karl f. nieman † , marcel nassar ‡ , jing lin † , and brian l. evans †
DESCRIPTION
Karl F. Nieman † , Marcel Nassar ‡ , Jing Lin † , and Brian L. Evans †. Pacific Grove, CA November 6, 2013. FPGA Implementation of a Message-Passing OFDM Receiver for Impulsive Noise Channels. IEEE Asilomar Conference on Signals, Systems, and Computers. - PowerPoint PPT PresentationTRANSCRIPT
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Karl F. Nieman†, Marcel Nassar‡, Jing Lin†, and Brian L. Evans†
Pacific Grove, CANovember 6, 2013
FPGA Implementation of a Message-Passing OFDM
Receiver for Impulsive Noise Channels
IEEE Asilomar Conference on Signals, Systems, and Computers
†Wireless Communications and Networks Group, The University of Texas at Austin, Austin, TX‡Mobile Solutions Lab, Samsung Information Systems America, San Diego, CA
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Smart Grid CommunicationsLocal utility
MV-LV transformer
Smart meters
Data concentrator
Home area data networksconnect appliances, EV charger and smart meter via powerline or wireless links
Smart meter communicationsbetween smart meters and data concentrator via powerline or wireless links
Communication backhaulcarries traffic between concentrator and utility on wired or wireless links
Low voltage (LV)< 1 kV
Medium Voltage (MV)1 kV – 33 kV
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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Impulsive Noise in 3-200 kHz PLC Band Outdoor medium-voltage line
(St. Louis, MO)
Cyclostationary noise becomes asynchronous after interleaving
Indoor low-voltage line (UT Campus)
= 1 MHz
Interleave
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
Impulsive noise can be 40 dB above background noise
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Impulsive Noise in OFDM Systems
FFT spreads received impulsive noise across all FFT bins– SNR of each FFT bin is decreased– Receiver communication performance degrades
IFFT Filter + FFTEqualize
r and detectorVector
of symbolamplitudes(complex) Channel
Receiver
𝐬 𝐲
Gaussian () +
ImpulsiveNoise ()
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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Impulsive Noise Mitigation (Denoising)
• FFT bins (tones)– Transmitter null tones have zero power– Received null tones contain noise
• Impulsive noise estimation– Exploit sparse structure of null tones– is over complete dictionary– is sparse vector– is complex Gaussian ()
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IFFT Filter + + FFTEqualize
r and detectorImpulsive
noise estimatio
n
Gaussian () +
ImpulsiveNoise ()
Vectorof symbolamplitudes(complex)
+-
Channel
Receiver
Ω is set of null tones (i.e. ) is DFT matrix
𝐬 𝐲
||
¿
+¿
Conventional OFDM systemAdded in our system
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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Impulsive Noise Mitigation Techniques
MethodLow SNR
High SNR
Non-Parametric
?
Computational
ComplexityNulling/Clipping[Tse12]
Low
Thresholded Least Squares/MMSE[Cai08]
Med
Sparse Bayesian Learning[Lin13]
High(matrix
inversion)l1-norm minimization[Cai08]
High
Approximate Message Passing (AMP) [Nas13]
Med
com
pres
sive
sens
ing
• Compressive sensing approaches are used for low SNR• AMP provides best performance vs. complexity
tradeoff
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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• M = null tones• N = FFT size• Iterate
– Time-frequencyprojections
• Mostly scalar arithmetic and data• Parallelizable for hardware
implementation– FFT/IFFT, exponential, vector multiplies, divisions
Approximate Message Passing (AMP)1.
Initialization 2. Output Linear 3. Output Non-Linear
5. Input Non-Linear 4. Input Linear
𝜏 𝑖𝑝 (𝑡 )=∑
𝑗𝜏 𝑗𝑥 (𝑡 )
𝜏 𝑖𝑠 (𝑡 )= 1
𝛾𝐵+𝜏𝑝
𝑖 (𝑡 )=𝜏 𝑖𝑠 (𝑡 ) ( 𝑦 𝑖−𝑖 (𝑡 ) )
𝜏 𝑗𝑟 (𝑡 )= 𝑁
𝑀𝜏𝑠 (𝑡 )
𝑗 (0 )=0
𝜌 𝑗=𝜂 𝑗
1+𝜂 𝑗 𝑗 (𝑡+1 )=
𝛾 𝐼
𝛾 𝐼+𝜏 𝑗𝑟 (𝑡 )
𝜌 𝑗𝑟 𝑗 (𝑡 )
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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Synchronous Dataflow (SDF) Model• Targeted architecture for real-time streaming
performance:– Xilinx Virtex V field programmable gate arrays (FPGAs)– Embedded x86 computers running real-time OS (Phar Lap ETS)
• SDF model of OFDM receiver with AMP noise mitigation:
• Periodic schedule is
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
(6950𝐎 ) (278𝐀 ) (278𝐁 )𝐂𝐃𝐄𝐅𝐇𝐈
Task
Processing
O Input samples from ADC
A Resampling FIR filters
B Time and Freq. Offset Correction
C FFT + Index Active and Null Subcarriers
D AMP Noise Estimation
E FFT + Index Active Subcarriers
FSubtract Noise Estimate, De-Interleave Reference Symbols
H Zero-Forcing Equalization
I Equalize and Detect
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Mapping AMP to Fixed-Point• Variables sized using MATLAB Fixed-Point Toolbox• Most variables sized within 16-bit wordlengths
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sizing for using graphical tool
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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Graphical High-Level FPGA SynthesisNational Instruments Communication System Design Tools
– LabVIEW DSP Design Module– LabVIEW FPGA– LabVIEW Real-Time
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
2. Output Linear
𝜏 𝑖𝑝 (𝑡 )=∑
𝑗𝜏 𝑗𝑥 (𝑡 )
𝑖 (𝑡 )=𝐼ΩFFT ( (𝑡 ) )−𝜏 𝑖𝑝 (𝑡 ) 𝑖 (𝑡−1 )
Step 2 of AMP
DSP diagram replaces
thousands of lines of VHDL
code
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AMP-Enhanced OFDM Testbed
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RT controller
LabVIEW RT
data symbol generation
FlexRIO FPGA Module 1 (G3TX)
LabVIEW DSP Design Module
data and reference symbol
interleave Ref. symbol LUT
zero padding
(null tones)
generatecomplex
conjugate pair
256 IFFT w/ 22 CP insertion
NI 5781
16-bit DAC
RT controller
LabVIEW RT
BER/SNR calculation w/ and w/o AMP
FlexRIO FPGA Module 2 (G3RX)
LabVIEW DSP Design Module
NI 5781
14-bit ADCsample
rate conversion
time and frequency
offset correction
256 FFT w/ 22 CP removal,
noise injection
FlexRIO FPGA Module 3 (AMPEQ)
LabVIEW DSP Design Module
null tone and active
tone separation
channel estimation/
ZFequalization
AMP noise estimate
Subtract noise
estimate from active
tones
data and reference
symbol de- interleave
Host Computer
LabVIEW
sample rate
conversion
256 FFT, tone select
testbench control/data visualization
diffe
rent
ial M
CX p
air
TX Chassis RX Chassis1 × PXIe-10821 × PXIe-81331 × PXIe-7965R1 × NI-5781 FAM
differential MCX pair(quadrature component = 0)
1 × PXIe-10821 × PXIe-81332 × PXIe-7965R1 × NI-5781 FAM
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
2-mode Gaussian Mixture noise injected here: ~
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Results• System implemented using G3-PLC signaling structure
MHz, (real-valued), active tones• Receiver w/ AMP was mapped across two FPGAs
– ‘G3RX’ – Downsampling, IFFT, time/frequency offset correction– ‘AMPEQ’ – AMP algorithm, equalization, and detection
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Utilization
Trans.
Rec. AMP+Eq
FPGA 1 2 3total slices
32.6% 64.0%
94.2%
slice reg. 15.8% 39.3%
59.0%
slice LUTs 17.6% 42.4%
71.4%
DSP48s 2.0% 7.3% 27.3%blockRAMs
7.8% 18.4%
29.1%
Received QPSK constellation at equalizer output
conventional receiver
with AMP
Resource Utilization
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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Bit-Error-Rate Measurements
13Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
unco
ded
bit-e
rror-r
ate
(BER
)
signal-to-noise ratio (SNR)
8 dB for 30 dBimpulsive
noise4 dB for 20 dB
impulsive noise
No loss (or gain) in non-
impulsive (AWGN) noise
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Conclusions• Approximate Message Passing Framework allows
– Impulsive noise mitigation at low and high SNR– Conversion of matrix operations to scalar and vector
operations– Parallelization and efficient mapping to hardware
• Up to 8 dB impulsive noise mitigation achieved using– Fixed-point data and arithmetic– Streaming G3-PLC rates
• LabVIEW project and FPGA bitfiles available here:– http://users.ece.utexas.edu/~bevans/papers/2013/fpgaReceiver/index.
html
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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References[Cai08] – G. Caire; T. Y. Al-Naffouri; A. K. Narayanan, "Impulse noise cancellation in
OFDM: an application of compressed sensing," Information Theory, 2008. ISIT 2008. IEEE International Symposium on , 2008.
[Tse12] – D-F. Tseng; Y. S. Han; W. H. Mow; L-C. Chang; A.J.H. Vinck, "Robust Clipping for OFDM Transmissions over Memoryless Impulsive Noise Channels," Communications Letters, IEEE , vol.16, no.7, 2012.
[Lin13] – J. Lin; M. Nassar; B. L. Evans, "Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning," Selected Areas in Communications, IEEE Journal on , vol.31, no.7, 2013.
[Nas13] – M. Nassar; P. Schniter; B. L. Evans, "A factor graph approach to joint OFDM channel estimation and decoding in impulsive noise environments," IEEE Trans. on Signal Processing, accepted for publication, 2013.
[Max11] – Maxim and ERDF, "Open Standard for Smart Grid Implementation," 2011.
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Questions?
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Backup Slides
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Powerline Communications (PLC)
• Uses orthogonal frequency-division multiplexing (OFDM)
• Communication challenges– Channel distortions– Non-Gaussian impulsive noise
Categories Band Bit Rates Coverage Enables Standards
Narrowband 3-500 kHz
up to 800 kbps
Multi-kilometer
Smart meter communication
• (ITU) PRIME, G3• ITU-T G.hnem• IEEE P1901.2
Broadband 1.8-250 MHz
up to200 Mbps <1500 m Home area
data networks•HomePlug•ITU-T G.hn•IEEE P1901
Background | Impulsive Noise Mitigation | Mapping to Hardware | Implementation
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AMPEQ.lvdsp(first half)
Background | System Design and Implementation | Demo | Conclusion
(second half)
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Approximate Message Passing (AMP)
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= number of null tones
= FFT size
• Reconstruct time-domainnoise from frequency-domain null tones
• Iterate until convergence
• Algorithm consists of:• Mostly scalar arithmetic• FFT/IFFTs• Exponential
• Targeted at G3-PLC signaling structureBackground | Impulsive Noise Mitigation | Mapping to Hardware |
Implementation