11

Spectrum Sensing Technique using Polyphase DFT Filter Bank

for Opportunistic Cognitive Radios

Minseok Kim Jun-ichi Naganawa Jun-ichi Takada

May 29, 2009TCSR@Niigata University

Tokyo Institute of Technology

2

Background Cognitive Radio (CR) offers to solve spectrum

underutilization problem Spectrum sensing is an essential task to realize CR The challenges of spectrum sensing:

Reliability & Efficiency Opportunistic use of inactive sub-bands (white spaces)

White spaces (Spectrum Holes)

Frequency

Noise level

Now used Now usedNow used Now used

3Spectrum Dynamic Spectrum usage is dynamically changed in time and

frequency domain simultaneously How to detect the white spaces for opportunistic

cognitive radios is the main concern Frequency scanning is not suitable as the sensing

measurements become stale Wideband sensing scheme for multiple bands will be

required for efficient use of spectrum dynamic

Wideband RF front-end with wideband tuning function Settling time for channel switching is necessary Each band cannot be detected at the same time

Time Domain Narrowband Sensing

4

sensing sensing sensing sensing sensing sensing

Time

Channel switching & reconfiguration

RF

Variable LO

NarrowbandADC

2 T

Time

Frequency Domain Wideband Sensing

5

Search multiple unused sub-bands (white spaces) within wide frequency band continuously

No frequency sweeping Simultaneous spectrum sensing

Wideband ADC is necessary to sample wider band Dynamic range problem ADC resolution How to estimate the power spectrum density (PSD)

efficiently?

RF

Fixed LO

Spectrum Estimation

Wideband ADC

Time

Periodogram Spectrum Estimator (PSE)

6

21

0)()()(

N

kknxkhfY

fkjeN

kh 21)(

Most basic non-parametric spectrum estimator Apply discrete Fourier transform

)( 1ffH

)(nx

)( 0fY

)( 1fY

)( 2fY

)( 1NfY

Bandpass filters bank

Modulated Rectangular Window Sinc response in freq. domain

2

)(DFT1)( kxN

fY

)( fH

)( 2ffH

)( 1 NffH

7Dynamic Range & Resolution

MN /

0 5 10 15 20 25 30-50

-40

-30

-20

-10

0

Frequency [MHz]

Pow

er [d

B]

0 5 10 15 20 25 30-50

-40

-30

-20

-10

0

Frequency [MHz]

Pow

er [d

B]

0 5 10 15 20 25 30-50

-40

-30

-20

-10

0

Frequency [MHz]

Pow

er [d

B]

Conventional periodogram spectral estimator (PSE) by DFT filter has large sidelobe. The significant leakage of spectral power among different bands.

Limit spectral dynamic range Sidelobes can be decreased at the cost of wider main lobe by using windowing. Reduction of leakage among different bands is traded at the cost of a lower resolution in frequency Filter bank provides best performance to minimize the leakage power (best spectral dynamic range) keeping the frequency resolution. Prototype filter can be optimally designed

DFT Hanning Example prototype filterof filter bank

Spectrum Sensing Problems in Multiple Sub-channels

White spaces (Spectrum Holes)

Frequency

1H

0H

8

Noise level

Now used Now used Now used Now used

Power leakage from neighboring subbands

False alarm

helps detection

decided to be present

decided to be present

M bandpass filter array where each bandpass filter is the frequency-shifted version of a low pass filter H(w) with impulse response of a prototype filter

Filter bank can provide efficient bandpass filter implementation with low spectrum power leakage (good false alarm performance)

9Poly-phase DFT Filter Bank

M2

Mm2

MM )1(2

th0 st1 thm th)1( M

)(H

nd2

Analysis filter bank Bandpass filters for each sub-band

Output spectra

Polyphase decomposition

Discrete Fourier transform

Poly-phase DFT Filter BankM

-Point ID

FT

-1z

-1z

-1z

)(0 kx

)(1 kx

)(2 kx

)(1 kxM

)(0 kY

)(1 kY

)(2 kY

)(1 kYM

0H

1H

2H

1MH

)(nxM-to-1

M-to-1

M-to-1

M-to-1

)(0 ky

)(1 ky

)(2 ky

)(1 kyM

10

Filter Bank Sensing 11

A/D M-point ID

FT Filter B

ank

RF IF

Local OSC

sf 2

Decisions

2

00 T

11 T

11 PUPU NN T

10 /HH

0Y

21Y

1MY

The composite signal of multiple PU (primary user) sub-channels is down-converted into IF frequency. Then, the passband signal is sampled by wideband ADC and fed into the filter bank processing block.

The original idea is the minimum complexity design where the user sub-channels correspond exactly to the sub-bands (M=(Npu+1)/2).

Energy detector is followed by filter bank output and hypothesis test is performed.

Sub-band can be thought equivalently as

Test statistics for m-th sub-band

Test statistics for m-th primary user with Sp sub-bands

Energy Detection 12

MNN

)( mffH )( fX

M-to-1mY

m

m

u

li

N

ki kY

1

0

2)(pT

M: # of sub-bands

0

2

H:2

nGmT

22~ N

Filter gain

422 ,~ npnpp GNSGNS NT

1 ppp luS

1

0

2)(N

ki kYmT

False alarm probability for p-th primary user

Threshold level is determined by

Detection probability for p-th primary user

Energy Detection (2) 13

2

2

FAnp

npp,p

GNS

GNSQP

2FA

1npp,pp GNSNSPQ

22

22FA

1

22

22

D

snp

spnp,p

snp

snpp,p

NS

NSNSPQQ

GNS

GNSQP

Signal is supposed to be Gaussian process as well

14

Wideband Sensing Framework Filter bank can provide efficient bandpass filter

implementation with low spectrum power leakage (good false alarm performance)

Multi-channel sensing architecture based on poly-phase DFT filter bank followed by energy detector with minimum complexity Sub-channels have the same bandwidth (single

resolution) Wideband RF frontend license free radio using DTV bands in US(76-88, 174~216, 470~608, 614~698MHz)

…3836…1413…7 5165

76 - 88MHz 614 - 698MHz174 - 216MHz 470 - 608MHz

6 MHz DTV bands for spectrum sharing with unlicensed devices

15DTV bands for spectrum sharing with unlicensed devices

IEICE Trans. Commun. Vol. J91-B, No. 11, pp. 1351-1358

In case of DTV channel, the sampling rate at about 1.3GHz is necessary

Prototype filter :Energy in passband is maximized (Prolate sequence filter)

Wideband Sensing Framework 16

0 5 10 15 20-60

-50

-40

-30

-20

-10

0

Frequency [MHz]

Leve

l [dB

]

ISDB-T signalfilter bankPSE

PU1 PU2 PU3

RF signal BW : 18 MHz (=6MHz x 3) The composite RF signal with 3 PU sub-channels is down-converted into IF centered at 12MHz (=fs/4). Processing with passband signal（Real signal）

“Prolate spheroidal” or “Prolate” or “Slepian” sequence Real sequence of finite length and unit energy with the

minimum energy in the specified region Two parameters : Length L and

Prolate Sequence 17

2)( jeZ

1)(1 subject to

)(1 Maximize

0

2

0

2

deZ

deZ

j

j

1 subject to Maximize

1

1

ZZPZZ

Z is the eigenvector corresponding to max eigenvalue

18Simulation Specifications

Frequency

Noise level

PU 1 PU 2 PU 3

RF signal BW : 18 MHz (=6MHz x 3) The composite RF signal with 3 PU sub-channels is down-converted into IF centered at 12MHz (=fs/4). Processing with passband signal

19

Prob. Detection (Pd)

理想的（サブバンド分離が完全）にはどの手法も同一性能になる

PSEの隣接ユーザチャネルのノイズを拾ってしまうため，性能が劣化している（隣接ユーザチャネルは空いている場合)

フィルタバンクは理論性能に近い

-25 -20 -15 -10 -5 0 50

0.2

0.4

0.6

0.8

1

P D

SNR [dB]

FBPSEFB/PSE theoreticalSequential SimulatedSequential theoretical

Frequency

Noise level

PU 1 PU 2 PU 3

H1

001V

Frequency

(Proposed)

Pfa = 0.05

20

Prob. False Alarm（Pfa）

PSEの場合，隣接ユーザチャネルの信号電力を拾ってしまうため，隣接ユーザチャネル電力が大きくなるに連れて誤警報確率が高くなってしまう

フィルタバンクの場合，性能劣化無し

-25 -20 -15 -10 -5 0 50

0.2

0.4

0.6

0.8

1

P FA

Ps,neighbor/Pn [dB]

V=[001] FBV=[010] FBV=[011] FBV=[001] PSEV=[010] PSEV=[011] PSEPFA = 0.05 Frequency

Noise level

PU 1 PU 2 PU 3

H0

320 vvV

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Prob. False Alarm（Pfa）(2)

PSEの分解能を向上し，隣接ユーザチャネルの影響を抑えることはある程度可能であるが，計算負荷が増加する

-25 -20 -15 -10 -5 0 50

0.2

0.4

0.6

0.8

1P FA

Ps,neighbor/Pn [dB]

V=[010] PSE (S=1)V=[010] PSE (S=4)V=[010] PSE (S=8)PFA = 0.05

Frequency

Noise level

PU 1 PU 2 PU 3 320 vvV

H0

3 4 5 60

2

4

6

8

10

log2(P)

Com

plex

ity R

atio

(CFB

/CPS

E)

S=1S=2S=4S=6S=8

22Complexity

P: # of sub-channels

S: # of sub-bands within sub-channels (PSE only)

S=1

S=2

S=4S=6S=8

23Summary Efficient wideband sensing scheme for multiple

sub-channels proposed By using poly-phase DFT filter bank, optimal

bandpass filter can be implemented Practical scenario (DTV in US) Future works

Optimum filter design Assessment in practical environment Dynamic range Optimum sensing strategy