MINISTRY OF EDUCATION AND TRAINING
HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

HOANG TRUNG TUYEN

DESIGN AND PERFORMANCE EVALUATION
OF COMMUNICATION PROTOCOLS IN RFID SYSTEMS

DOCTORAL DISSERTATION OF
TELECOMMUNICATION ENGINEERING

Hanoi−2023


MINISTRY OF EDUCATION AND TRAINING
HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

HOANG TRUNG TUYEN

DESIGN AND PERFORMANCE EVALUATION
OF COMMUNICATION PROTOCOLS IN RFID SYSTEMS
Major: Telecommunication Engineering
Code: 9520208

DOCTORAL DISSERTATION OF
TELECOMMUNICATION ENGINEERING
SUPERVISORS:
1.Assoc. Prof. Nguyen Thanh Chuyen
2.Dr. To Thi Thao

Hanoi−2023

DECLARATION OF AUTHORSHIP
I, Hoang Trung Tuyen, declare that the dissertation titled "Design and performance evaluation of communication protocols in RFID systems" has been entirely
composed by myself. I assure some points as follows:
■

This work was done wholly or mainly while in candidature for a Ph.D. research
degree at Hanoi University of Science and Technology.

■

The work has not been submitted for any other degree or qualifications at Hanoi
University of Science and Technology or any other institutions.

■

Appropriate acknowledgement has been given within this dissertation where reference has been made to the published work of others.

■

The dissertation submitted is my own, except where work in the collaboration has
been included. The collaborative contributions have been clearly indicated.

Hanoi, September 12, 2023
PhD Student

Hoang Trung Tuyen

SUPERVISORS


Assoc.Prof. Nguyen Thanh Chuyen

i

Dr. To Thi Thao


ACKNOWLEDGEMENT
This dissertation was written during my doctoral course at School of Electrical
and Electronic Engineering (SEEE) and Communications Theory and Applications
Research Group (CTARG), Hanoi University of Science and Technology (HUST). I
would like to thank all member of SEEE, CTARG as well as all of my colleagues in
Military Science Academy (MSA). I am so grateful for all people who always support
and encourage me for completing this study.
I would like to extend my heartfelt gratitude to my principal supervisor Associate
Professor Nguyen Thanh Chuyen for his instructive guidance and valuable suggestions
in my academic studies. He gave me much help and advice during my PhD study and
the preparation of this dissertation. I am deeply grateful for his help. I gratefully
appreciate my secondary advisor Dr To Thi Thao for her constructive suggestions.
I also acknowledge Associate Professor Le Doan Hoang from the University of Aizu,
Japan, for their instructive comments and discussions about my research work. I am
also thankful to my friends and my fellow CTARG members for their discussions and
comments about my dissertation.
I would like to express my heartfelt gratitude to my family, wife, and children for
their unwavering support throughout my PhD journey. Their encouragement, patience,
and understanding have been instrumental in helping me overcome the challenges and
obstacles that I have encountered along the way. Their love and sacrifices have been
my driving force, and I am forever grateful for their unwavering support. Thank you
for being my rock and my inspiration, I could not have done this without you.

Hanoi, 2023
Ph.D. Student

ii


CONTENTS

DECLARATION OF AUTHORSHIP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

i

ACKNOWLEDGEMENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ii

CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vi

ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vi

SYMBOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


xi

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiv

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

CHAPTER 1. BACKGROUND OF STUDY . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.1. Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.1.1. Introduction to the Internet of Things (IoT). . . . . . . . . . . . . . . . . . . . . . . . . . .

6

1.1.2. Radio Frequency Identification (RFID) Systems . . . . . . . . . . . . . . . . . . . . . . .

7

1.2. Problem Statement and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16


1.2.1. Anti-collision protocols/algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

1.2.2. Missing-tag Detection/Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23

1.3. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

CHAPTER 2. PERFORMANCE ANALYSIS OF HYBRID ALOHA/CDMA
RFID SYSTEMS WITH QUASI-DECORRELATING DETECTOR IN NOISY
CHANNELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26

2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

26

2.2. System Description and Conventional Approach . . . . . . . . . . . . . . . . . . . . . . . . . .

27

2.2.1. System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27


2.2.2. Transmission Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

28

2.2.3. Conventional Decorrelating Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29

2.3. Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

30

2.3.1. Quasi-decorrelating Detector (QDD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

30

2.3.2. Performance Analysis of Tag Identification Efficiency . . . . . . . . . . . . . . . . .

32

2.4. Performance Evaluation and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

2.4.1. System Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

34

iii



2.4.2. False Alarm and False Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

38

2.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41

CHAPTER 3. ON THE DESIGN OF NOMA-ENHANCED BACKSCATTER COMMUNICATION SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

3.1.1. Related Works and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

3.1.2. Major Contributions and Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43

3.2. System Model and Conventional Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

3.2.1. System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45


3.2.2. Conventional Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

46

3.3. Proposed NOMA-Enhanced BackCom Systems . . . . . . . . . . . . . . . . . . . . . . . . . . .

48

3.3.1. NOMA-Enhanced BackCom: Static Systems . . . . . . . . . . . . . . . . . . . . . . . . .

48

3.3.2. NOMA-Enhanced BackCom: Dynamic Systems . . . . . . . . . . . . . . . . . . . . . .

51

3.4. Simulation Results and Discussions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52

3.4.1. Number of Successful Backscatter Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53

3.4.2. Number of Successful Transmitted Bits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57

3.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


60

CHAPTER 4. EFFICIENT MISSING-TAG EVENT DETECTION PROTOCOLS TO COPE WITH UNEXPECTED TAGS AND DETECTION
ERROR IN RFID SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61

4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61

4.2. System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

62

4.2.1. System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

62

4.2.2. Communication Protocol: Aloha, Wireless Channel Model, and Detection
Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

63

4.2.3. Conventional Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65

4.3. Proposed Missing-Tag Event Detection Protocols . . . . . . . . . . . . . . . . . . . . . . . . .


66

4.3.1. Protocol Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

66

4.3.2. Parameter Optimization under Impacts of Unexpected Tags and Detection
Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69

4.3.3. Expected Detection timeslots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

70

iv


4.4. Numerical Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71

4.4.1. False-Alarm and True-Alarm Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . .

74

4.4.2. Performance Comparison with Conventional Protocols. . . . . . . . . . . . . . . .

75


4.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

76

CONCLUSION AND FUTURE WORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78

PUBLICATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

80

BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

92

v


ABBREVIATIONS

No.

Abbreviation Meaning


1

APRC

Adaptive Power Reflection Coefficient

2

AWGN

Addtitive White Gaussian Noise

3

BMTD

Bloom filter-based Missing-Tag Detection

4

BN

Backscatter Node

5

CD

Code-Domain


6

CDMA

Code Division Multiple Access

7

DD

Decorrelating Detector

8

DSP

Dynamic-Size Pairing

9

FA

False Alarm

10

FDMA

Frequency Division Multiple Access


11

FSA

Frame Slotted Aloha

12

ID

IDentity

13

IoT

Internet of Thing

14

MAC

Medium Access Control

15

MAI

Multiple Access Interference


16

NOMA

Non-othogonal Multiple Access

17

PD

Power-Domain

18

QDD

Quasi-Decorrelating Detector

19

RF

Radio Frequency

20

RFID

Radio Frequencyl IDdentification


21

SDMA

Space Division Multiple Access

22

SIC

Successive Interference Cancellation

23

SINR

Signal-to-Interference-and Noise Ratio

24

SNR

Signal-to-Noise Ratio

25

TA

True Alarm


26

TDMA

Time Division Multiple Access

27

TNP

Two Node Pairing
vi


SYMBOLS

No.

Symbol

Meaning

1

A

Multi-stage feed-forward matrix

2


B

Number of backscatter nodes

3

b

Number of backscatter nodes multiplexed

4

α

Required reliability

5

C1i

The i-th tag counter, i ∈ [1, |E|]

6

C2

Reader counter

7


Cth

Counter threshold

8

c

Gold code

9

D1

Expected detection time slots of mRUN1 protocol

10

D2

Expected detection time slots of mRUN2 protocol

11

E

Set of expected tags

12


E[D]

Expected detection time slots

13

i ]
E[X01

Expected number of slots that is expectedly empty in the i-th in
pre-computed frame but observed as non-empty in the i-th executed
frame

14

ξi

Power reflection coefficient of the i-th BN

15

ϵ

Number of feed-forward stage matrix

16

f

Frame size


17

G

Annular region

18

G

Code set

19

g

Probability that a missing-tag event is detected at a given time slot
among f slots

20

H(.)

Hash function

21

h


Channel coefficient

22

I

Identity matrix

23

K

Number of Gold codes

24

L

Length of the register
vii


25

Lc

Gold code length

26


M

Truncation matrix

27

M

NOMA group size

28

m

Number of tags in E missing from population

29

N

Number of tags

30

B

Number of BNs

31


Nl

Number of tags in the l-th slot

32

Nfa

Number of available tags detected as missing ones

33

Nfd

Number of actual missing tags detected as available ones

34

No

Noise power

35

NS

Normalized number of successful BNs

36


N near

Number of successful BNs from near subregion

37

N far

Number of successful BNs from far subregion

38

Nnear

Number of BNs in near regions

39

Nfar

Number of BNs in far regions

40

n

Number of frames required to ensure detection

41


n

Vector of White Gaussian noise

42

n(t)

White Gaussian noise

43

η

System efficiency

44

P

Reader’s transmitted power

45

PeDD

Bit error probability using DD

46


PeQDD

Bit error probability using QDD

47

Paloha (i)

Probability that i tags among N tags simultaneously transmit their
IDs.

48

Pd (a|i)

Probability that a tags are not collided

49

Ps (a|i)

Probability that a tags are successfully detected

50

Pcdma (a|i, K)

Probability that a tags are assigned with a different codes of the K
codes


51

c
Pcdma
(i − a|i, K − a) Probability

that the remaining (i − a) tags are collided with the

(K − a) codes
52

Ps (j)

Probability that the j-th tag is successfully detected

53

Pde

Probability of detection error
viii


54

Pr i

Received power at the reader from the i-th BN

55


Pfp

Probability that slots, which expectedly include a particular missing
tag, are observed as non-empty after nf executed frame

56

Pnear (r)

Probability that a node of distance r belongs to the near subregions.

57

Pfar (r)

Probability that a node of distance r belongs to the far subregions.

58

pn

Probability of a BN being in the subregion specified by RI and r

59

pf

Probability of a BN being in the subregion specified by RO and r


60

p(t)

Rectangular pulse

61

pi01

Probability that an expectedly empty slot is observed as non-empty
in the i-th frame

62

ρ

Path-loss coefficient

63

Q(.)

Monotonically decreasing function

64

R

Random seed


65

R

Correlation matrix

66

R−1

Inversion matrix of R

67

RI

Inner radius of coverage area

68

RO

Outer radius of coverage area

69

Rmn

Cross-correlation coefficient of matrix R


70

Rfa

False alarm rate

71

Rfd

False detection rate

72

r

Distance between a BN and a reader

73

r(t)

Received signal at the reader

74

S

Total number of slots used to detect a missing-tag event


75

s(t)

Transmitted signal from the tag

76

sgn(.)

Sign function

77

T

Threshold to detect missing tags

78

Ts

Time-slot duration

79

Tb

Bit duration


80

Tc

Chip duration

81

U

Set of unknown/unexpected tags

82

ν(t)

Transmitted signal at the reader

ix


83

l
X01

Random variable for number of slots that is expectedly empty in
the l-th in pre-computed frame but observed as non-empty in the
l-th executed frame


84

x

Vector of transmitted information bits

85

ˆ
x

Estimate of x

86

x

Transmitted information bit

87

γth

Reader’s sensitivity threshold

88

z


Filters’ output signal matrix

89

zˆ

Signal vector after MAI elimination

90

⌊·⌋

Greatest integer function

91

|·|

Cardinality of a set

x


LIST OF TABLES

1.1

Tag characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1


Simulation parameters for RFID system. . . . . . . . . . . . . . . . . . . . 34

3.1

Simulation parameters for NOMA-aided BackCom systems. . . . . . . . . . 53

3.2

Backscatter node’s data structure. . . . . . . . . . . . . . . . . . . . . . . . 53

4.1

A comparison of related works on missing-tag event detection. . . . . . . . 68

4.2

Simulation parameters for missing-tag event detection protocols. . . . . . . 72

4.3

Optimal selection of Cth in mRUN1 and mRUN2, given Pta = 0.95 and
m = T = 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

xi


LIST OF FIGURES

1.1


The Internet of Things (IoT) Integration [14]. . . . . . . . . . . . . . . . . 6

1.2

Components of an RFID system. . . . . . . . . . . . . . . . . . . . . . . . 7

1.3

RFID tag. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4

A passive RFID tag having 96 bits of memory to represent an EPC number. Header identifies the version of EPC itself; EPC Manager number
identifies an organization; Object class refers to a unique type of product
produced by an EPC manager; Serial number uniquely identifies each
item within an object class. . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.5

A magnetic coupling RFID system. . . . . . . . . . . . . . . . . . . . . . . 11

1.6

An electromagnetic coupling RFID system. . . . . . . . . . . . . . . . . . . 12

1.7

An illustration of Tree-based protocol. . . . . . . . . . . . . . . . . . . . . 13


1.8

FSA throughput for different frame sizes. . . . . . . . . . . . . . . . . . . . 15

1.9

An illustration of FSA protocol. . . . . . . . . . . . . . . . . . . . . . . . . 15

1.10 An illustration of tag collision (a) and reader collision (b). . . . . . . . . . 16
1.11 An illustration of FSA-based communication protocol with CE and DE. . . 19
1.12 CDMA detector: A matched filter bank [78]. . . . . . . . . . . . . . . . . . 21
1.13 Decorrelating detector. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.14 A RFID system using NOMA. . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1

CDMA-based RFID system with FSA protocol. . . . . . . . . . . . . . . . 27

2.2

Transmission channel model. . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.3

Reader structure with decorrelating detector. . . . . . . . . . . . . . . . . . 30

2.4

Quasi-decorrelating detector structure. . . . . . . . . . . . . . . . . . . . . 31

2.5


Flowchart of simulation process to calculate BER and system efficiency. . . 35

2.6

BER performance of QDD and DD detectors with respect to a number
of tags, given Lc = 31, SNR = 7 dB ϵ = 3. . . . . . . . . . . . . . . . . . . 36

2.7

BER comparison between DD and QDD by varying values of SNR. . . . . 36

2.8

System efficiency with respect to the number of tags, given f = 32, K
= 30, Lc = 30, SNR = 7 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.9

System efficiency with respect to the number of tags, given K = 30, f
= 32, Lc = 31, SNR = 7 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . 38
xii


2.10 System efficiency with respect to the number of codes, given K = 30, f
= 32, Lc = 31, SNR = 7 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.11 System efficiency with respect to frame size, given N =1000, K = 30, Lc
= 31. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.12 False alarm and false detection rate with respect to the SNR in the
conventional missing-tag detection protocols with DD and QDD, given

N =1000, K = 15, f = 512, L = 4, Threshold = 0.3. . . . . . . . . . . . . 40
2.13 False alarm and false detection rates with respect to the threshold in the
conventional missing-tag detection protocols with DD and QDD, given
N =1000, K = 15, f = 512, L = 4, SNR = 0 dB. . . . . . . . . . . . . . . 40
3.1

Illustration of (a) system model, (b) time-slot structure, and (c) NOMAaided BackCom system with M = 2. . . . . . . . . . . . . . . . . . . . . . 45

3.2

The structure of backscatter node with variable power reflection coefficients. 51

3.3

The flowchart of TNP scheme. . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.4

The flowchart of APRC scheme. . . . . . . . . . . . . . . . . . . . . . . . . 55

3.5

The flowchart of DSP scheme. . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.6

The comparison of different schemes in static NOMA-enhanced BackCom systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.7


Normalized successful BNs versus channel threshold for the static systems using the TNS scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . 57

3.8

Normalized successful BNs versus channel threshold for the static systems using the APRC scheme. . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.9

Normalized successful BNs versus channel threshold for the dynamic
systems using the DSP scheme. . . . . . . . . . . . . . . . . . . . . . . . . 58

3.10 Performance comparison of different schemes in static NOMA-enhanced
BackCom systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.11 Performance comparison of DSP and hybrid APRC/DSP schemes in
dynamic NOMA-enhanced BackCom systems. . . . . . . . . . . . . . . . . 59
4.1

RFID system model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.2

Aloha communication protocol with unexpected tags and detection error. . 64

4.3

Flowchart of mRUN1 protocol.

. . . . . . . . . . . . . . . . . . . . . . . . 67

4.4


Flowchart of mRUN2 protocol.

. . . . . . . . . . . . . . . . . . . . . . . . 68

4.5

Detection error probability Pde versus the number of tags in a slot. . . . . 72

xiii


4.6

Theoretical and simulation results of the number of slots with respect
to the number of missing tags. . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.7

Theoretical and simulation results of the number of slots with respect
to the detection error probability. . . . . . . . . . . . . . . . . . . . . . . . 73

4.8

True-alarm and false-alarm probabilities with respect to the detection
error probability of mRUN1. . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.9

True-alarm and false-alarm probabilities with respect to the detection

error probability of mRUN2. . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.10 The numbers of slots with respect to the number of missing tags of
conventional RUN, BMTD, proposed mRUN1 and mRUN2. . . . . . . . . 76
4.11 The numbers of slots with respect to the detection error probability of
conventional RUN, BMTD, proposed mRUN1 and mRUN2. . . . . . . . . 77
4.12 False-alarm (FA) and True-alarm (TA) probabilities with respect to
the detection error probability of conventional RUN, BMTD, proposed
mRUN1 and mRUN2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

xiv


INTRODUCTION
Motivation
Radio Frequency IDentification (RFID) has become increasingly prevalent in IoT
applications due to advancements in automatic identification technology. The technology provides a convenient means of identifying and tracking a huge number of
different objects and devices that make up IoT networks. It has also recently garnered
significant research interests, prompting extensive exploration of challenging issues in
literature, such as tag anti-collision and missing-tag detection/monitoring. Tag anticollision focuses on resolving signal collision caused by simultaneous transmission from
multiple tags [1]. This collision leads to incorrect signal decoding at central processing
units (readers/interrogators) and renders subsequent operations of RFID systems nonfunctional. Meanwhile, the missing-tag detection/monitoring issue is to design reliable
protocols that can accurately detect/monitor whether some tags are missing [2].
In order to cope with the tag collision, many communications/access protocols have
been designed for years. They are usually based on different multiple access techniques,
which schedule and control the order of each tag’s transmission. Among them, Code
Division Multiple Access (CDMA) [3, 4] is considered as one of the most promising anticollision solutions, especially for dense RFID systems. Each tag is assigned with one
of the orthogonal pseudo-codes so that multiple tags could be successfully identified at
the same time. In this case, Decorrelating Detector (DD) is usually implemented at the
reader for signal decoding. Nevertheless, the implementation of DD might also enhance

the background noise according to [5], and thus, might degrade the system performance.
To overcome the disadvantage of the noise enhancement in DD, Quasi-Decorrelating
Detector (QDD) [6] has been studied as one of the alternative solutions. This motivates
me to study and propose using QDD as one of the most efficient candidates for the
structure of readers in CDMA-based RFID systems.
Communication and signal processing algorithms/technologies also play an important role in mitigating the tag collision. One of the well-known recent approaches is
non-orthogonal multiple access (NOMA). NOMA enables multiple tags to be served at
the same time/frequency resources by using the principle of successive interference cancellation (SIC) decoding [7]. This is achieved by differently designed transmitted power
levels, also known as power-domain NOMA. In [8], authors provide a design guideline
1


for the backscatter communication (BackCom) systems (including RFID) using a hybrid TDMA and power domain NOMA. In particular, backscatter nodes (BNs) i.e.,
tags are categorized into different regions based on their power levels. Then, NOMA
is implemented for groups of tags selected from the different regions. However, there
are three major drawbacks to this design framework. Firstly, the BNs are chosen for
NOMA grouping in a random manner, which may increase the likelihood of signal
decoding failure due to adverse wireless channel issues. Secondly, BNs are assigned
by constant power reflection coefficients based on their locations, which is to make a
significant difference in the channel gains. Using these fixed settings, nonetheless, may
lead to poor performance over time-varying channel conditions. Thirdly, the design
framework in [8] is intended for static NOMA-aided BackCom systems only. In practical systems, BNs may frequently enter or leave the reader’s coverage area, necessitating
dynamic schemes. These limitations make it essential to develop novel schemes to enhance the performance of conventional NOMA-aided BackCom systems. Therefore,
this dissertation aims to address these limitations by proposing new paring schemes
both static and dynamic BackCom systems.
On the other hand, the issue of missing-tag detection/monitoring in RFID systems
has been extensively studied in both academia and industry, but it is still a relatively
new and under-investigated problem. Many existing works assume a perfect system
implementation that includes only expected tags that are known to RFID readers. The
assumption is, clearly, not practical since there also exists unexpected tags (the ones

whose IDs are not known a priori to readers) in real RFID systems. In such scenarios,
the unexpected tags might result in more severe radio collision and wrong observations
of the status of each timeslot. In this situation, previous protocols may report false
alarms on the event detection. Moreover, many existing studies do not consider the
issue of detection error due to effects of wireless transmission and noise, which is a
common phenomenon in RFID literature [9, 10, 11]. In particular, when the received
signal strength at the reader during a timeslot falls below a certain sensitivity threshold
due to noise or multipath fading, the signal decoding is not successful even in timeslots
with one tag’s response. As a result, traditional missing-tag detection protocols may
frequently produce false alarms for the system administrator, requiring more time and
energy consumption. This inefficiency and unreliability render the protocols no longer
efficient and reliable. Therefore, it is crucial to mitigate the impact of unexpected tags
and the detection error in missing-tag event detection protocols.

2


Objectives
The primary objectives of this dissertation is to offer a design framework for the
performance enhancement of RFID systems, by considering the critical issues of (i) tag
anti-collision and (ii) missing-tag monitoring.
For tag anti-collision, the objective of implementing QDD at the reader structure
in hybrid Aloha/CDMA-based RFID systems is not only to improve tag identification performance, but also to overcome the disadvantage of noise enhancement in DD.
Another objective in this research area is to improve the performance of conventional
NOMA-aided BackCom using novel user pairing schemes for static and dynamic systems. One scheme selects NOMA groups based on successful decoding probability,
while another adjusts BN’s power reflection coefficients depending on their channel
conditions to increase decoding probability.
On the other hand, for missing-tag detection, the objective is to re-design conventional detection protocols taking the effect of unexpected tags and detection error into
account. It is expected that the new protocols outperform conventional ones in terms
of time (and/or) energy consumption.


Research scope and methodology
Research scope
The research scope of this dissertation focuses on the efficient designs and analysis
in terms of time/energy consumption of tag anti-collision and missing-tag detection
protocols in RFID systems. The designs are mainly based on assumptions/requests of
practical RFID models that might not be optimal for current protocols. They, then,
adopt common and different communication/signal processing technologies, algorithms
to improve the protocols’ performance. It is also noted that the research scopes are
contributed in terms of theoretical points of view without testbed, experimental systems.
Methodology
The research methodology employed in this dissertation mainly includes mathematical analysis and Monte Carlo computer simulation evaluation. Mathematical analysis
is for protocol designs, while Monte Carlo simulations are implemented to validate the
logic and also, the accuracy of the designs.

3


Contributions and structure of the dissertation
The dissertation has three main contributions as follows:
• The first contribution of dissertation is the analysis and evaluation of the performance of hybrid ALOHA/CDMA-based RFID systems using QDD as a multi-user
detector. This work is proved both in terms of analysis and computer simulations
to enhance the efficiency of tag identification under effects of wireless channel
impairments. This contribution has been published in:
Tuyen T. Hoang, Hieu V. Dao, Vu X. Phan, and Chuyen T. Nguyen, Performance Analysis of Hybrid ALOHA/CDMA RFID Systems with Quasidecorrelating Detector in Noisy Channels, REV Journal on Electronics and
Communications, Vol. 9, No. 1–2, January–June, 2019.
• The second contribution of the dissertation is the proposal of a comprehensive
design framework for both static and dynamic NOMA-enhanced BackCom systems. It includes, for static systems, Two-node pairing (TNP) and novel adaptive power reflection coefficient (APRC) schemes. For dynamic systems, a novel
dynamic-sized pairing (DSP) and hybrid APRC/DSP schemes are introduced.
These schemes are proposed to improve the system performance in terms of the

number of successfully decoded bits and the number of successful multiplexed BNs
from different regions for NOMA grouping. This contribution has been submitted
to IEEE Access.
Tuyen T. Hoang, Hoang D. Le, Luu X. Nguyen, and Chuyen T. Nguyen, On
the Design of NOMA-Enhanced Backscatter Communication Systems, IEEE
Access, DOI: 10.1109/ACCESS.2023.3272892, (ISI), May 2023.
• The third contribution of the dissertation is proposal of two protocols, namely
mRUN1 and mRUN2, to address the issue of missing-tag event detection. These
protocols use tracking counters at the reader and tags to mitigate detection errors
and announce the event only if the counters reach a predefined threshold. The
protocols are validated through performance analysis and simulations, showing
their superiority over conventional protocols in terms of false-alarm and true-alarm
probabilities. The contribution has been published in Wireless Communications
and Mobile Computing, 2019.
Chuyen T. Nguyen, Tuyen T. Hoang, Linh T. Hoang, and Vu X. Phan
(2019), Efficient missing-tag event detection protocols to cope with unexpected
4


tags and detection error in RFID systems, Wireless Communications and Mobile Computing, DOI: 10.1155/2019/6218671, (ISI), 2019.
The dissertation consists of four chapters and is organized as follows:
• Introduction provides the main motivations, objectives of the dissertation as well
as research scope, methodology, contributions, and structure of dissertation.
• Chapter 1 presents the research background, problem statements, and literature
review.
• Chapter 2 addresses the tag anti-collision issue with CDMA-based approach in
which Quasi-decorrelating detector is adopted in hybrid ALOHA/ CDMA-based
RFID systems.
• Chapter 3 focuses the design and analysis of efficient user pairing schemes for
NOMA-enhanced Backscatter communication systems.

• Chapter 4 considers the missing-tag monitoring issue with unexpected tags and
detection error.
• Conclusion and future works summarize the contributions of this dissertation, and
introduces some future works.

5


Chapter 1
BACKGROUND OF STUDY
1.1. Research Background
1.1.1. Introduction to the Internet of Things (IoT)
The growth of the Internet of Things (IoT) has been spurred by recent advancements in wireless communication and smart device technologies. This enables millions
of physical objects to be connected to the Internet with widespread sensing and computing abilities, as illustrated in Fig. 1.1. The IoT plays a crucial role in the future of
the internet and has been widely discussed by both academic and industrial communities because of its ability to provide various customer services in various aspects of
daily life [12, 13]. The IoT allows for smooth communication and automatic control
among different devices without human interaction. This results in the potential for
industry revolution and significant benefits to society through advanced, intelligent,
and automated remote management systems.

Figure 1.1: The Internet of Things (IoT) Integration [14].

On the other hand, the fast growth of smart devices in IoT applications poses
challenges of autonomy, low-power consumption, low cost, and high scalability [13, 15],
while the essential issue is making a full interoperability of interconnected IoT devices
possible [16]. To cope with the issue, there are several solutions in the literature
6


which are Internet Protocol version 6 (IPv6), Wireless Sensor Networks (WSNs), and

Radio Frequency Identification (RFID). Nevertheless, it is infeasible and un-necessary
for each among billions of IoT devices to be associated with an IP address [17]. In
addition, the WSNs face with not-easy-to-solve challenges of energy consumption and
configuration [18, 19]. In this case, the RFID technology has been considered as one of
the most potential candidates for the aforementioned connectivity issue in IoT thanks
to a number of considerable benefits, which is also our focus in this dissertation. Indeed,
it is reported in [19] that RFID has become one of the fundamental pillars that enable
the IoT and promote its rapid development. It has been widely used in a variety of
large-scale applications, including inventory management, logistics tracking, precision
agriculture, etc., to enable automatic identification and tracking of tags attached to
objects [20, 21, 22, 23, 24]. Moreover, RFID is capable of detecting/identifying multiple
objects/tags without line-of-sight signal propagation, while it is able to offer a number
of other great advantages including low manufacture costs (5 cents per tag), easy
implementation, long service lifetime, and robustness [20].
1.1.2. Radio Frequency Identification (RFID) Systems
Radio Frequency Identification (RFID) is a contactless automatic identification and
data capture (AIDC) technology that uses RF signals for communication. Data is
stored on silicon chips (tag memory), which are attached to targets such as books,
parcels, humans, animals, or other objects. This section presents the components of an
RFID system and explains how it operates to provide a better understanding of RFID.
1.1.2.1. RFID Components
As illustrated in Fig. 1.2, a typical RFID system consists of a reader, multiple
tags/transponders, and the middle-ware software (application) [25].
Data

Reader

Clock
Energy


Contactless
data carrier =
Transponder

Coupling element
(coil, microwave antenna)

Application

Figure 1.2: Components of an RFID system.

RFID Tag: Tags (or transponders) are the actual data-carrying devices that are
attached to objects for identification. A tag harvests energy from reader interrogation,
7


performs lightweight computation, and transmits data in response to reader queries.
Due to their simple structure, small size, and low manufacturing cost, tags serve as an
economical and competitive method for managing massive objects, such as inventory
control, object tracking, activity monitoring, authentication, localization, and more.

Figure 1.3: RFID tag.

The basic components of a tag include a microchip containing non-volatile memory
and an antenna to collect and transmit radio waves as shown in Fig. 1.3 [26]. The chip
contains circuitry that stores a unique binary number in called an electronic product
code (EPC) [27], while the antenna serves as the receiver and transmitter of information. EPC is a universal identifier (normally, 64 or 96 bits) that provides a unique
identity to a specific physical object. The antenna, which is much larger than the
microchip, typically consists of loops or coiled wire extending out from the chip. It
receives signals from an RFID reader and backscatters the signal with required data.

RFID tags can be broadly classified in three types: passive, active, and semi-passive[1].
• Passive tags do not have their own power source, and they rely on the energy
emitted by the RFID reader to power them up. When the RFID reader sends
a signal to the tag, the tag absorbs the energy and uses it to transmit the data
back to the reader. Passive tags can be further classified as low-frequency (LF),
high-frequency (HF), and ultra-high frequency (UHF) tags. LF tags are suitable
for short-range applications, such as access control systems, whereas HF tags
are ideal for mid-range applications, such as payment systems. UHF tags are
suitable for long-range applications, such as inventory management and supply
chain management. Fig. 1.4 shows the EPC tag data structure of the 96-bit
passive tag [28, 29].
• Active tags have an onboard power source, usually a battery, and are equipped
with a powered receiver and transmitter. This enables the reception of very weak
8


Figure 1.4: A passive RFID tag having 96 bits of memory to represent an EPC number. Header
identifies the version of EPC itself; EPC Manager number identifies an organization; Object class
refers to a unique type of product produced by an EPC manager; Serial number uniquely identifies
each item within an object class.

signals and transmission of signals over long distances or through interference.
Moreover, active tags are capable of detecting collisions and sensing the channel,
thereby improving their overall performance. They are particularly useful in scenarios where the tag needs to operate in harsh environments or over a long range.
Active tags are often larger and more expensive than passive tags due to their
additional components and power source. However, they offer greater flexibility
and functionality in terms of their communication capabilities
• Semi-passive tags combine elements of both active and passive tags. They possess an onboard power source that is used to power the microchip and a passive
receiver. The semi-passive tag communicates using backscatter and can communicate over a longer range than passive tags. Semi-passive tags are typically used
in applications where longer read ranges are needed, such as in logistics or asset tracking. The use of a battery allows for the tag to transmit data at higher

power levels than passive tags. This feature is particularly useful in applications
where tags are embedded in metal or other materials that can interfere with the
radio signal. Semi-passive tags are also capable of sensing their environment and
collecting additional data such as temperature, humidity, or motion.
Table 1.1: Tag characteristics
Characteristics

Passive tag

Active tag

Semi-passive tag

Frequency
Internal power
Transceiver on broad
Bit rate (Kbps)
Memory (KB)
Multi-tag collection
Read Range (m)
Tag size
Cost (USD)
Life Time (years)

LF, HF, UHF, Microwave
No
No
246
128
3 sec. to identify 20 tags

0.1-7
Thin, flexible
0.15-1.00
3-10

UHF, Microwave
Yes
Yes
20/40/250
128
1000 tag/sec at 100 mph
More than 100
Large, bulky
10-100
0.5-5

UHF
Yes
No
16
4
7 tags/sec at 3 mph
60-80
Thin, flexible
0.75-2.00
0.5-5

In brief, Table. 1.1 technically summarizes various characteristics a according to tag
types [1, 30].
9