doc.: IEEE Submission Sep 2008 Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs) Submission Title: Channel modeling for medical implanted communication systems by numerical simulation Date Submitted: [xx Sep, 2008] Source: Jaehwan Kim[ETRI], HyungSoo Lee[ETRI], Jeong Ki Pack[CNU], Tae Hong Kim[CNU] Contact: Jae Whan Kim, ETRI, Korea Voice: : , Re: [n/a] Abstract: Provide needs of channel modeling for medical implanted communication system Purpose: To provide basic channel characteristics for the manufacture of medical implantable communication system Notice:This document has been prepared to assist the IEEE P 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 reserves 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 maybe made publicly available by P Slide 1

doc.: IEEE Submission Sep 2008 Contents Channel models for BAN Methods for channel modeling Channel modeling 1 Channel modeling 2 Channel modeling 3 Conclusions Slide 2

doc.: IEEE Submission ScenarioDescriptionFrequency BandChannel Model S1Implant to Implant MHzCM1 S2Implant to Body Surface MHzCM2 S3Implant to External MHzCM2 S4Body Surface to Body Surface (LOS)TBD (f 1,… f n )CM3 S5Body Surface to Body Surface (NLOS)TBD (f 1,… f n )CM3 S6Body Surface to External (LOS)TBD (f 1,… f n )CM4 S7Body Surface to External (NLOS)TBD (f 1,… f n )CM4 Channel models for WBAN Sep 2008 Slide 3

doc.: IEEE Submission Sep 2008 Channel modeling - Scenario 1(CM1), Scenario 2(CM2 ) - FDTD method was used for channel modeling using Remcom XFDTD Frequency: MHz Human body model - Korean male phantom model (voxel size : 3 mm) TX antenna - Hertzian dipole - Channel models must not be affected by the transmitting antenna pattern. So the proper compensation for directive gain as well as polarization are needed. Methods for channel modeling Slide 4

doc.: IEEE Submission Sep 2008 Simulation scenario LocationDevice Deep tissueCapsule endoscope, Capsule for drug delivery(1-4, 6-9) Near surface Glucose-Insulin(10-11), Insulin pump(12-13) Pacemaker(14) Deep brain stimulator, Parkinson’s disease, Cortical stimulator, Visual neuro-stimulator, Audio-neuro stimulator(15-16) Central-nerve stimulator(11) Healthcare shoes (17) − Transmitter location : 17 positions  Near surface implants : 9  Deep tissue implants : 8 Slide − Receiver location : 138 points  Implants(in-body) : 78  Body surface : 60

doc.: IEEE Submission Sep 2008 Slide 6 Channel modeling 1 Path loss model - Path loss model used in IEEE P PL(d)=PL(d 0 )+10nlog 10 (d/d 0 )+S [dB]  d 0 : reference distance, 50 mm  n : path loss exponent  S : random scatter around the regression line, N(0, σ s ) Channel modeling - Grouping of the transmitter location  Deep tissue implant  Near surface implant - Grouping of the receiver location  In-body (implant)  Body surface : from the skin to 2 cm away from the skin

doc.: IEEE Submission Sep 2008 Slide 7 Deep tissue implant to another implant (the maximum path length is about 180 cm) Near surface implant to another implant (the maximum path length is about 180 cm) ( n=4.33, PL(d0)=47.70, σ s =5.77 ) (n=3.65, PL(d0)=46.52, σ s =8.84) Path Loss vs. Distance Scatter Plot(CM1)

doc.: IEEE Submission Sep 2008 Slide 8 Near surface implant to body surface (the maximum path length is about 180 cm) Deep tissue implant to body surface (the maximum path length is about 180 cm) (n=3.53, PL(d0)=45.37, σ s =9.46) (n=5.22, PL(d0)=37.28, σ s =5.76) Path Loss vs. Distance Scatter Plot(CM2)

doc.: IEEE Submission Sep 2008 (n=6.17, PL(d0)=34.89, σ s =5.44) Slide 9 Deep tissue implant to another implant (fitted up to 50 cm) (n=5.34, PL(d0)=35.48, σ s =8.42) Near surface implant to another implant (fitted up to 50 cm) Path Loss vs. Distance Scatter Plot(CM1)

doc.: IEEE Submission Sep 2008 (n=4.34, PL(d0)=40.17, σ s =10.09) Slide 10 Near surface implant to body surface (fitted up to 50 cm) (n=6.06, PL(d0)=31.95, σ s =5.75) Deep tissue implant to body surface (fitted up to 50 cm) Path Loss vs. Distance Scatter Plot(CM2)

doc.: IEEE Submission Sep 2008 Slide 11 When we fit the path loss model for the whole receiver locations (the maximum path length is about 180 cm), the modeling parameters are slightly different from the results of IEEE However, the parameter values are well within the statistical error bound (one σ s value). Summary of the channel modeling 1 Implant to ImplantPL(d0)nσsσs Deep tissue Near surface Implant to body surfacePL(d0)nσsσs Deep tissue Near surface

doc.: IEEE Submission Sep 2008 Slide 12 When fitted up to 50 cm as shown in IEEE , we obtained similar results as in IEEE P Implant to ImplantPL(d0)nσsσs Deep tissue Near surface Implant to body surfacePL(d0)nσsσs Deep tissue Near surface

doc.: IEEE Submission Sep 2008 Slide 13 Channel modeling 2 Path loss model - Same as the model used in IEEE P Channel modeling - We tried channel modeling for different scenarios (different grouping of TX’s or RX’s). - Modeling 2A  Path loss was modeled for total implant locations (i. e., the whole TX’s are grouped together for fitting) - Modeling 2B  Path loss was modeled for the total implant locations with the RX points grouped differently.  Receiver groups: head, trunk, lower parts of the body, arms - Modeling 2C  Path loss was modeled for the type of the transmitting implanted devices (Implant to Implant)  Type of devices : capsule endoscope, glucose-insulin, pacemaker, deep brain stimulator, healthcare shoes

doc.: IEEE Submission Sep 2008 (n=3.83, PL(d0)=48.46, σ s =8.53) Slide 14 Total implant(deep tissue and near surface) to implant Path Loss vs. Distance Scatter Plot(2A) (n=4.01,PL(d0)=43.97, σ s =8.89) Total implant(deep tissue and near surface) to body surface

doc.: IEEE Submission Sep 2008 Slide 15 Summary of the channel modeling 2A Modeling parameters fitted for all transmitting implanted devices PL(d0)nσsσs Implant to Implant Implant to Body surface

doc.: IEEE Submission Sep 2008 (n=4.03, PL(d0)=47.11, σ s =8.65 ) (n=3.75, PL(d0)=47.89, σ s =9.78) Slide 16 Implant to head Implant to lower parts of the body Path Loss vs. Distance Scatter Plot(2B)

doc.: IEEE Submission Sep 2008 (n=4.26, PL(d0)=44.99, σ s =8.21 ) (n=3.81, PL(d0)=48.02, σ s =7.97) Slide 17 Implant to trunk Implant to arms Path Loss vs. Distance Scatter Plot(2B)

doc.: IEEE Submission Sep 2008 Head Lower parts of the body TrunkArms PL(d0) n σsσs Slide 18 Summary of the channel modeling 2B Modeling parameters for different RX locations ( Implant to Implant)

doc.: IEEE Submission Sep 2008 (n=4.48, PL(d0)=45.57, σ s =6.42 ) (n=3.23, PL(d0)=43.52, σ s =6.46) Slide 19 Capsule endoscope (gullet) Path Loss vs. Distance Scatter Plot(2C) Glucose Insulin (right hand)

doc.: IEEE Submission Sep 2008 (n=4.29, PL(d0)=46.75, σ s =6.55 ) (n=3.60, PL(d0)=47.96, σ s =10.07) Slide 20 Pacemaker (heart) Path Loss vs. Distance Scatter Plot(2C) Deep brain stimulator (right throat)

doc.: IEEE Submission Sep 2008 (n=3.83, PL(d0)=36.72, σ s =5.54 ) Slide 21 Healthcare shoes (sole) Path Loss vs. Distance Scatter Plot(2C)

doc.: IEEE Submission Sep 2008 Summary of the channel modeling 2C Capsule endoscope (gullet) Glucose- Insulin (right hand) Pacemaker (heart) Deep brain stimulator (right throat) Healthcare shoes (sole) PL(d0) n σsσs Slide 22 Modeling parameters for different transmitting implanted devices (Implant to Implant)

doc.: IEEE Submission Sep 2008 Slide 23 We have modeled WBAN channels for three different modeling scenarios(2A, 2B, 2C). The results of the modeling scenario 2A are similar to those of the channel modeling 1. Thus, it seems that the modeling scenario in IEEE could be simplified. The results of the modeling scenario 2B show that there are no large difference in the model parameters for different body parts. So, the modeling scenario in IEEE seems to be good. Summaries of the channel modeling 2

doc.: IEEE Submission Sep 2008 Slide 24 When we classify the transmitting implants in more detail(modeling scenario 2C), the parameter values are similar to the results of the modeling scenario 2A. Thus, the detailed classification of the transmitting implants does not seem to be necessary.

doc.: IEEE Submission Sep 2008 Path loss model - PL(d)=PL(d 0 )+10nlog 10 (d/d 0 )+ a*d +S [dB]  d 0 : reference distance, 50 mm  a : coefficient for absorption loss  n : path loss exponent, N(0, σ s )  S : random scatter around the regression line - The absorption loss of biological tissues is very large. Because the absorption loss is a linear term in a log scale, we tried a modified path loss model by adding the first order term to WBAN channel more accurately. Channel modeling - Same as in the channel modeling 1 Slide 25 Channel modeling 3

doc.: IEEE Submission Sep 2008 Path loss vs. Distance Scatter Plot(CM1) Slide 26 Deep tissue implant to another implant Near surface implant to another implant (n=5.87, PL(d0)=36.80, a=-19.70, σ s =8.22) (n=7.39, PL(d0)=33.58, a=-28.00, σ s =5.07)

doc.: IEEE Submission Sep 2008 Path loss vs. Distance Scatter Plot(CM2) Slide 27 Near surface implant to body surface Deep tissue implant to body surface (n=4.89, pl=39.83, a=-13.44, σ s =9.30) (n=7.38, PL(d0)=29.37, a=-24.96, σ s =5.45)

doc.: IEEE Submission Sep 2008 Slide 28 The comparison of the results of the channel modeling 3 shows that the modified pass loss model seems to be better, in terms of the modeling accuracy (i. e. standard deviation σ s ). Implant to ImplantPL(d0)naσsσs Deep tissue Near surface Implant to body surfacePL(d0)naσsσs Deep tissue Near surface Summary of the channel modeling 3

doc.: IEEE Submission Sep 2008 Conclusions We have modeled WBAN channel by two path loss models. We also tried different scenarios for channel modeling. Extensive simulation to characterize the MICS path loss has been performed and statistical path loss models at MHz are derived. The models are based on 9 near surface implants and 8 deep tissue implants for the Korean male phantom model. Slide 29

doc.: IEEE Submission Sep 2008 The modeling results show that  Classification of the implants to two groups(deep tissue and near surface) might not be necessary.  The modified path loss model seems to work better.  The modeling parameters could be different depending on the path length for fitting, the location of TX’s or RX’s, but they are well within the statistical error bound (one σ s value). Slide 30

doc.: IEEE Submission Sep 2008 Slide 31 References Doc : A statistical path loss model for MICS Doc : Channel model for body area network (BAN)