cr wideband spectrum sensing baseband progress report 05/29/2009
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CR Wideband Spectrum Sensing Baseband Progress Report
05/29/2009
Tsung-Han Yuthyu0918@ee.ucla.edu
Click to edit Master title style Outline Motivation
System specification
Weak signal detection
Sideband power estimation
Conclusion
Click to edit Master title style Motivation Why cognitive radio
– Growing demand on spectrum utilization– Opportunistic way to access spectrum
Spectrum sensing– Key function for cognitive radio system– Reliably detect weak primary signals– Avoid harmful interference
Why wideband sensing– Sense empty channel at a time– Save RF front-end design complexity
Challenge– Channelize signal introduce spectral leakage
Click to edit Master title styleSystem Specification Weak signal detection
– Wideband sensing ~ 250MHz– Signal detection < -5dB– PFA < 0.1 / PD > 0.9– Spectral resolution ~ 200 KHz
Sideband power estimation– Measure reference sideband power down to -70 dBm – Estimation error < 0.5 dB– Sensing time < 20-30 ms
Click to edit Master title styleWeak Signal Detection PSD-based energy detection
– Apply FFT for spectrum estimation– Apply polyphase filterbank to reduce spectral leakage
EnergyMeas.4 Polyphase
Filterbank1024FFT
From ADC | | 2 Avg.
BRAM
Z-1024 Z-1024
Prog
ram
mab
leCo
effici
ents
c0c1
cP-1cP-2
c0 c1 cP-2 cP-1
ComplexReal
NoiseEst.
EnergyDetect
RF Spectrum MeasurementCR Spectrum
Sensing
Real-timeChannel
OccupancyThreshold
Click to edit Master title stylePolyphase Filterbank Apply polyphase filterbank [1], [2]
Decompose the M-tap lowpass filter into N K-tap lowpass filter
N
N
N
X(n)
X0(m)
X1(m)
XN-1(m)
X(n)WN-n0
WN-n1
WN-n(N-1)
N
N
N
X0(m)
X1(m)
XN-1(m)
X(n) X0(m)
X1(m)E1(ZN)
EN-1(ZN)
E0(ZN)
N-pointDFT
XN-1(m)
Z-1
Z-1
Z-1
1
0
Mn
n
H z h n z
1
0
KN rN
lr
E z h l r N z
Click to edit Master title styleSpectrum Sensing in Freq. Domain Power Detector:
H0:– Mean– Variance
H1:– Mean– Variance
1
1
22/2 1 1 1
2/2 0 0
1 [ ]j knk K M NN
k k K m n
T x n mN eN MK
0
1
: ( ) ( ): ( ) ( ) ( )
H x n nH x n s n v n
N: FFT sizeM: # of Avg.K: BW (in FFT bin)
2 / N
22 / /N M K 2
0 2
/( ) Pr( | )/f
NP T H QN M K
2
0 2
/ /( ) Pr( | )
/ /d
P K NP T H Q
p K N M K
2 / /N P K
22 / / /N P K M K
Click to edit Master title styleNumerical Result (1/2) Large-bandwidth signal
detection– ~8 us sensing time for
-5dB SNR
Large-bandwidth signal detection with strong blockers
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PFB-FFTFFT
Sensing Time: 8 usNoise Power: -90 dBm/ 10MHzPU1 (Power / BW): (-95 dBm / 10MHz)PU2 (Power / BW): (-95 dBm / 10MHz)PU3 (Power / BW): (-95 dBm / 10MHz)
PFA
P D
f
PU1 PU2 PU3
Band1 BOI Band3(BOI: Band of Interest)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PFA
P D
Sensing Time: 8 usNoise Power: -90 dBm/ 10MHzPU1 (Power / BW): (-65 dBm / 10MHz)PU2 (Power / BW): (-95 dBm / 10MHz)PU3 (Power / BW): (-65 dBm / 10MHz)
f
PU1 PU2 PU3
Band1 BOI Band3(BOI: Band of Interest)
PFB-FFTFFT
3X PMD
Click to edit Master title styleNumerical Result (2/2) Narrow-bandwidth signal
detection– ~0.4 ms sensing time for
-5dB SNR
Narrow-bandwidth signal detection with strong blockers– Interferer cancellation
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PFA
P D
Sensing Time: 0.4 msNoise Power: -107 dBm/ 250KHzPU1 (Power / BW): (-112 dBm / 250KHz)PU2 (Power / BW): (-112 dBm / 250KHz)PU3 (Power / BW): (-112 dBm / 250KHz)
f
PU1 PU2 PU3
Band1 BOI Band3(BOI: Band of Interest)
PFB-FFTFFT
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Sensing Time: 0.4 msNoise Power: -107 dBm/ 250KHzPU1 (Power / BW): ( -82 dBm / 250KHz)PU2 (Power / BW): (-112 dBm / 250KHz)PU3 (Power / BW): ( -82 dBm / 250KHz)
f
PU1 PU2 PU3
Band1 BOI Band3(BOI: Band of Interest)
PFA
P D
PFB-FFTFFT
Click to edit Master title styleComputation Complexity1024pt FFT (# Op. / 4ns)
P-Tap PFB(# Op. / 4ns)
Avg.(# Op. / 4ns)
LOAD 10 P-1 1
STORE 10 P-1 1
MULT (complex) 10 P 0
ADD (complex) 10 P-1 1
EnergyMeas.4 Polyphase
Filterbank1024FFT
From ADC | | 2 Avg.
BRAM
Z-1024 Z-1024
Prog
ram
mab
leCo
effici
ents
c0c1
cP-1
cP-2
c0 c1 cP-2 cP-1
ComplexReal
NoiseEst.
EnergyDetect
RF Spectrum MeasurementCR Spectrum
Sensing
Real-timeChannel
OccupancyThreshold
Click to edit Master title styleSideband Power Estimation Use energy detector to measure sideband power
– Apply FFT to channelize the spectrum– Apply polyphase filterbank to reduce spectral leakage
f1 f1 + fREF
PLL
ADC
fs fs + fREF
f1
Spectrum Sensing
Processor
EnergyMeas.4 Polyphase
Filterbank1024FFT
From ADC
| | 2 Avg.BRAM
Z-1024 Z-1024
Prog
ram
mab
leCo
effici
ents
c0c1
cP-1cP-2
c0 c1 cP-2 cP-1
ComplexReal
NoiseEst.
SidebandEst.
ToPLL
Force the sideband at a FFT bin
Measure the bin powerAvg. the bin power
Click to edit Master title style Summary Sensing large bandwidth signal
– w/o strong blocker● Take ~8us (2 FFT avg.) sensing time
– w/ strong blocker● Take ~8us (2 FFT avg.) sensing time● Improve PMD by 3X (PFA ~ 0.1)
Sensing narrow bandwidth signal– w/o strong blocker
● Take ~0.4ms (100 FFT avg.) sensing time– w/ strong blocker
● Require more sensing time● Require interferer cancellation
Click to edit Master title styleFuture Work Baseband algorithm
– Computation complexity analysis– Interference cancellation– Match-filter-based spectrum sensor– Match-filter-based sideband power estimator
Baseband implementation– Low-power high speed FFT design– BEE2 platform for real-time emulation
Click to edit Master title style Reference [1] B Farhang-Boroujeny, Filter Bank Spectrum Sensing
for Cognitive Radios, IEEE Trans. Signal Processing, vol. 56, no. 5, May 2008, pp. 1801-1811.
[2] F. Sheikh, and B Bing, Cognitive Spectrum Sensing and Detection Using Polyphase DFT Filter Banks, in Proc. 5th IEEE Consumer Communications and Networking Conference (CCNC), Jan. 2008, pp. 973-977.
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