The presentation introduces the research on IR-UWB that jointly exploited realtime link analysis with non-deterministic renewable energy characteristics and developed adaptive schemes that can dynamically operate the sensing or communications with better time coverage, energy efficiency, and resistance to battery aging effects, etc.

1. LINK AND ENERGY ADAPTIVE
DESIGN OF SUSTAINABLE IR-UWB
COMMUNICATIONS AND SENSING
November 21, 2013
Dong Zhao
University of Connecticut
E&CE Department Lab

2. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
OUTLINE
1. Introduction
2. Sustainable Model
3. Energy-Adaptive Schemes
4. Simulation
5. IR-UWB Sensing
6. Conclusion
7. Acknowledgment
2

3. INTRODUCTION

4. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
IMPULSE RADIO ULTRA WIDEBAND (IR-UWB) TECHNOLOGY
-41 dBm/Mhz
Noise Floor
802.11a802.11b
Bluetooth
Phones
Microwaves
GPS PCS
1.6 1.9 2.4 3.1 5.0 10.6
Frequency (GHz)
EmittedSignalPower
UWB
Figure: The relative power spectral densities and bandwidths of UWB
and other wireless technologies.
4

5. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
RANGE AND DATA RATE OF COMMON WIRELESS TECHNOLOGIES
1Gbit/s
ISM
ZigBee
802.15.14
Bluetooth
802.15.3
Wi-Fi
802.11b/g
Wi-Fi
802.11n
Wi-Fi
802.11a
UWB
Infrared
RFID
100 km
10 km
1 km
100 m
10 m
1 m
10 kbit/s 100 kbit/s 1 Mbit/s 10 Mbit/s 100 Mbit/s
Data Rate
Range
WiMAX 802.16
Cell Phones
GSM, UMTS,
CDMA2000
WCDMA with
HSDPA
Figure: Comparing range and data rate among popular wireless
technologies, UWB dominates high data rate but short range.
5

6. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
WIRELESS BODY AREA NETWORKS (WBAN)
Figure: Examples of anticipated BAN applications. The markets and
functions shown here are but a few of the many that will benefit from
the features that Body Area Networks provide.
6
Figure appeas in a 08/19/2013 post on Hearst Electroniv Products, Renesas Solutions for Wireless Networks - Part 2: Body Area Networks.
http://www.electronicproducts.com/Analog_Mixed_Signal_ICs/Sensors/Renesas_Solutions_for_Wireless_Networks_-_Part_2_Body_Area_Networks.aspx

7. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SUSTAINABLE INFORMATION TECHNOLOGY DEVELOPMENT
Figure: A plausible system architecture for
combining energy-harvesting leaves with the
battery-powered sensornet tree.
Figure appears in Dutta, Prabal. "Sustainable sensing for a smarter planet."XRDS: Crossroads, The ACM Magazine for Students 17.4 (2011): 14-20.
7

8. SUSTAINABLE MODEL

9. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SELF-POWERED IR-UWB SYSTEM
Energy Harvesting Unit
(EHU)
Battery
Power Management Unit
(PMU)
Eh
Eb
IR-UWB Transmitter
Ek , Nb
PC , α
Figure: System Diagram of Self-Powered IR-UWB Systems 1
1
Commercial solutions available, for example TI Energy Harvesting (solar
energy, thermal gradients, mechanical vibrations), Intel WISP (electrical
fields), Michigan MpowerCube (magnetic fields).
9

10. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
TIME-HOPPING(TH) PAM IR-UWB TRANSMITTER STRUCTURE
Repetition
Coder
(Nf, 1)
Transmission
Coder
PAM
Modulator
Pulse Shaper
p(t)
b da
Power Management Unit Control
Data Mapping
Pulse Gene.
PA
s(t)
RF Circuit
The output sequence s(t) representing the binary data is
s(t) =
√
Es
Nf
∞∑
j=−∞
b⌊j/Nf ⌋p(t − jTf − cjTc), (1)
where p(t) is the impulse response of pulse shaper, usually the
second derivative of Gaussian function.
p(t) =
d2g(t)
dt2
= A(1 − 4π
t2
σ2
)e− 2πt2
σ2 . (2)
10

11. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
AVERAGE IR-UWB SIGNAL POWER
Assume that all the symbols used in a PAM scheme appear with
same probability. The average signal power PS of the transmitted
symbols in a PAM scheme can be derived as
PS =
M∑
m=1
(2m − 1 − M)2
M
Pk =
M2 − 1
3
Pk, (3)
where M = 2Nb and Nb ∈ {1, 2, 3, ...} is the number of bits per
symbol, and Pk is the average power per symbol in 2-PAM, which
is also the baseline power per symbol in higher PAM schemes.
11

12. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
POWER CONSUMPTION ANALYSIS OF THE TRANSMITTER
The power consumption Pt of the IR-UWB circuit is related to the
signal power PS,
Pt = PC +Pamp+PS = PC +
ξNb
η
PS = PC +
ξNb
η
(M2 − 1)
3
Pk, (4)
where PC is the modulation-independent power consumption of
the circuit and Pamp is power consumption of the power amplifier
(PA). Drain efficiency η of PA is constant, PARP (Peak-to-Average
Power Ratio) ξNb
is defined as
ξNb
=
max |s(t)|2
E|s(t)|2
=
(2Nb − 1)2
12+32+...+(2Nb −1)2
2Nb−1
= 3 −
6(4Nb − 2Nb )
8Nb − 2Nb
, (5)
12

13. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
PERFORMANCE DESCRIPTION (QOS) BY SYMBOL ERROR RATE
Through link budget analysis, SER for PAM UWB can be derived as
Pre =
(
1 −
1
M
)
erfc
(√
3γ
(M2 − 1)
)
=
(
1 −
1
M
)
erfc
(√
αPk
PN
)
(6)
The complete error function erfc(x) is a decreasing function of x,
erfc(x) =
2
√
π
∫ ∞
x
e−t2
dt. (7)
γ denotes SNR at the receiver
γ =
αPS
PN
=
α(M2 − 1)Pk
3PN
, (8)
where PN is total noise power from the channel, PS is the
transmitted PAM signal power in (3), and α is channel gain.
13

14. ENERGY-ADAPTIVE SCHEMES

15. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ENERGY & CHANNEL CONSTRAINTS
1. Possible PAM is determined by total available energy during
the period, where Ton(≤ Ts) is the on time of transmitter, Eh
and Eb are available energy in battery and EHU, respectively.
Eb + Eh ≥ Ton · Pt, (9)
2. Possible PAM is also determined by the minimum QoS
requirement Prth, where 2Nb = M for M-PAM scheme.
(1 −
1
2Nb
)erfc
(√
αPk
PN
)
≤ Prth, (10)
Hence, the actual M-PAM must be determined by Me and Mc, the
highest possible PAM bounded by (9) and (10), respectively.
M = min(Me, Mc), (11)
15

16. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ENERGY CONSTRAINT PROBLEM
Given Nb and system on time Ton of transmitter, the equivalent
data rate (assuming a fixed symbol rate) in one time slot can be
obtained by substituting Ton and Pt from (9) and (4).
rNb
=
Nb
Ts
Ton ≤
Nb
Ts

 Eb + Eh
PC +
ξNb
η
4Nb −1
3 Pk

 , (12)
When there is energy excess, the achievable data rate is bounded
by the limit of Ton ≤ Ts, i.e. rNb
= Nb
Ton
Ts
≤ Nb.
Hence, the level of PAM M = 2Nb of each time slot should be
determined by the following simultaneous equations:



rNb
= Nb(Eb+Eh)
Ts
(
PC +
ξNb
η
4Nb −1
3
Pk
),
rNb
≤ Nb.
(13)
16

17. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ENERGY CONSTRAINT ILLUSTRATION
E1,2 E2,3
0
0.5
1
1.5
2
2.5
3
3.5
Energy Supply
AverageDataRate(bit/use)
2−PAM
4−PAM
8−PAM
TsPt (2) TsPt (3)
TsPt (1)
Figure: Illustration of determining energy thresholds.
17

18. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ENERGY CONSTRAINT ANALYSIS
Energy supply levels to 2-PAM (Nb = 1) and 4-PAM (Nb = 2).
1. Select 2-PAM: r2 < r1 < 1, both are on for less than Ts;
2. Select 2-PAM: r2 < 1 ≤ r1, r1 is bounded by N1 = 1;
3. Select 4-PAM: 1 < r1 ≤ r2, 4-PAM continue to increase.
The turning points locate at rNb+1 = (rNb
)max = Nb.
ENb,Nb+1 =
NbTs
Nb + 1
(
PC +
ξNb+1
η
4Nb+1 − 1
3
Pk
)
, (14)
in this case, it is
Eb + Eh < E1,2 =
Ts
2
(
PC +
5ξ2
η
Pk
)
.
18

19. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
TIME COVERAGE OPTIMIZATION
If the system is required to first maintain largest time coverage,
than for high date rate, the energy thresholds need to be chosen
differently.
Energy consumption for M-PAM to work in full time Ts is easy to
calculate,
ENb
= Ts
(
PC +
ξNb
η
4Nb − 1
3
Pk
)
. (15)
When the available energy is larger than ENb
but smaller than
ENb+1, the M = 2Nb level PAM can achieve a full-time coverage.
Et
Nb,Nb+1 = Ts
(
PC +
ξNb+1
η
4Nb+1 − 1
3
Pk
)
. (16)
19

20. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ENERGY CONSTRAINT ILLUSTRATION
E1,2 E2,3
0
0.5
1
1.5
2
2.5
3
3.5
Energy Supply
AverageDataRate(bit/use)
2−PAM
4−PAM
8−PAM
TsPt (2) TsPt (3)
TsPt (1)
Figure: Illustration of different adaptive schemes.
20

21. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
CHANNEL CONSTRAINT
Channel conditions and available energy levels are
non-deterministic and can be considered as mutually
uncorrelated.
This allows us to determine Me and Mc separately. Then the
channel constraint can be directly adopted from (10),
αNb,Nb+1 =
PN
Pk
[
erfc−1
(
Prth
1 − 1
2Nb
)]2
. (17)
Similar to energy thresholds expressed in (18), these channel gain
thresholds divide the possible channel conditions into several
sub-ranges and within each there is a suitable PAM to choose.
21

22. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SUMMARY & IMPLEMENTATION
Power Management Unit
(PMU)
Adder
Divider
Eb
Eh
PN
Lookup Table
ENb, Nb+1
Lookup Table
Nb, Nb+1 /PN
Comparator
Me
Mc
M
(or Nb)
Figure: An Implementation of the Power Management Unit.
22

23. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
PHYSICAL LAYER PROTOCOL MODIFICATION
SHR PHR PSDU
Transmit Order
Figure: UWB PPDU Structure.
Frame format of physical layer protocol data unit (PPDU) is
utilized by concatenating the synchronization header (SHR), the
physical layer header (PHR), and the physical layer service data
unit (PSDU), as illustrated.
The modulation information can be encoded by adding an 8-bit
data in the PPDU.
23

24. SIMULATION

25. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SOLAR ENERGY HARVESTING MODEL
The solar energy model describes the daily solar radiation as
Eh(t) = 10N(t)cos(
t
70π
)cos(
t
100π
) , (18)
where N(t) is a normally distributed random variable with zero
mean and unit variance.
0 100 200 300 400
0
0.5
1
1.5
2
2.5
Time Slot (30 minutes)
UnifiedSolarRadiationpertimeslot(W/m2
)
25

26. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
WIRELESS BODY AREA CHANNEL MODEL
Channel gain α in WBAN is determined by the path loss while
shadowing and multi-path fading have little effects.
PL(d) = a log10 d + b + N, (19)
d is the distance between the transmitter and receiver in mm,
N denotes a fluctuation term to correct minor differences,
a and b are coefficients from linear regression fitting.
Table: Parameters in the WBAN channel model.
Parameter Fat Normal Thin Free Space
a 24.9616 26.3447 19.8356 19.1882
b -6.7814 -11.7383 -1.8094 -10.8922
26

27. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SIMULATION RESULTS
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Channel Gain
AverageDataRate(bit/use) M−PAM
2−PAM
4−PAM
8−PAM
16−PAM
Figure: Performance of the energy-adaptive PAM and fixed PAM.
27

28. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
BATTERY AGING EFFECT
0 0.2 0.4 0.6 0.8 1
1.6
1.7
1.8
1.9
2
2.1
2.2
Battery Capacity (Percentage)
AverageDataRate(bit/use)
MPAM
4−PAM
8−PAM
Figure: Impact of the battery aging effect.
28

29. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SIMULATION FOR TIME COVERAGE ENHANCEMENT
0 0.2 0.4 0.6 0.8 1
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Time Coverage
AverageDataRate(bit/use)
M−PAM
4−PAM
8−PAM
16−PAM
2−PAM
M−PAM II
Figure: Comparison of time coverage and date rate.
29

30. IR-UWB SENSING

31. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
SELF-POWERED IR-UWB SENSING MODEL
Energy Harvest Unit
Battery
Power
Management
Unit
UWB Pulse
Radar
Figure: Model of self-Powered IR-UWB sensing transceiver.
31

32. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
IR-UWB SENSING TRANSCEIVER STRUCTURE
Target
Pulse
Generator
Delay
Generator
Shaper
MultiplierIntegrator
LNA
Transmitter
Receiver
Energy
Detector
Rp
SNR
Output
Local
delay
control
Figure: UWB pulse radar transceiver for sensing applications.
32

33. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
PULSE REPETITION FREQUENCY
In conventional UWB pulse radar, pulse repetition frequency Rp is
determined by the maximum detection range under a given SNR
requirement γs.
Rp,c = I · (
dmax
d0
)n
10(γs−C)/10
, (20)
where I is detection rate per second, dmax represents the
maximum detection range, d0 is reference distance, propagation
exponent n is 2 for air space. C is constance yielded by formula
manipulation2.
To adaptively tune the repetition rate at ith period,
Ri
p = Rp,c10(γs−γi)/10
. (21)
2
C = G0 + Gf + Pt − Pn. (G0 - path gain at d0, Gf - multi-path fading gain,
Pt - transmitted power of UWB pulses and Pn - wireless channel noise power)
33

34. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ENERGY AND DETECTION RANGE
While through energy analysis,
Ej
h + Ej
b = ¯RpEpTs, (22)
where ¯Rp is average pulse repetition frequency in each time slot
Ts, and Ep denotes energy consumption of one pulse.
Consider that an object moves randomly in the range of
di ∈ [0, dmax], while the PDF of di is fdi = 1/dmax (Random Walk
Model).
¯Rp =
∫ dmax
0
fdi · Ri
pd[di] =
I
n + 1
(
dmax
d0
)n
· 10(γs−C)/10
, (23)
Combining (22) and (23) yields the maximum detection range
dmax =
{
(Ej
h + Ej
b )(n + 1)
IEpTs10(γs−C)/10
} 1
n
d0. (24)
34

35. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
PULSE REPETITION IN PRACTICE
Rearranging (24), (20), and (21), the pulse repetition frequency
Ri
p in the ith update period can be expressed as
Ri
p =
(Ej
h + Ej
b )(n + 1)
Ep · Ts
10(γs−γi)/10
, (25)
Two options under occasionally condition of unexpected energy
shortage.
1. Similar to conventional designs, shut down the system for
the corresponding time period;
2. More rational option is to reduce the further reduce the pulse
repetition rate to accommodate the energy while degrade
SNR. When time coverage is critical while SNR is negotiable.
35

36. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
NATIONAL CLIMATIC DATA CENTER MEASURED SOLAR DATA
0 50 100 150 200
0
50
100
150
200
Time (hour)
SolarRadiationperhour(W/m
2
)
0 20 40 60 80 100 120 140 160 180
1000
2000
3000
4000
Time (day)
SolarRadiationperday(W/m
2
)
Figure: Solar power from the field measurements by the National
Climatic Data Center (NCDC).
36

37. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
TIME COVERAGE AND DETECTION RANGE IMPROVEMENT
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Detection range (m)
NormalizedAverageDetectionTime
Conventional scheme
Proposed scheme
Figure: Comparison of detection time coverage and range coverage
under the measured energy results.
37

38. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
AVERAGE ENERGY CONSUMPTION DECREASE
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
5
10
15
20
25
Detection range (m)
NormalizedAverageEnergyConsumption
Conventional scheme
Proposed scheme
Figure: Comparison of average energy consumption within one update
period (normalized by the energy consumption of the conventional
technique at d = 1m).
38

39. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
PERFORMANCE AGAINST BATTERY AGING
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.7
0.75
0.8
0.85
0.9
0.95
1
Battery Capacity
NormalizedAverageDetectionTime
Conventional scheme
Proposed scheme
Figure: Comparison of detection time coverage under different battery
capacities.
39

40. CONCLUSION

41. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
CONCLUSIONS
1. Replace the over-design with right size and smart control.
2. Deal with limited and non-deterministic energy supply with
negligible overheads.
3. Notable performance improvement in time coverage, data
rate or detection range.
4. Better energy efficiency and battery cooperation.
5. Coherently take care of link conditions.
Future work is being directed towards physical implementation
and field test, refine comparing algorithms, network design, and
related source/channel coding, etc.
41

42. ACKNOWLEDGMENT

43. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
ACKNOWLEDGMENT
Advisor:
Dr. Wang, Lei
Labmate and Co-author:
Chen, Junlin
Ridvan Umaz, Jack Huang,
Guoxian Huang, and all
members of lab!
43

44. Outline
. . . .
Introduction
. . . . .
Sustainable Model
. . . . . . . . .
Energy-Adaptive Schemes
. . . . .
Simulation
. . . . . . . . .
IR-UWB Sensing Conclusion Acknowledgment
PUBLICATIONS
D. Zhao, J. Chen, and L. Wang, Energy-adaptive pulse
amplitude modulation for IR-UWB communications under
renewable energy. Springer Journal of Signal Processing
Systems, 2013.10
J. Chen, D. Zhao, and L. Wang, Link and energy adaptive
UWB-based embedded sensing with renewable energy. Proc.
of International Symposium on Circuits and Systems (ISCAS),
2013
44

45. Q A