CC2420 Channel and RSSI Evaluation Nov/22/2006 Dept. of EECS, UC Berkeley C O nnect vityLab i

CC2420 Channel and RSSI Evaluation

Nov/22/2006

Dept. of EECS, UC Berkeley

COnnect vity Labi

Contents

Introduction

Chipcon CC2420 Radio

CC2420 RSSI Evaluation

The Comparison of Different Methods

Introduction

0 1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1The CDF of Different RSS Based Indoor Positiong Methods (Note: Different Experiment Environments)

Error (m)

CD

F

MP&AP Perspective + Kalman FilterHistogram Bayseian ApproachAdaptive TemporalNovel ToF(Steven Lanzisera - UC Berkeley)Hours (Cluster&Cluster Key)Ekahau SystemParticle Filter (Median error)A System for LEASE (Median error)ECOLOCATION (Max error)

RSS values measured by COTS wireless systems are sufficiently rich in information to permit a mobile device locates itself,

reliably and accurately

The most important trade off is between Offline Calibration and accuracy

Continuing The Project

Next Steps

Last Final Conclusion

Sensors Evaluation+

Fingerprinting

Distributed Estimation(Study, Modeling,

Simulation, Implementation)

Radio Map Independent Algorithm (Study, Modeling

Simulation, Implementation)

Tx1

Rx1Rx2

Rx3

The Goal of this Part of the project

Last Final Conclusion

Become familiar with our sensors and evaluate them as much as possible

Understand the behavior of RSSI in indoor environments

Measure the effect of different parameters on RSSI

Survey the existence of correlation in RSSI Samples and base band received signals

Study the theory of indoor wireless channels (as much as it was needed)

Gathering a lot of data in different situations and environment

Analysis of these data

Performed Works


The Chipcon CC2420 Radio Chip Characteristics

Frequency: 2.4 GHz

Communication Technology: Direct Sequence Spread Spectrum

Data Rate: 250 kbps

Total BW: 83 MHz

Output power: Programmable (Max Power = 0 dBm)

The CC2420 is an IEEE 802.15.4 compliant RF transceiver designed for low-power and low-voltage wireless applications

The CC2420

Range (Indoor) : 30 m



The CC2420

IF: 2 MHz

2 Sym: 4 Bits 32 Chips

Tc = 0.5 us

Pulse Shaping: Half SineEach Byte

The RSSI value is always averaged over 8 symbols periods (128 us) and digitized by an 8 bit ADC



RSSI and LQI in The CC2420

The Link Quality Indication (LQI) measurements is a characterization of the strength and/or quality of received packet

RSSI and LQI values are not necessarily linked; but Low LQI Invalid RSSI

The Chipcon CC2420 Radio Chip Characteristics The Chipcon CC2420 Radio Chip Characteristics

The values of RSSI could really affected by other people working with motes

Although there is no direct interference with the Wi-Fi channels, the channel quality can be affected by the overload and thus distort some

of the packets


Telos Mote Experiment, Stockholm, 2005

The Effect of Radio Interference

Measurements with the same motes at the same location, same batteries show a difference up to a few dBm (from new motes to

old motes)

The Effect of the Motes Themselves

RMS Delay Spread

CC2420 Wireless Channel Characteristics

Delay Spread

ns30

nsnsnsmm

Td 973.310010*3

1

10*3

3088

pathshortestandlongestjittT jiji

d :,,)()(max,

MHzT

Wd

C 7.52

1

MHzMHzW BWSignal 7.52 Channel is flat fading and a single channel filter tap is sufficient to model the channel

RMS Delay Spread

5.06.65

1

9.066050

1

FCFMHz

FCFKHz

WC

F=2.4 GHz

Flat Fading

Frequency Selective

Corry Hall Corridor


Coherence Time

smvms

smvms

vvf

C

fT

cdCohe /3.176

/36.17

104.2

103423.423.

423.9

8

1375

137

128 sRSSI

TroundNoRSSIS

MeasTime

Cohe

The maximum number of

stable RSSI samples

In our system, there is one RSSI sample per packet

In this network configuration the RSSI rate is 26.3 sample/second per device

CC2420 Wireless Channel Characteristics

25*tan

cmtyUserVelociCohTimeceDisCoherence


Indoor Path Loss Model

Path Loss Model


One sensor put at different distances (at the ~same time/Corridor)

Test Scenario

Run: RSSI_FigAvgVarHist.m at C:\ZigBeeNodes\CollectedData\Corridor_072106\Test1_QP

TxRx

1d

2d


Path Loss Model


Test Scenario

Conclusions

- Constant offset is not -45 dBm

- Path Loss almost follows the Log-Normal Model

- The effect of multipath is remarkable

- Something is wrong in these sensors (Interference/Sensors Board)

Worth Case

n

d

dPL

04

0


0 2 4 6 8 10 12 14 16 18 20-100

-80

-60

-40

-20

0

Distance (ft)

Pat

h Lo

ss (

dB)

PathLoss vs Distance in Quiet Corridor

RSSI(d) = Pt(dBm) - PL(D0) - 10nlog10(d/D0) + Noise

RSSI(d) = -17.112 - 36.3236 - 1.85*10log10(d/3(ft))

RSSI(d) = -17.112 - 57.0773 - 1.69*10log10(d/3(ft))

Theoritical PLPractical PLReal

0 2 4 6 8 10 12 14 16 18 20-100

-80

-60

-40

-20

0

Distance (ft)

Pat

h Lo

ss (

dB)

PathLoss vs Distance in the Presense of People at Corridor

RSSI(d) = Pt(dBm) - PL(D0) - 10nlog10(d/D0) + Noise

RSSI(d) = -17.112 - 34.9492 - 1.78*10log10(d/3(ft))

RSSI(d) = -17.112 - 50.016 - 1.64*10log10(d/3(ft))

Theoritical PLPractical PLReal

-17 dbm


Path Loss Model

Test Scenario

0 2 4 6 8 10 12 14 16 18 20-80

-60

-40

-20

0

20

Distance (ft)

RS

SI

Mea

n

RSSI Mean vs Distance In The Corridor

Max Err = 9.6015 & Mean Err = 3.1511

Quiet EnvPeople|Mean Err|

0 2 4 6 8 10 12 14 16 18 200

1

2

3

4

5

Distance (ft)

RS

SI

Var

RSSI SD vs Distance In The Corridor

Quiet EnvPeople




Path Loss Model

Test ScenarioOne sensor put at different distances (at the ~same time/Corridor)


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

-80

-70

-60

-50

-40

-30

-20

Box-Whisker Plot, Env: Quiet Corridor, Black Circle: Mean Value

RS

SI

(dB

m)

Distance (ft)

200 RSSI


Path Loss Model

Test ScenarioOne sensor put at different distances (at the ~same time/Corridor)


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

-80

-70

-60

-50

-40

-30

-20

Box-Whisker Plot, Env: Not Quiet Corridor, Black Circle: Mean ValueR

SS

I (d

Bm

)

Distance (ft)

200 RSSI


Path Loss Model


Test Scenario

Conclusions

-Something is wrong in these sensors

- The effect of interference

Worth Case

Our Sensors

(Outdoor)

0.20

0.05

1.90

9.17

0.91

1.60

Our Sensors (Indoor)

0.02

0.20

2.17

19.20

2.40

7.68

Stanford Sensors

(Indoor & 100 Sensors )

0.0 13.77 .02822

Min Var Max Var Avg Var

dBm

Variance Test

Mica Z


Outdoor Path Loss Model

Path Loss Model

Two sensors put at different distances (at the ~same time/Soccer Court)

Test Scenario

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5-100

-80

-60

-40

-20

0

Distance (ft)

RS

SI

Mea

n (d

B)

RSSI Mean vs Distance In Large Outdoor Env

Device 1Dev1-LogNrmPLDevice 2Dev2-LogNrmPL

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5-80

-60

-40

-20

0

20

Distance (ft)

RS

SI

Mea

n (d

B)

RSSI Mean vs Distance In Large Outdoor Env After Offset Compensation, Avg-PrcThrDiff ~ 2.5 dB

Device 1Dev1-LogNrmPLDevice 2Dev2-LogNrmPL

Conclusions

- Constant offset is not -45 dBm

- Path Loss almost well follows the Log-Normal Model

- In the outdoor these sensors have almost same behavior

- There is some offset between sensors, so calibration is necessary


Outdoor Path Loss Model

Path Loss Model

Two sensors put at different distances (at the ~same time/Soccer Court)

Test Scenario

1 2 3 4 5 6 7

-80

-60

-40

-20

Box-Whisker Plot, Dev 1, Env: OutDoor

RS

SI

(dB

m)

Distance (ft)

1 2 3 4 5 6 7

-80

-60

-40

-20

Box-Whisker Plot, Dev 2, Env: OutDoor

RS

SI

(dB

m)

Distance (ft)


400 RSSI

200 RSSI

Height : 1.22 m height difference 6 dBm power difference

S1: 4.6022 S2: 1.9371 S3: 0.0090

S4: 11.2586 S5: 7.6377

S6: 2.0158

4.5767 dBm

S1: 4.1134 S2: 7.8446

S3: 12.3849 S4: 12.9487

S5: 0.4060 S6: 7.2592

The Effect of Height

The Effect of Different Parameters on RSSI

Two different sets of data at two different height with 1.22 m height difference was compared (Test1 & Test2 was done at two different time/Env-Conditions)

Test Scenario

Test 1AvgErr AbsMeanErr

7.4928 dBm

Test 2AvgErr AbsMeanErr

All in dBm

All in dBmReported Error for the Same Chip is 10

dBm for 1m height difference


The Effect of Window

Test ScenarioTwo different sets of data at two different situations (at the same place, time and Env-

condition) was compared

Test

AbsMeanErr (All in dBm)

Window : Open/Closed 0.6 dBm power difference

S1: 1.9241 S2: 0.0140 S3: 0.9735 S4: 0.5581 S5: 0.0630 S6: 0.2506

Close to Window



The Effect of Door



Test


Door : Open/Closed 1.5 dBm power difference

S1: 3.5996 S2: 0.0005 S3: 0.7787 S4: 1.9850 S5: 1.9377 S6: 0.0354

Close to Door



- Due to the need of calibration, error should be lower that this


The Effect of Lights



Lights : On/Off 1.7 dBm power difference

S1: 7.7846 S2: 0.0319 S3: 1.0982 S4: 0.8703 S5: 0.5434 S6: 0.0461

Test


In the order of several 0.1 dBm



Radiated pattern Vertical mounting Radiated pattern horizontal mounting

Se

ns

ors

(T

x)

Mo

bile

No

de

(Rx)

Lig

ht

Fra

me2

Lig

ht

Fra

me1

12

6

34

5

BrdWal

Win

Dor

The Effect of Antenna Direction

Test ScenarioTwo different sets of data at two different antenna directions (at the same place, time

and Env-condition) was compared


Test

AbsMeanErr (All in dBm) 5.5 dBm power difference


?


Power Distribution

Feasibility Test

Test ScenarioData Received by coordinator located under each sensor (at the time and Env-


02

4

05

100

50

100

Device 1 & Direction: Brd

-RS

SI

(dB

)

02

4

05

100

50

100


-RS

SI

(dB

)

02

4

05

100

50

100


-RS

SI

(dB

)

02

4

05

100

50

100


-RS

SI

(dB

)

02

4

05

100

50

100


-RS

SI

(dB

)

02

4

05

100

50

100


-RS

SI

(dB

)

Conclusions

- Due to the need of calibration, there is some uncertainty in these results.


Basic Fingerprinting

)]([),,()([ VectorRSSmeanTxPwDirectionSensorIDIndexCellPosition lkji

Radio Map Elements

By the offset compensation of sensors we can improve the performance

48 runs in different situations (Quiet and Non-Quiet)

Sensors

Mobile Node

Light Frame2

Light Frame1

126

345

1.65 m2.44 m

1 2 3 4 5 6 3 1 4 2 24 4 6 6 5 1 3 3 6 3 4 5 6 4 1 3 1 6 5 2 5 5 3 4 3 6 3 4 5 6 4 3 3 1 6 5 2 5 5 4 4

Expected PositionEstimated Position (SQR)

Estimated Position (ABS)

Max Error 2.44m

Static Test

MobileTest

4 4 4 4 4 4 3 3 2 2 2 3 3 3 3 1 3 1 3 2 6 2 5 5 2 2 2 2 2 6 6 6 6 6 2 2 2 5 2 2 2 6 2 2 2 2 2 2 2 2 2 2 2 2

Estimated Position:



Expected Position: 3 4(2) 5(6) 6(5) 2 1 3 4(2) 5(6) 6(5) 2 1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9-45

-40

-35

-30

-25

-20

-15

-10

-5

0

5

Distance (m)

Rec

eive

d P

ower

(dB

m)n=2.2, AvgVar= 0.05 & MaxVar= 0.25

Practical PLTheortical PL|Thr-Prc|

NI Power samples

NI RSSI Evaluation

Thank You

The End

Box Whisker Plot (N(0,sigma))

Documents

CC2420 Channel and RSSI Evaluation Nov/22/2006 Dept. of EECS, UC Berkeley C O nnect vityLab i