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Wireless Networking and Communications Group. Prof. Brian L. Evans Department of Electrical and Computer Engineering The University of Texas at Austin In collaboration with PhD students Ms. Jing Lin and Mr. Marcel Nassar. - PowerPoint PPT Presentation
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August 4, 2011
Non-Parametric Methods forMitigating Interference in OFDM
Receivers
American University of Beirut
1
Prof. Brian L. Evans
Department of Electrical and Computer Engineering
The University of Texas at Austin
In collaboration with PhD studentsMs. Jing Lin and Mr. Marcel Nassar
Wireless Networking and Communications Group
Outline
Motivation
System model
Prior work
Sparse Bayesian learning
Proposed algorithms and results
Conclusion
Wireless Networking and Communications Group
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3
Mobile Internet Data: The Big Picture
Observations 2x increase/year in data traffic: 1000x increase next 10 years Demand is increasing exponentially but revenues are not Revenue and traffic suddenly decoupled vs. voice service Business models remain fuzzy especially for video
Consequences to industry Restrict data usage (unpopular) OR Decrease cost per bit exponentially (how?) OR Lose money and/or watch network collapse (current status)
Wireless Networking and Communications Group
Source: J. G. Andrews, "Wireless 1000x?", University of Notre Dame Seminar, May 5, 2011.
Heterogeneity: Make Cells Smaller/Smarter
Demand handled by different networks Macrocells guarantee basic coverage and
require fast dedicated backhaul Picocells target traffic “hotspots” Femtocells must interoperate w/ cellular
networks with minimal coordination
Wireless Networking and Communications Group
4
Basestation
Range
Power
Build Costs
Oper. Costs
Deployed By
Macrocell 1-10 km
40W $100k High Service Provider
Picocell 100m 1-2W $15-40k Low Service Provider
Femtocell 10m 200mW
$100 Very Low User at Home
Source: J. G. Andrews, "Wireless 1000x?", University of Notre Dame Seminar, May 5, 2011.
Tower-mounted
macrocell
pico
femto
Basestations
Wireless Networking and Communications Group
Wireless Interference5
Guard zone
Example: Dense Wi-Fi Networks
Duration
Channel 11
Channel 11
Channel 9
(a) (a)
(b)(c)
(d)
Interferencea) Co-channelb) Adjacent channelc) Out-of-platformd) In-platform
Wireless Networking and Communications Group
In-Platform Interference6
May severely degrade communication performance Impact of LCD noise on throughput for IEEE 802.11g
embedded wireless receiver [Shi, Bettner, Chinn, Slattery & Dong, 2006]
Low-Voltage Power Line Noise
Wireless Networking and Communications Group
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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/fr
eque
ncy
(dB
/Hz)
Power Spectral Density Estimate Measurement on 20 Mar 2011 on low-voltage US apartment power outlet at 5:00 amPowerline comm. standards use either 40-90 kHz or 10-500 kHzImpulsive noise is 45-50 dB above the noise floor
[Nassar, Gulati, Mortazavi & Evans, 2011]
Heterogeneity: Receiver’s Perspective
Wireless Networking and Communications Group
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Wireless CommunicationSources
Uncoordinated Transmissions
Non-CommunicationSources
Electromagnetic radiations
Computational Platform• Clocks, busses, processors• Other embedded transceivers
Antennas
Baseband Processor
Network heterogeneity leads to the increase of uncoordinated interference at the receiver
Statistical Modeling of Interference
Wireless Networking and Communications Group
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• Cellular networks• Hotspots (e.g. café)
• Sensor networks• Ad hoc networks
• Dense Wi-Fi networks• Networks with contention
based medium access
Symmetric Alpha Stable Middleton Class A (form of Gaussian Mixture)
[Gulati, Evans, Andrews & Tinsley, 2010]
Statistical Modeling of Interference
Wireless Networking and Communications Group
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• Cluster of hotspots (e.g. marketplace)
• In-cell and out-of-cell femtocell users in femtocell networks
• Out-of-cell femtocell users in femtocell networks
Symmetric Alpha Stable Gaussian Mixture Model
[Gulati, Evans, Andrews & Tinsley, 2010]
Statistical Modeling of Interference
Low-voltage power lines Multiple noise sources 1% of impulses exceed
1 ms in duration Amplitude statistics
By derivation, model is Gaussian mixture
Gaussian mixture best fit for tail probabilities
Wireless Networking and Communications Group
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Data captured on power outlet in apartment in Austin, Texas USA, 20 Mar 2011Fit blocks of 14 ms of data sampled at 1 MSample/s (blocks of 14000 samples)
[Nassar, Gulati, Mortazavi & Evans, 2011]
Statistical Models of Impulsive Noise
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
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Orthogonal Frequency Division Multiplexing
Divides transmission band into narrow subchannels Null tones at band edges for reducing spectral leakage Null tones in low signal-to-noise ratio (SNR) subchannels Pilot tones for synchronization and channel estimation Power loading per subcarrier to increase data rates
Subchannel processing combats multipath effects Better resilience to impulsive noise vs. single carrier Used in modern data communications standards
Wireless: IEEE802.11a/g/n, cellular LTE Powerline: PRIME, G3, IEEE1901.2Wireless Networking and Communications Group
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Need
Proposed impulsive noise mitigation in OFDM receiver No assumption of a specific impulsive noise model Exploit sparse nature of impulsive noise
System Model
Wireless Networking and Communications Group
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[Lin, Nassar & Evans, 2011]
OFDM Receivers in Impulsive Noise
DFT spreads out impulsive energy across all tones
SNR of each tone is decreased Receiver performance degrades Noise in each tone is asymptotically Gaussian (as )Wireless Networking and Communications Group
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[Lin, Nassar & Evans, 2011]
16
Parametric Methods
Use parameterized functional forms of noise statistics Need to estimate and track noise parameters
Suffer degradation in performance Due to model mismatch or parameter mismatch When noise statistics are changing rapidly
Not dependent on null tones Higher throughput when noise statistics are slowly varying
Complexity in parameter estimation and tracking OFDM decoders: high complexity for optimality and
low-complexity approximations may work well enoughWireless Networking and Communications Group [Lin, Nassar & Evans, 2011]
Prior Work
Wireless Networking and Communications Group
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OFDM Systems in Impulsive Noise
Haring2003 SISO Iterative Decoding • high complexity• approaches PEP bound• assumes noise model
Haring2001 SISO MMSE Estimate • with and without CSI• assumes noise model
Haring2000 SISO Iterative Decoding with Threshold Limiter
• threshold not flexible• assumes noise model
Matsuo2002 SISO Iterative Decoding with Threshold Limiter
• threshold selection is ad-hoc•assumes noise model
Caire2008 MIMO Compressed Sensing approach
• very limited number of samples
Parametric Methods
Semi-nonParametric Methods (Threshold Selection)
nonParametric Method
[Lin, Nassar & Evans, 2011]
Sparse Bayesian Learning
Underdetermined linear regression : observation vector : sparse weight vector: i.i.d. Gaussian noise w/ variance : overcomplete basis
SBL algorithmParameterized Gaussian prior o: Estimate by computing maximum likelihood (ML) using expectation maximizationEstimate w from posterior mean: Guaranteed to converge to sparse solution Fewer local minima vs. other compressed sampling algorithms
Wireless Networking and Communications Group
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[Lin, Nassar & Evans, 2011]
M: # of known tonesN: total # of tones
Estimation Using Null Tones
Noise observed on null tones is DFT matrix, = , and sparse weight vector and
Estimate e by sparse Bayesian learning Parameterized Gaussian prior imposed on e ML estimation of two hyper-parameters Minimum mean-square estimate of e
Receiver block diagram
Wireless Networking and Communications Group
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[Lin, Nassar & Evans, 2011]
Estimation Using All Tones
Joint estimation of data and noise is DFT matrix and = Treat as a third hyper-parameter to be estimated is relaxed to be continuous variables to guarantee
convergence of expectation maximization algorithm Estimate of sent to channel equalizer and MAP detector with
hard decisions after impulsive noise mitigation Receiver
blockdiagram
Wireless Networking and Communications Group
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M: # of known tonesN: total # of tones
[Lin, Nassar & Evans, 2011]
Communication Performance Simulations
In different impulsive noise scenarios
Wireless Networking and Communications Group
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Gaussian mixture model Middleton Class A model
~6dB
~8dB~6dB
~10dB
~4dB
Parametric (no null tones =>higher throughput)
[Lin, Nassar & Evans, 2011]
Communication Performance Simulations
In different impulsive noise scenarios (continued)
Wireless Networking and Communications Group
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Symmetric alpha stable model
~7dB
~4dB
[Lin, Nassar & Evans, 2011]
Communication Performance Simulations
Wireless Networking and Communications Group
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Performance of first algorithm as number of known tones decreases SNR is 0 dB 256 tones Middleton Class A noise
In both algorithms, theEM algorithm converges after a few iterations
[Lin, Nassar & Evans, 2011]
Comparison
Based on formula for impulsive noise distribution
Needs parameter estimation Good for slowly varying noise
statistics Suffer from model mismatch
in fast varying environments High complexity for optimal
decoders
No assumption of noise statistics
Uses null tones in each OFDM symbol
Robust in fast varying noise environments
Potential reduction in throughput due to null tones (if not already in standard)
Parametric Methods Non-Parametric Methods
24
Wireless Networking and Communications Group
Conclusions and Future Work
Proposed impulsive noise reduction algorithms Assume real-valued OFDM symbols (G3, PRIME, ADSL) Use null + pilot tones to give 4-6 dB SNR gain in simulation Use all tones to give 8-10 dB SNR gain in simulation
Future work Extend to complex-valued OFDM symbols (802.11a/b/n, LTE) Track impulsive noise OFDM symbol to OFDM symbol Incorporate knowledge of noise statistics Add channel estimation Analyze performance with coding and with correlated noise
Wireless Networking and Communications Group
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References
G. Caire, T. Al-Naffouri, and A. Narayanan, “Impulse noise cancellation in OFDM: an application of compressed sensing,” Proc. IEEE Int. Sym. on Info. Theory, 2008, pp. 1293–1297.
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.
K. Gulati, B. Evans, J. Andrews, and K. Tinsley, “Statistics of cochannel interference in a field of Poisson and Poisson-Poisson clustered interferers,” IEEE Trans. on Signal Proc., vol. 58, no. 12, pp. 6207–6222, 2010.
J. Haring and A. Vinck, “Iterative decoding of codes over complex numbers for impulsive noise channels,” IEEE Trans. Info. Theory, vol. 49, no. 5, pp. 1251–1260, 2003.
J. Lin, M. Nassar, and B. L. Evans, “Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning,” Proc. IEEE Int. Global Communications Conf., Dec. 5-9, 2011.
D. Middleton, “Statistical-Physical Models of Electromagnetic Interference”, IEEE Trans. On Electromagnetic Compatibility, vol. 19, no. 3, Aug. 1977, pp. 106-127.
D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: new methods an results for Class a and Class b noise models,” IEEE Trans. on Info. Theory, vol. 45, no. 4, pp. 1129–1149, 1999.
Wireless Networking and Communications Group
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References
M. Nassar, K. Gulati, M. DeYoung, B. Evans, and K. Tinsley, “Mitigating near-field interference in laptop embedded wireless transceivers,” Journal of Signal Proc. Sys., pp. 1–12, 2009.
M. Nassar, K. Gulati, Y. Mortazavi, and B. L. Evans, “Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks”, Proc. IEEE Int. Global Communications Conf., Dec. 5-9, 2011.
M. Nassar and B. L. Evans, "Low Complexity EM-based Decoding for OFDM Systems with Impulsive Noise", Proc. Asilomar Conf. on Signals, Systems and Computers, Nov. 6-9, 2011.
H. W. Sorenson and D. L. Alspach, “Recursive Bayesian estimation using Gaussian sums”, Automatica, vol. 7, no. 4, July 1971, pp. 465-479.
E. S. Sousa, “Performance of a spread spectrum packet radio network link in a Poisson field of interferers,” IEEE Trans. on Info. Theory, vol. 38, no. 6, pp. 1743–1754, Nov. 1992.
D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Trans. Signal Proc., vol. 52, no. 8, pp. 2153–2164, 2004.
Wireless Networking and Communications Group
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BACK UP SLIDES28
Wireless Networking and Communications Group
Interference Mitigation Techniques (cont…)
Interference cancellationRef: J. G. Andrews, ”Interference Cancellation for Cellular Systems: A Contemporary Overview”, IEEE Wireless Communications Magazine, Vol. 12, No. 2, pp. 19-29, April 2005
Wireless Networking and Communications Group
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Return
Femtocell Networks
Reference:V. Chandrasekhar, J. G. Andrews and A. Gatherer, "Femtocell Networks: a Survey", IEEE Communications Magazine, Vol. 46, No. 9, pp. 59-67, September 2008
Wireless Networking and Communications Group
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Return
Wireless Networking and Communications Group
Problem Statement31
Designing wireless transceivers to mitigate residual RFI
Guard zone
Example: Dense Wi-Fi Networks
Duration
Channel 11
Channel 11
Channel 9
Physical (PHY) Layer
Improves: Link communication performance
Transmitsignal Pre-Filter Conventional
Receiver
RFIThermal
noise
Medium Access Control (MAC) LayerOptimize channel access protocols, e.g.,
Improves: Network communication performance
Distribution of Duration
Poisson Field of Interferers
Interferers distributed over parametric annular space
Log-characteristic function
Wireless Networking and Communications Group
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Return
Poisson Field of Interferers
Wireless Networking and Communications Group
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Return
Poisson-Poisson Cluster Field of Interferers
Cluster centers distributed as spatial Poisson process over
Interferers distributed as spatial Poisson process
Wireless Networking and Communications Group
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Return
Poisson-Poisson Cluster Field of Interferers
Log-Characteristic function
Wireless Networking and Communications Group
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Return
Gaussian Mixture vs. Alpha Stable
Gaussian Mixture vs. Symmetric Alpha Stable
Wireless Networking and Communications Group
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Gaussian Mixture Symmetric Alpha StableModeling Interferers distributed with Guard
zone around receiver (actual or virtual due to pathloss function)
Interferers distributed over entire plane
Pathloss Function
With GZ: singular / non-singularEntire plane: non-singular
Singular form
Thermal Noise
Easily extended(sum is Gaussian mixture)
Not easily extended (sum is Middleton Class B)
Outliers Easily extended to include outliers Difficult to include outliers
Return
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Wireless Networking and Communications Group
Middleton Class A model
Probability Density Function
1
2!)(
2
2
02
2
2
Am
where
em
Aezf
m
z
m m
mA
Zm
-10 -5 0 5 100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Noise amplitude
Pro
babi
lity
dens
ity fu
nctio
n
PDF for A = 0.15, = 0.8
A
Parameter
Description RangeOverlap Index. Product of average number of emissions per second and mean duration of typical emission
A [10-2, 1]
Gaussian Factor. Ratio of second-order moment of Gaussian component to that of non-Gaussian component
Γ [10-6, 1]
Return
Home Power Line Noise Measurement
Wireless Networking and Communications Group
38
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/fr
eque
ncy
(dB
/Hz)
Power Spectral Density Estimate
Home Power Line Noise Measurement
Wireless Networking and Communications Group
39
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/fr
eque
ncy
(dB
/Hz)
Power Spectral Density Estimate
Spectrally-ShapedBackground Noise
Home Power Line Noise Measurement
Wireless Networking and Communications Group
40
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/fr
eque
ncy
(dB
/Hz)
Power Spectral Density Estimate
Spectrally-ShapedBackground Noise
Narrowband Noise
Home Power Line Noise Measurement
Wireless Networking and Communications Group
41
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/fr
eque
ncy
(dB
/Hz)
Power Spectral Density Estimate
Spectrally-ShapedBackground Noise
Narrowband Noise
Periodic and Asynchronous Noise
Analytical Models for Powerline Noise
Wireless Networking and Communications Group
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Expectation Maximization Overview
Wireless Networking and Communications Group
4343
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44
Video over Impulsive Channels
Video demonstration for MPEG II video stream 10.2 MB compressed stream from camera (142 MB uncompressed) Compressed file sent over additive impulsive noise channel Binary phase shift keying
Raised cosine pulse10 samples/symbol10 symbols/pulse length
Composite of transmitted and received MPEG II video streamshttp://www.ece.utexas.edu/~bevans/projects/rfi/talks/video_demo19dB_correlation.wmv Shows degradation of video quality over impulsive channels with
standard receivers (based on Gaussian noise assumption)Wireless Networking and Communications Group
Additive Class A Noise ValueOverlap index (A) 0.35Gaussian factor () 0.001SNR 19 dB
Return
Video over Impulsive Channels #2
Video demonstration for MPEG II video stream revisited 5.9 MB compressed stream from camera (124 MB uncompressed) Compressed file sent over additive impulsive noise channel Binary phase shift keying
Raised cosine pulse10 samples/symbol10 symbols/pulse length
Composite of transmitted video stream, video stream from a correlation receiver based on Gaussian noise assumption, and video stream for a Bayesian receiver tuned to impulsive noise
http://www.ece.utexas.edu/~bevans/projects/rfi/talks/video_demo19dB.wmv
Wireless Networking and Communications Group
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Additive Class A Noise ValueOverlap index (A) 0.35Gaussian factor () 0.001SNR 19 dB
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Video over Impulsive Channels #2
Structural similarity measure [Wang, Bovik, Sheikh & Simoncelli, 2004]
Score is [0,1] where higher means better video quality
Frame number
Bit error rates for ~50 million bits sent:
6 x 10-6 for correlation receiver
0 for RFI mitigating receiver (Bayesian)
Return