cr wideband spectrum sensing baseband progress report 05/29/2009

Click to edit Master title style

CR Wideband Spectrum Sensing Baseband Progress Report

05/29/2009

Tsung-Han [email protected]

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


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


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


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


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


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.

cr wideband spectrum sensing baseband progress report 05/29/2009

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