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Multistage Spectrum Sensing for Cognitive Radios
UCLA CORES
Outline
1. Introduction
2. Problem Statement
3. Proposal
4. Markov Chain Model
5. Results
2
Spectral Vacancy
Frequency
PSD
Spectral Vacancy
Introduction to Spectrum Sensing
The Frequency Spectrum is mostly allocated Spectral vacancies exist in:
1. Unallocated frequency bands.
2. Allocated bands where the Primary Users (PUs) are spatially absent or temporarily idle.
Cognitive radios (CRs) find spectral vacancies by performing spectrum sensing. Spectrum Sensing Design Objectives
1. Maximize the CR throughput
2. Minimize delay in vacating channel for an incoming PU
3. Minimize collisions between CRs and PUs
3
Spectral Vacancy
Cognitive Radio
OSI Model of Cognitive Radios
4
Collaborative Sensing
Application
BandwidthMAC
PHY
RF
PHY
RF
MAC
Sensing Method
Design Parameters:
1. Sensing Algorithm
2. Sensing Time
Design Parameters:
1. Narrowband
2. Wideband
.
.
.
PU Traffic
Conventional Single Stage Sensing
i. No PU
ii. PU arrives
Problem Statement
5
CR Active Sensing CR Active Sensing CR Active Sensing
Time
CR Active Sensing
No FA
FalseAlarm (FA)
-Throughput Waste-
CR Stops
Transmission
CR Active Sensing
MD
-Collision with PU--Delay in detecting PU-
No Misdetection
(MD) CR Stops
Transmission
PU
0 T 2T 3TS S S
Multistage Sensing
6
i. No PU
ii. PU arrives
Degrees of freedom:1. Number of Sensing Stages (S)
2. Sensing Methods
3. Sensing Times
CR ActiveSensingStage 1
No FA
FA CR Stops
Transmission
No FA
CR ActiveSensingStage 2
CR ActiveSensingStage S
No FA
FA FA
PU
S1 S2 SS
CR ActiveSensingStage 1
MD
No MD CR Stops
Transmission
MD
CR ActiveSensingStage 2
CR ActiveSensingStage S
MD
No MD
No MD
S1 S2 SS
Previous Work
802.22 features 2-stage sensing: Coarse and Fine sensing.
Jeon et al* propose a multistage sensing algorithm* W. S. Jeon, D. G. Jeong, J. A. Han, G. Ko, and M. S. Song, "An efficient quiet period management scheme for cognitive
radio systems," IEEE Trans. Wireless Comm., vol. 7, no. 2, Feb. 2008.
Limited model; single channel, single sensing algorithm, no collaboration, simple traffic model.
Literature lacks a unified analytical framework that includes Multistage Sensing
7
ProposalWe introduce a unified analytical framework that models:
Multistage Sensing Number of sensing stages
Sensing Methods Algorithm Sensing Time
Bandwidth Narrowband Wideband
CR traffic models CBR – VBR Buffered – Unbuffered
Goal is to analyze the impact of varying parameters on:
1. CR throughput
2. Delay in vacating channel for a PU
3. CRs and PUs collisions
8
Discrete Time Markov Chain
Analysis is based on Markov Chain:Well established Math tool for modeling discrete space
stochastic processesFuture evolution of process depends solely on current
state.Provides closed form/numerical solutions for steady
state probabilities and process variables
9
Assumptions
1. PUs and CRs arrive and depart at discrete times that are multiples of T
2. CRs communicating together are synchronized through a control channel
3. Time taken to switch between communicating and sensing modes is negligible
10
Model OverviewModel is divided into 3 levels:
1.CR traffic level
2.Multistage sensing level
3.Spectrum Sensing level
11
ImplementationLevel 1 – CR Traffic Level
12
CR IdleCR Sensing
and/or
TransmittingPCR
1 - PCR
QCR
1 - QCR
PCR ≡ Probability of arrival of a CR
QCR ≡ Probability of departure of a CR
PCR and QCR are tuned to accommodate for different traffic models: CBR – VBR Buffered – Unbuffered
(Buffer Size)
ImplementationLevel 2 - Multistage Sensing Level
13
CR Sensing
and/or
Transmitting
S ≡ Number of Sensing Stages
CR Sensing
and/or
TransmittingCR Idle
PCR
1 - PCR
QCR
1 - QCR
CR Active
Stage 1
CR Active
Stage 2
CR Active
Stage SCR Quiet
PU detected
or False Alarm
PU misdetected
or no False Alarm
PU detected
or False Alarm
CR Active
Stage i
ImplementationLevel 1 – CR Traffic Level
ImplementationLevel 3 – Spectrum Sensing Level
14
CR Active
Stage 1
PU Absent
CR Active
Stage i
PU Absent
CR Active
Stage i + 1
PU Absent
CR Active
Stage 1
PU Present
CR Active
Stage I
PU Present
CR Active
Stage i + 1
PU Present
(1-PPU).Pi
(1-QPU).(1-Qi)
QPU.Pi
PPU.(1-Qi)
(1-PPU).(1-Pi)
(1-QPU).QiPPU ≡ Prob of arrival of a PU
QPU ≡ Prob of departure of a PU
Pi ≡ Prob of False Alarm at Stage i
Qi ≡ Prob of Misdetection at Stage i
PPU and QPU reflect the PU traffic model
Pi and Qi are tuned to describe the sensing method
CR Active
Stage i
CR Active
Stage i
CR Active
Stage 1
CR Active
Stage 2
CR Active
Stage SCR Quiet
ImplementationLevel 2 - Multistage Sensing Level
1.
2.
Implementation – BandwidthNarrowband Sensing, N Channels
16
CR
Idle
Sens.
/Tx
Ch 1
MSS
Ch 1
Quiet
Ch 2
MSS
Ch 2
Quiet
Ch N
MSS
Ch N
Quiet
CR
Idle
Sens.
/Tx
Ch 1
MSS
Ch 2
MSS
Ch N
MSS
Implementation 1: Implementation 2:
Time
CR QuietCR ActiveSensingStage 1
CR ActiveSensingStage 1
Example: Single stage, 3 channels:
Time
CR QuietCR ActiveSensingStage 1
CR ActiveSensingStage 1
CR ActiveSensingStage 1
Example: Single stage, 3 channels:
Implementation – BandwidthNarrowband Sensing, N Channels
Throughput for the Narrowband Case
17
Simulation Configuration
Delay in finding PU
18
Simulation Configuration
Implementation – BandwidthWideband Sensing
19
IDLE Tx
P
P
1-P 1-P
N Channels
Time
CR ActiveSensing
Stage 1CR Active
Sensing
Stage 2CR Active
Sensing
Stage 3CR Active
Sensing
Stage 4
CR ActiveSensing
Stage 3CR Active
Sensing
Stage 1CR Idle CR Active
Sensing
Stage 1
CR ActiveSensing
Stage 1CR Active
Sensing
Stage 1CR Quiet CR Quiet
Example: Single stage, 3 channels:
Throughput for the Wideband Case
20
Simulation Configuration
Appendix
21
Simulation Configuration
22
AWGN independent channels. Sensing time = , where i is the stage number, L0 = 50 us, Ts = 20 ms, and δ =
2. Sensing time for the last sensing stage = Ts.
Energy Detection parameters: Pr = -104 dBm.
Noise Floor = -163 dBm. BW = 6 MHz SNR = -8.8 dB. Energy Threshold = -94.74 dBm.
# of Sensing Nodes = 5.
SU Arrival Probability = 0.2. SU Departure Probability = 0.2. PU Arrival Probability = 0.05. PU Departure Probability = 0.05.
Switching time = 0.2xTs. 2 Channels 1000 stages. 10,000 cycles.
},min{ 0LTs i
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