channel model for ieee 802.15

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May 2006 Z. Lai Slide 1 doc.: IEEE 802.15-06-0229-00-003c Submission Project: IEEE P802.15 Working Group for Wireless Personal Area N Project: IEEE P802.15 Working Group for Wireless Personal Area N etworks ( etworks ( WPANs WPANs ) ) Submission Title: [IBM Measured Data Analysis Revised] Date Submitted: [May 2006] Source: [Zhiguo Lai, University of Massachusetts Amherst, [email protected]] [Shahriar Emami, [email protected] ] [Brian Gaucher, IBM Research, [email protected]] [Thomas Zwick, [email protected]] [Abbie Mathew, NewLANS, [email protected]] Abstract: [] Purpose: [To update task group on channel modeling simulation work] Notice: This document has been prepared to assist the IEEE P802.15. It is offered as a basis for discussion and is not binding on the contributing individual(s) or organization(s). The material in this document is subject to change in form and content after further study. The contributor(s) reserve(s) the right to add, amend or withdraw material contained herein. Release: The contributor acknowledges and accepts that this contribution becomes the property of IEEE and may be made publicly available by P802.15.

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May 2006

Z. LaiSlide 1

doc.: IEEE 802.15-06-0229-00-003c

Submission

Project: IEEE P802.15 Working Group for Wireless Personal Area NProject: IEEE P802.15 Working Group for Wireless Personal Area Networks (etworks (WPANsWPANs))

Submission Title: [IBM Measured Data Analysis Revised]Date Submitted: [May 2006]Source: [Zhiguo Lai, University of Massachusetts Amherst, [email protected]]

[Shahriar Emami, [email protected] ] [Brian Gaucher, IBM Research, [email protected]][Thomas Zwick, [email protected]][Abbie Mathew, NewLANS, [email protected]]

Abstract: []Purpose: [To update task group on channel modeling simulation work]Notice: This document has been prepared to assist the IEEE P802.15. It is offered as a basis for discussion and is not binding on the contributing individual(s) or organization(s). The material in this document is subject to change in form and content after further study. The contributor(s) reserve(s) the right to add, amend or withdraw material contained herein.Release: The contributor acknowledges and accepts that this contribution becomes the property of IEEE and may be made publicly available by P802.15.

May 2006

Z. LaiSlide 2

doc.: IEEE 802.15-06-0229-00-003c

Submission

Recall: IBM Measured Dataref: 15-06-0191-00

Four environments:Four environments:•• Office: including cubicles and small conference rooms;Office: including cubicles and small conference rooms;•• Laboratory: highly reflective metallic equipment and walls;Laboratory: highly reflective metallic equipment and walls;•• Library: large hall;Library: large hall;•• Private home: wood/plasterboard construction.Private home: wood/plasterboard construction.

Frequency: 59 to 64 GHzFrequency: 59 to 64 GHzTime resolution: 0.2 nsTime resolution: 0.2 nsVertical polarized omni antennas on both endsVertical polarized omni antennas on both endsOver 700 channel measurementsOver 700 channel measurementsProposed CIR model: singleProposed CIR model: single--cluster Scluster S--V modelV modelProposed PDP model: exponential decay preceded by a constant parProposed PDP model: exponential decay preceded by a constant partt

CIR = Channel Impulse ResponseCIR = Channel Impulse Response

PDP = Power Delay ProfilePDP = Power Delay Profile

RDS = RMS Delay SpreadRDS = RMS Delay SpreadPow

er (d

B)

Delay (ns)breakτ

May 2006

Z. LaiSlide 3

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Office Environment

*Adapted from [Zwick, T., T. J. Beukema, H. Nam, Wideband Channel Sounder with Measurements and Model for the 60 GHz Indoor Radio Channel, IEEE Transactions on Vehicular Technology, Volume 54, Issue 4, 1266-1277, July 2005].

λ = 0.5 /ns

τbreak = 0.4 ns

τmax = 100 ns

γ = 8.33 ns

Using an Exponential Decay Preceded by a Constant Part*

May 2006

Z. LaiSlide 4

doc.: IEEE 802.15-06-0229-00-003c

Submission

Revised Model: S-V Model

∑∞

=

−=0

)()( :singlek

ki

kkeh ττδβτ φ

PDP model: single or multi-exponential decay

CIR model: single or multi-cluster S-V model

γτββ /20

2 :single kek−⋅=

(ns)factor decay cluster (ns)factor decay ray

(1/ns) rate arrivalcluster (1/ns) rate arrivalray

:ParametersMain

=Γ==Λ=

γ

λ

∑∑∞

=

=

−−=0 0

)()( :multil k

klli

klkleh τττδβτ φ

γττββ //200

2 :multi kll eekl−Γ− ⋅⋅=

Λparameter on with distributiPoisson : parameter on with distributiPoisson : ,

)[0,2over on distributi uniform : ,ondistributiRayleigh :,

l

klk

klk

klk

τλττ

πφφββ

May 2006

Z. LaiSlide 5

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Office Environment

Using Single-Cluster S-V Model

May 2006

Z. LaiSlide 6

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Office Environment

Using the Conventional Multi-Cluster S-V Model

May 2006

Z. LaiSlide 7

doc.: IEEE 802.15-06-0229-00-003c

Submission

Original fitting

λ = 0.5/ns

γ = 8.33 ns

Multi-cluster S-V

λ = 0.25/ns

Λ = 0.14/ns

γ = 2.2 ns

Γ = 8.3 ns

Single-cluster S-V

λ = 0.135/ns

γ = 7.95 ns

May 2006

Z. LaiSlide 8

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Laboratory Environment

Using Single-Cluster S-V Model

May 2006

Z. LaiSlide 9

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Laboratory Environment

Using the Conventional Multi-Cluster S-V Model

May 2006

Z. LaiSlide 10

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Library Environment

Using Single-Cluster S-V Model

May 2006

Z. LaiSlide 11

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Library Environment

Using the Conventional Multi-Cluster S-V Model

May 2006

Z. LaiSlide 12

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Private House Environment

Using Single-Cluster S-V Model

May 2006

Z. LaiSlide 13

doc.: IEEE 802.15-06-0229-00-003c

Submission

Mean Normalized PDP and Cumulative Probability of RDS For Private House EnvironmentUsing the Conventional Multi-Cluster S-V Model

May 2006

Z. LaiSlide 14

doc.: IEEE 802.15-06-0229-00-003c

Submission

Multipath Model Parameters

Office Laboratory Library Private home

Single Multi Single Multi Multi Multi0.13 0.65

0.15

1.5

4.2

rms delay (ns) 6.83 9.44 6.03 3.19

0.04

3.2

11.2

0.18

0.09

3.2

12.5

0.25

0.14

2.2

8.3

Single Single0.1

cluster arrival rate Λ (1/ns) - - - -

cluster decay factor Γ (ns) - - - -

11.8

9.99

200

ray arrival rate λ (1/ns) 0.135 0.045 0.22

ray decay factor γ (ns) 7.95 11.2 3.85

mean excess delay (ns) 7.01 7.85 3.39

maximum delay (ns)* 100 200 50

environmentsparameters

* The maximum delay used in the simulation to ensure the capture of all possible rays.

May 2006

Z. LaiSlide 15

doc.: IEEE 802.15-06-0229-00-003c

Submission

Four sets of measured data (office, laboratory, libratory, privaFour sets of measured data (office, laboratory, libratory, private house) te house) provided by IBM were fitted to Sprovided by IBM were fitted to S--V model.V model.Parameters (ray/cluster arrival rates, ray/cluster decay factorsParameters (ray/cluster arrival rates, ray/cluster decay factors, mean , mean excess delay, and excess delay, and rmsrms delay) were extracted.delay) were extracted.For the office environment, the new fitting (SFor the office environment, the new fitting (S--V model) is better than V model) is better than the original one (exponential decay preceded by a constant part)the original one (exponential decay preceded by a constant part)..SS--V model is well supported by IBM measured dataV model is well supported by IBM measured data.. For office and For office and private house environments, the measured data were equally well private house environments, the measured data were equally well fitted to the singlefitted to the single--cluster model and the multicluster model and the multi--cluster model. For cluster model. For laboratory and library environments, the multilaboratory and library environments, the multi--cluster model has a cluster model has a better fitting.better fitting.

Summary and Conclusions