenson & Alspach, 1971]
Amplitude distribution
Middleton Class A (w/o Gaussian component) [Middleton, 1977]
Wireless Networking and Communications
Group
2
1
Validating Statistical RFI Modeling
Validated for measurements of radiated RFI from laptop
Wireless Networking and Communications
Group
2
2
Smaller KL divergence• Closer match in distribution• Does not imply close match in
tail probabilities
Radiated platform RFI• 25 RFI data sets from Intel• 50,000 samples at 100 MSPS• Laptop activity unknown to us
0 5 10 15 20 250
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Measurement Set
Kullb
ack-L
eib
ler
div
erg
ence
Symmetric Alpha Stable
Middleton Class A
Gaussian Mixture Model
Gaussian
Turbo Codes in Presence of RFI
Wireless Networking and Communications
Group
2
3
Decoder 1Parity 1Systematic Data
Decoder 2
Parity 2
1
-
-
-
-
A-priori Information
Depends on channel statistics
Independent of channel statistics
Gaussian channel:
Middleton Class A channel:
Independentof channel statistics
Extrinsic Information
Leads to a 10dB improvement at BER of 10-5 [Umehara03]
Return
RFI Mitigation Using Error Correction
Wireless Networking and Communications
Group
2
4
Decoder 1Parity 1
Systematic Data
Decoder 2
Interleaver
Parity 2 Interleaver
-
-
-
-
Interleaver
Turbo decoder
Decoding depends on the RFI statistics
10 dB improvement at BER 10-5 can be achieved using accurate RFI statistics [Umehara, 2003]
Return
Extended to include spatial and temporal dependence
Multivariate extensions of
Symmetric Alpha Stable
Gaussian mixture model
Extensions to Statistical RFI Modeling
Wireless Networking and Communications
Group
2
5
Statistical Modeling of RFI
Single AntennaInstantaneous statistics
Spatial Dependence Temporal Dependence
• Multi-antenna receivers• Symbol errors • Burst errors• Coded transmissions• Delays in network
RFI Modeling: Joint Interference Statistics
Throughput performance of ad hoc networks
Wireless Networking and Communications
Group
2
6
Ad hoc networks
Multivariate Symmetric Alpha StableCellular networks
Multivariate Gaussian Mixture Model
2 4 6 8 10 12 14 162
3
4
5
6
7
8
9
10
Expected ON Time of a User (time slots)
Ne
two
rk T
hro
ug
hp
ut (n
orm
alize
d)
[ b
ps/H
z/a
rea
]
With RFI Mitigation
Without RFI Mitigation
Network throughput improved by optimizing distribution of ON Time of users (MAC parameter)
~1.6x
RFI Mitigation: Multi-carrier systems
Proposed Receiver
Iterative Expectation Maximization (EM) based on noise model
Communication Performance
Wireless Networking and Communications
Group
2
7
-10 -5 0 5 10 15 20
10-4
10-3
10-2
10-1
100
Signal to Noise Ratio (SNR) [in dB]
Bit E
rro
r R
ate
OFDM Receiver
Single Carrier
Proposed EM-based Receiver
Simulation Parameters
• BPSK Modulation• Interference Model
2-term Gaussian Mixture Model~ 5 dB
Smart Grids: The Big Picture
Smart car : charge of
electricalvehicleswhile
panels are producing
Long distance
communication :
access to isolated
houses
Real-Time :
Customers profiling
enabling good
predictions in demand
= no need to use an
additional power plant
Anydisturbance due to a
storm : action
canbetakeninmediatelybas
ed on real-time
information
Smart building :
significant cost reduction
on energy bill through
remote monitoring
Demand-side
management : boilers
are activatedduring the
night
whenelectricityisavaila
ble
Micro- production
: better knowledge
of energy produced
to balance the
network
Security
featuresFireisdetect
ed :
relaycanbeswitched
off rapidly
Source: ETSI
2
8
Networks of networks
Networks
Data acq.
AntennasWires
Communication links
Processors
Systems
Compilers
Circuit design
Protocols
Systems of systems
Middleware
Operating systems
Devices
Waveforms
Networks of systems
Applications2
9
Wireless Networking & Comm. Group
17 faculty140 grad students
Collaboration with UT faculty outside of WNCG
Wireless Networking & Comm. Group
A. Gerstlauer
Embedded Sys
G. de Veciana
Networking
S. Vishwanath
Sensor Networks
S. Nettles
Network Design
S. Shakkottai
Network Theory
J. Andrews
Communication
L. Qiu
Network Design
C. Caramanis
Optimization
H. Vikalo
Genomic DSP
A. Bovik
Image/Video
B. Evans
Embedded DSP
T. Humphreys
GPS/Navigation
T. Rappaport
RF IC Design
R. Heath
Comm/DSP
B. Bard
Security
S. Sanghavi
Network Science
A. Tewfik
Biomedical
Communications Networking Applications
3
0
Com
puta
tion
Our Publications
Journal Publications• K. Gulati, B. L. Evans, J. G. Andrews, and K. R. Tinsley, “Statistics of Co-Channel
Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers”, IEEETransactions on Signal Processing, vol. 58, no. 12, Dec. 2010, pp. 6207-6222.
• M. Nassar, K. Gulati, M. R. DeYoung, B. L. Evans and K. R. Tinsley, “Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers”, Journal of SignalProcessing Systems, Mar. 2009, invited paper.
Conference Publications• M. Nassar, X. E. Lin, and B. L. Evans, “Stochastic Modeling of Microwave Oven
Interference in WLANs”, Proc. IEEE Int. Conf. on Comm., Jun. 5-9, 2011.• K. Gulati, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel
Interference in a Field of Poisson Distributed Interferers”, Proc. IEEE Int. Conf. onAcoustics, Speech, and Signal Proc., Mar. 14-19, 2010.
• K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-ChannelInterference”, Proc. IEEE Int. Global Comm. Conf., Nov. 30-Dec. 4, 2009.
Cont…
3
1
Wireless Networking and Communications
Group
Our Publications
Conference Publications (cont…)• A. Chopra, K. Gulati, B. L. Evans, K. R. Tinsley, and C. Sreerama, “Performance Bounds
of MIMO Receivers in the Presence of Radio Frequency Interference”, Proc. IEEE Int.Conf. on Acoustics, Speech, and Signal Proc., Apr. 19-24, 2009.
• K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans, K. R. Tinsley, and X. E. Lin, “MIMOReceiver Design in the Presence of Radio Frequency Interference”, Proc. IEEE Int.Global Communications Conf., Nov. 30-Dec. 4th, 2008.
• M. Nassar, K. Gulati, A. K. Sujeeth, N. Aghasadeghi, B. L. Evans and K. R. Tinsley,“Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers”, Proc.IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 30-Apr. 4, 2008.
3
2
Wireless Networking and Communications
Group
Software Releases• K. Gulati, M. Nassar, A. Chopra, B. Okafor, M. R. DeYoung, N. Aghasadeghi, A. Sujeeth,
and B. L. Evans, "Radio Frequency Interference Modeling and Mitigation Toolbox in MATLAB", version 1.6 beta, Dec. 16, 2010.
References
RFI Modeling1. D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: New
methods and results for Class A and Class B noise models”, IEEE Trans. Info. Theory, vol. 45, no. 4, pp. 1129-1149, May 1999.
2. K. Furutsu and T. Ishida, “On the theory of amplitude distributions of impulsive random noise,” J. Appl. Phys., vol. 32, no. 7, pp. 1206–1221, 1961.
3. J. Ilow and D . Hatzinakos, “Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers”, IEEE transactions on signal processing, vol. 46, no. 6, pp. 1601-1611, 1998.
4. E. S. Sousa, “Performance of a spread spectrum packet radio network link in a Poisson field of interferers,” IEEE Transactions on Information Theory, vol. 38, no. 6, pp. 1743–1754, Nov. 1992.
5. X. Yang and A. Petropulu, “Co-channel interference modeling and analysis in a Poisson field of interferers in wireless communications,” IEEE Transactions on Signal Processing, vol. 51, no. 1, pp. 64–76, Jan. 2003.
6. E. Salbaroli and A. Zanella, “Interference analysis in a Poisson field of nodes of finite area,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 1776–1783, May 2009.
7. M. Z. Win, P. C. Pinto, and L. A. Shepp, “A mathematical theory of network interference and its applications,” Proceedings of the IEEE, vol. 97, no. 2, pp. 205–230, Feb. 2009.
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Wireless Networking and Communications
Group
References
Parameter Estimation1. S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM
[Expectation-Maximization] algorithms”, IEEE Trans. Info. Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991 .
2. G. A. Tsihrintzis and C. L. Nikias, "Fast estimation of the parameters of alpha-stable impulsive interference", IEEE Trans. Signal Proc., vol. 44, Issue 6, pp. 1492-1503, Jun. 1996.
Communication Performance of Wireless Networks1. R. Ganti and M. Haenggi, “Interference and outage in clustered wireless ad hoc networks,” IEEE
Transactions on Information Theory, vol. 55, no. 9, pp. 4067–4086, Sep. 2009.
2. A. Hasan and J. G. Andrews, “The guard zone in wireless ad hoc networks,” IEEE Transactions on Wireless Communications, vol. 4, no. 3, pp. 897–906, Mar. 2007.
3. X. Yang and G. de Veciana, “Inducing multiscale spatial clustering using multistage MAC contention in spread spectrum ad hoc networks,” IEEE/ACM Transactions on Networking, vol. 15, no. 6, pp. 1387–1400, Dec. 2007.
4. S. Weber, X. Yang, J. G. Andrews, and G. de Veciana, “Transmission capacity of wireless ad hoc networks with outage constraints,” IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4091-4102, Dec. 2005.
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Wireless Networking and Communications
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References
Communication Performance of Wireless Networks (cont…)5. S. Weber, J. G. Andrews, and N. Jindal, “Inducing multiscale spatial clustering using multistage MAC
contention in spread spectrum ad hoc networks,” IEEE Transactions on Information Theory, vol. 53, no. 11, pp. 4127-4149, Nov. 2007.
6. J. G. Andrews, S. Weber, M. Kountouris, and M. Haenggi, “Random access transport capacity,” IEEE Transactions On Wireless Communications, Jan. 2010, submitted. [Online]. Available: http://arxiv.org/abs/0909.5119
7. M. Haenggi, “Local delay in static and highly mobile Poisson networks with ALOHA," in Proc. IEEE International Conference on Communications, Cape Town, South Africa, May 2010.
8. F. Baccelli and B. Blaszczyszyn, “A New Phase Transitions for Local Delays in MANETs,” in Proc. of IEEE INFOCOM, San Diego, CA,2010, to appear.
Receiver Design to Mitigate RFI1. A. Spaulding and D. Middleton, “Optimum Reception in an Impulsive Interference Environment-
Part I: Coherent Detection”, IEEE Trans. Comm., vol. 25, no. 9, Sep. 1977
2. J.G. Gonzalez and G.R. Arce, “Optimality of the Myriad Filter in Practical Impulsive-Noise Environments”, IEEE Trans. on Signal Processing, vol 49, no. 2, Feb 2001
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References
Receiver Design to Mitigate RFI (cont…)3. S. Ambike, J. Ilow, and D. Hatzinakos, “Detection for binary transmission in a mixture of Gaussian
noise and impulsive noise modelled as an alpha-stable process,” IEEE Signal Processing Letters, vol. 1, pp. 55–57, Mar. 1994.
4. G. R. Arce, Nonlinear Signal Processing: A Statistical Approach, John Wiley & Sons, 2005.
5. Y. Eldar and A. Yeredor, “Finite-memory denoising in impulsive noise using Gaussian mixture models,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 48, no. 11, pp. 1069-1077, Nov. 2001.
6. J. H. Kotecha and P. M. Djuric, “Gaussian sum particle ltering,” IEEE Transactions on Signal Processing, vol. 51, no. 10, pp. 2602-2612, Oct. 2003.
7. J. Haring and A.J. Han Vick, “Iterative Decoding of Codes Over Complex Numbers for Impulsive Noise Channels”, IEEE Trans. On Info. Theory, vol 49, no. 5, May 2003.
8. Ping Gao and C. Tepedelenlioglu. “Space-time coding over mimo channels with impulsive noise”, IEEE Trans. on Wireless Comm., 6(1):220–229, January 2007.
RFI Measurements and Impact1. J. Shi, A. Bettner, G. Chinn, K. Slattery and X. Dong, "A study of platform EMI from LCD panels –
impact on wireless, root causes and mitigation methods,“ IEEE International Symposium onElectromagnetic Compatibility, vol.3, no., pp. 626-631, 14-18 Aug. 2006
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