1. Wireless Networking and Communications
Group
Design of Interference-Aware
Communication Systems
Prof. Brian L. Evans
Cockrell School of Engineering
24 Mar 2011 WNCG “Dallas or Bust” Roadtrip
2. Completed Projects – Prof. Evans
2
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
17 PhD and 8 MS alumni
3. On-Going Projects – Prof. Evans
3
System Contributions SW release Prototype Companies
Powerline noise reduction; LabVIEW LabVIEW and Freescale,
Comm. testbed C/C++ in PXI IBM, SRC, TI
Wimax/WiFi RFI mitigation MATLAB LabVIEW/PXI Intel
RF Test noise reduction LabVIEW LabVIEW/PXI NI
Underwater MIMO testbed; MATLAB Lake Travis Navy
Comm. space-time meth. testbed
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
8 PhD and 4 MS students
4. Radio Frequency Interference (RFI)
4
(Wimax Basestation)
(Microwave) (Wi-Fi)
(Wi-Fi) (Wimax)
antenna (Wimax Mobile)
Wireless
Non-Communication Sources Communication Sources
Electromagnetic radiation • Closely located sources
• Coexisting protocols
baseband processor
(Bluetooth)
Computational Platform
• Clock circuitry
• Power amplifiers
• Co-located transceivers
Wireless Networking and Communications
Group
5. RFI Modeling & Mitigation
5
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)
Project began January 2007
Wireless Networking and Communications
Group
6. RFI Modeling
6
Ad hoc and
cellular networks
•Single antenna
•Instantaneous
statistics • Sensor networks • Cellular networks • Dense Wi-Fi networks
• Ad hoc networks • Hotspots (e.g. café)
Femtocell
networks
•Single antenna
•Instantaneous
statistics • In-cell and out-of-cell • Cluster of hotspots • Out-of-cell
femtocell users (e.g. marketplace) femtocell users
Symmetric Alpha
Stable Gaussian Mixture Model
Wireless Networking and Communications
Group
7. RFI Mitigation
7
Interference + Thermal noise
Pulse Matched Detection
Pre-filtering
Shaping Filter Rule
Communication performance
0
10
Correlation Receiver
Bayesian Detection
Myriad Pre-filtering
-1
10
Vector Symbol Error Rate
-1
10
Symbol Error Rate
10 – 100x reduction
in bit error rate ~ 8 dB
-2
~ 20 dB -2
10
10
Optimal ML Receiver (for Gaussian noise)
Optimal ML Receiver (for Middleton Class A)
Sub-Optimal ML Receiver (Four-Piece)
-3 -3
10 10 Sub-Optimal ML Receiver (Two-Piece)
-40 -35 -30 -25 -20 -15 -10 -5 -10 -5 0 5 10 15 20
Signal to Noise Ratio (SNR) [in dB] SNR [in dB]
Single carrier, single antenna (SISO) Single carrier, two antenna (2x2 MIMO)
Wireless Networking and Communications
Group
8. RFI Modeling & Mitigation Software
8
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 Snapshot of a demo
System simulation (e.g. Wimax or powerline communications)
Fit RFI measurements to statistical models
Version 1.6 beta Dec. 2010: http://users.ece.utexas.edu/~bevans/projects/rfi/software
Wireless Networking and Communications
Group
9. Voltage Levels in Power Grid
High-Voltage
Source: Électricité
Réseau Dist. France
(ERDF)
Medium-Voltage
Low-Voltage
Concentrator
“Last mile” powerline communications on low/medium voltage line
9
10. Powerline Communications (PLC)
1
0
Concentrator controls medium
to 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 Source: Powerline Intelligent
Metering Evolution (PRIME)
Multichannel transmission Alliance Draft v1.3E
11. Noise in Powerline Communications
1
1
Superposition of five noise sources [Zimmermann, 2000]
Different types of power spectral densities (PSDs)
Colored Background lumped together asAsynchronous to Main: Main:
Narrowband Impulsive Noise Noise Synchronous to
Can be Noise: Noise:
PeriodicPeriodic Impulsive Asynchronous Impulsive Noise:
• •
•
Generalized• Background Noise •impulses by switching transients
PSD decreases with frequency modulated amplitudes
•
Sinusoidal with 50-100Hz, Short duration
• 50-200kHz
•
Superposition of numerous noisesubbands
Affects several sources •
Caused
• PSD decreases with frequency
Caused by switching power supplies Arbitrary interarrivals with micro-
•
with lower intensity •
Caused by medium by narrowbands
• Approximated and shortwave convertors millisecond durations
Caused by power
• Time varying (order of minutes and hours)
broadcast channels • 50dB above background noise
Broadband Powerline Communications: Network Design
12. Powerline Noise Modeling & Mitigation
1
2
Problem: Impulsive noise is primary
impairment 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
20. Designing Interference-Aware Receivers
2
0
Guard zone
Statistical Modeling of RFI
• Derive analytically
• Estimate parameters at receiver
Physical (PHY) Layer Medium Access Control (MAC) Layer
• Receiver pre-filtering • Interference sense and avoid
• Receiver detection • Optimize MAC parameters
• Forward error correction (e.g. guard zone size, transmit power)
RTS / CTS: Request / Clear to send
Example: Dense WiFi Networks
Wireless Networking and Communications
Group
21. Statistical Models (isotropic, zero centered)
2
1
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
22. Validating Statistical RFI Modeling
2
2
Validated for measurements of radiated RFI from laptop
0.4
Symmetric Alpha Stable
0.35 Middleton Class A Radiated platform RFI
Gaussian Mixture Model • 25 RFI data sets from Intel
Kullback-Leibler divergence
Gaussian
0.3
• 50,000 samples at 100 MSPS
0.25 • Laptop activity unknown to us
0.2
0.15 Smaller KL divergence
0.1
• Closer match in distribution
• Does not imply close match in
0.05 tail probabilities
0
0 5 10 15 20 25
Measurement Set
Wireless Networking and Communications
Group
23. Turbo Codes in Presence of RFI
2
3
Return
-
Parity 1
Systematic Data
Decoder 1
-
Gaussian channel:
- Middleton Class A channel:
Parity 2 Decoder 2
-
1
Extrinsic A-priori
Information Information
Leads to a 10dB improvement at Independent of Depends on Independent
BER of 10-5 [Umehara03] channel channel of channel
statistics statistics statistics
Wireless Networking and Communications
Group
24. RFI Mitigation Using Error Correction
2
4
Return
Turbo decoder
-
Parity 1 Decoder 1 Interleaver
-
Systematic Data
Interleaver
-
Parity 2 Decoder 2 Interleaver
-
Decoding depends on the RFI statistics
10 dB improvement at BER 10-5 can be achieved using
accurate RFI statistics [Umehara, 2003]
Wireless Networking and Communications
Group
25. Extensions to Statistical RFI Modeling
2
5
Extended to include spatial and temporal dependence
Statistical Modeling of RFI
Single Antenna Spatial Dependence Temporal Dependence
Instantaneous statistics
• Symbol errors • Multi-antenna receivers • Burst errors
• Coded transmissions
• Delays in network
Multivariate extensions of
Symmetric Alpha Stable
Gaussian mixture model
Wireless Networking and Communications
Group
26. RFI Modeling: Joint Interference Statistics
2
6
Ad hoc networks Cellular networks
Multivariate Symmetric Alpha Stable Multivariate Gaussian Mixture Model
Throughput performance of ad hoc networks
10
Network Throughput (normalized)
With RFI Mitigation
9
Without RFI Mitigation
8
~1.6x Network throughput improved
[ bps/Hz/area ]
7
by optimizing distribution of
6
ON Time of users (MAC parameter)
5
4
3
2
2 4 6 8 10 12 14 16
Expected ON Time of a User (time slots)
Wireless Networking and Communications
Group
27. RFI Mitigation: Multi-carrier systems
2
7
Proposed Receiver
Iterative Expectation Maximization (EM) based on noise model
Communication Performance
0
10
OFDM Receiver
Single Carrier Simulation Parameters
-1 Proposed EM-based Receiver
10
• BPSK Modulation
-2 • Interference Model
Bit Error Rate
10
2-term Gaussian Mixture Model
-3 ~ 5 dB
10
-4
10
-10 -5 0 5 10 15 20
Signal to Noise Ratio (SNR) [in dB]
Wireless Networking and Communications
Group
28. Smart Grids: The Big Picture
2
8
Long distance
Real-Time : communication :
Customers profiling access to isolated
enabling good houses
predictions in demand Micro- production
= no need to use an : better knowledge
additional power plant of energy produced
to balance the
Demand-side network
management : boilers
are activatedduring the
night
whenelectricityisavaila
ble
Smart building :
Anydisturbance due to a significant cost reduction
storm : action on energy bill through Security
canbetakeninmediatelybas remote monitoring featuresFireisdetect
ed on real-time ed :
Smart car : charge of
information relaycanbeswitched
electricalvehicleswhile
panels are producing off rapidly
Source: ETSI
29. Wireless Networking & Comm. Group
2
9
Applications
Systems of systems Networks of networks
Networks of systems
Systems Networks
Compilers Middleware
Operating systems Protocols
Processors Communication
links
Circuit
design Waveforms
Data Antennas
Collaboration acq. Wires 17 faculty
with UT faculty
outside of WNCG 140 grad students
Devices
30. Wireless Networking & Comm. Group
3
0
Communications Networking Applications
B. Evans J. Andrews S. Nettles B. Bard C. Caramanis A. Bovik
Embedded DSP Communication
Computation
Network Design Security Optimization Image/Video
A. Gerstlauer R. Heath S. Shakkottai G. de Veciana S. Sanghavi A. Tewfik
Embedded Sys Comm/DSP Network Theory Networking Network Science Biomedical
T. Rappaport T. Humphreys S. Vishwanath L. Qiu H. Vikalo
RF IC Design GPS/Navigation Sensor Networks Network Design Genomic DSP
31. Our Publications
3
1
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”, IEEE
Transactions 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 Signal
Processing 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. on
Acoustics, Speech, and Signal Proc., Mar. 14-19, 2010.
• K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel
Interference”, Proc. IEEE Int. Global Comm. Conf., Nov. 30-Dec. 4, 2009.
Cont…
Wireless Networking and Communications
Group
32. Our Publications
3
2
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, “MIMO
Receiver 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.
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.
Wireless Networking and Communications
Group
33. References
3
3
RFI Modeling
1. 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.
Wireless Networking and Communications
Group
34. References
3
4
Parameter Estimation
1. 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 Networks
1. 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.
Wireless Networking and Communications
Group
35. References
3
5
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 RFI
1. 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
Wireless Networking and Communications
Group
36. References
3
6
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 Impact
1. 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 on
Electromagnetic Compatibility, vol.3, no., pp. 626-631, 14-18 Aug. 2006
Wireless Networking and Communications
Group
Hinweis der Redaktion
PRIME and G3 use OFDMIEC 61334 uses SFSK
In order to make new application area possible, we need to have the compute & communicate architecturesTo design it all, we need new design methodology and tools Increasing complexity- Hard to make everything work together but there are tremendous advantagesWe are UT. We are going and starting an interdiscliplanary new movement thanks to the new Dean. New paradigm shift inside the Cockrell school to enable this paradigm shift in industry.
