bingxuan zhao wireless communication and satellite communication project ii shimamoto laboratory,...
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Bingxuan ZHAOWireless Communication and Satellite
Communication Project IIShimamoto Laboratory, GITS
Waseda University
Ph.D Academy
Spectrum Sensing for Wireless Networks
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Outline
Introduction
Cooperative Spectrum Sensing
Conclusion of the Dissertation
2
Current Status of Wireless Spectrum
Limited Supply vs. Growing Demand
http://www.lbl.gov/MicroWorlds/ALSTool/EMSpec/EMSpec2.html
3
Current Status of Wireless Spectrum
Scarcity vs. Largely Underutilized
http://en.wikipedia.org/wiki/Frequency_allocation
4 Cognitive Radio: improve spectrum utilization
http://www.its.bldrdoc.gov/isart/art06/slides06/mch_m/mch_m_slides.pdf
Research QuestionCooperative Spectrum SensingEffectively find the White Spaces: decrease PFA
Avoid interference with the PUs: decrease PMD
How to address the power uncertainty problem to decrease PFA and PMD5
Power
Frequency
Time
Spectrum Hole/White Space
Noise Power
Uncertainty
Local Sensing Techniques
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Spectrum Sensing Techniques
Primary Transmitter Detection Primary Receiver Detection
Non-Coherent Detection Coherent Detection
Matched Filter Detection
Energy Detection
Cyclostationary Detection
Wavelet Detection
Covariance Detection
Eigenvalue Detection
Simplicity
No prior-knowledge required
Most widely used
Energy Detection mechanism
BP Filter A/D Converter SquareAverage N Samples
DecisionTest
StatisticsReceiving
Signal
Presence
Absense
FFT SquareAverage M
bins N timesTest
StatisticsReceiving
SignalA/D Converter Decision
Presence
Absense
Time Domain
Frequency Domain
Cooperative Spectrum Sensing
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CR1
CR2
CR3
Multi-path fading
Shadowing
Primary Transmitter
Primary Receiver
CR4Receiver
Uncertainty
Fusion Center
Data Collocati
on
Data Processin
g
Data Reportin
g
Infer
Presence
Absence
Soft Combing: Data fusion, high performance, high BW requirement Hard Combing: Decision fusion, low performance, low BW requirement
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1 1K
if f
i
Q p
1
Ki
md mdi
Q p
Model of Inter-Channel Interference
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FrequencyPower spectral density
Ch 1 Ch 2 Ch 3 Ch 4 Ch 5
Bandwidth
Interference
Interference
TargetSignal
Superposed Power
Power Decomposition 1/3
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d is the distance between tx and r.Pt is the transmission power beta is the path loss exponentI(u,v) is the interference factor is constant
Superposed Power
Signal Power
Background Power
Interference Power
Interference Power
...
Number of primary transmitters in adjacent channels
Frequency
Channel v
Transmitter
Channel u
receiver
, ( , )t
r cP d r tx P I u v
The received power of the receiver, r, working on channel u produced by the transmitter, tx, working on channel v can be represented by:
c
ACM Sigmetrics
(2006)
Power Decomposition 2/3
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The total received power by a secondary user s(i,j):
Cluster 1
GatewayInternet
Cluster 2
Cluster 3
, ,
1
M
m
s i j s i j
m
p pl N
#
,,
s i jp s i j ND p
Mathematical
Transform
,1
, 2#
,
,1
, 2
,
s i
s i
s i Ni ii dN n
p Ns i
p Ns i
p Ns i N
D
Dp
D
*s i s iD p p
*1s i s ip D p
Performance Evaluation of Power Decomposition
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Power decomposition works well in low SINR with conventional method can not.Soft combination can achieve better performance than hard combination.Power decomposition can cope with the increase of the inter-channel interference.Power decomposition can achieve lower PFA, i.e., higher spectrum utilization.Power decomposition can achieve higher PD, lower interference with PUs
-2 -4 -6 -8 -10 -12 -14 -16 -18 -20 -220
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SINR(dB)
Prob
abil
ity
of f
alse
ala
rm
pf_con_hcpf_con_scpf_dec_hcpf_dec_sc
Conventionalmethod
Powerdecomposition
-2 -4 -6 -8 -10 -12 -14 -16 -18 -20 -220
0.1
0.2
0.3
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0.5
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0.9
1
SINR(dB)Pr
obab
ilit
y of
det
ecti
on
pd_con_hcpd_con_scpd_dec_hcpd_dec_sc
Powdecomposition
Conventionalmethod
Conclusion of Chapter 3
Proposed a power decomposition method: Non-coherent: depends only on distancesImprove spectrum utilizationDecrease interference with PUs
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