multistage spectrum sensing for cognitive radios

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Multistage Spectrum Sensing for Cognitive Radios UCLA CORES

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Multistage Spectrum Sensing for Cognitive Radios. UCLA CORES. Outline. Introduction Problem Statement Proposal Markov Chain Model Results. Introduction to Spectrum Sensing. Spectral Vacancy. Spectral Vacancy. Spectral Vacancy. The Frequency Spectrum is mostly allocated - PowerPoint PPT Presentation

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Page 1: Multistage Spectrum Sensing for Cognitive Radios

Multistage Spectrum Sensing for Cognitive Radios

UCLA CORES

Page 2: Multistage Spectrum Sensing for Cognitive Radios

Outline

1. Introduction

2. Problem Statement

3. Proposal

4. Markov Chain Model

5. Results

2

Page 3: Multistage Spectrum Sensing for Cognitive Radios

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

Page 4: Multistage Spectrum Sensing for Cognitive Radios

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

Page 5: Multistage Spectrum Sensing for Cognitive Radios

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

Page 6: Multistage Spectrum Sensing for Cognitive Radios

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

Page 7: Multistage Spectrum Sensing for Cognitive Radios

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

Page 8: Multistage Spectrum Sensing for Cognitive Radios

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

Page 9: Multistage Spectrum Sensing for Cognitive Radios

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

Page 10: Multistage Spectrum Sensing for Cognitive Radios

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

Page 11: Multistage Spectrum Sensing for Cognitive Radios

Model OverviewModel is divided into 3 levels:

1.CR traffic level

2.Multistage sensing level

3.Spectrum Sensing level

11

Page 12: Multistage Spectrum Sensing for Cognitive Radios

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)

Page 13: Multistage Spectrum Sensing for Cognitive Radios

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

Page 14: Multistage Spectrum Sensing for Cognitive Radios

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

Page 15: Multistage Spectrum Sensing for Cognitive Radios

1.

2.

Implementation – BandwidthNarrowband Sensing, N Channels

Page 16: Multistage Spectrum Sensing for Cognitive Radios

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

Page 17: Multistage Spectrum Sensing for Cognitive Radios

Throughput for the Narrowband Case

17

Simulation Configuration

Page 18: Multistage Spectrum Sensing for Cognitive Radios

Delay in finding PU

18

Simulation Configuration

Page 19: Multistage Spectrum Sensing for Cognitive Radios

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:

Page 20: Multistage Spectrum Sensing for Cognitive Radios

Throughput for the Wideband Case

20

Simulation Configuration

Page 21: Multistage Spectrum Sensing for Cognitive Radios

Appendix

21

Page 22: Multistage Spectrum Sensing for Cognitive Radios

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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