Differentiated Access Mechanism in Cognitive Radio
Networks with Energy-Harvesting Nodes
Network System Lab.
Yunmin Kim, Tae-Jin Lee
Sungkyunkwan Univ.
Network System Lab.
Introduction

Wireless Sensor Networks (WSNs)
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
Research issues in WSNs

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

Consists of distributed sensor nodes to monitor the various physical condition
Apply to a variety of applications such as monitoring systems, medical
systems, and military systems
Sensor devices have limited energy amount
 Energy-harvesting creates electric energy from various source
Sensor nodes suffers interference and overcrowded problems in ISM band
 Using cognitive radio, sensors can operate in under-crowded licensed band
Efficient contention scheme to access channel
Objectives


Collision among sensor nodes is reduced to enhance throughput performance
Energy consumption of sensor nodes is reduced to improve energy efficiency
Sungkyunkwan Univ.
2
Network System Lab.
System Model

Network topology
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Base Station (BS) : provides service to primary users
Primary User (PU) : access the channel without constraint
Secondary User (SU) : access the channel only if the channel is not occupied
PU
PU
SU_r
PU
PU
PU
PU
PU
: base station
SU_r
: secondary receiver
: transmission of primary user
: energy level
: primary user
SU_t
: secondary transmitter
: transmission of secondary user
: threshold value
Sungkyunkwan Univ.
3
Network System Lab.
System Model

Frame structure
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Sensing period
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Contention period
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SUs sense the channel
SUs perform backoff contention to reserve data transmission
SUs choose random waiting time from contention window
Transmission period

Succeed SU transmits data packets
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4
Network System Lab.
Proposed Energy Level based MAC (EL-MAC) Protocol

Access probability
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

An SU determines the access probability based on the its energy level
An SU decides to use the channel based on its access probability
A low energy SU has the higher access probability than a high energy SU
A low energy SU is more desperate to transmit data before all the energy is discharged

Emax
PAcc,i
i


i  Eth
 max 1 
, PAcc,min 
 Emax  Eth

PAcc
Eth
< Battery of SU >
Sungkyunkwan Univ.
PAcc , min
i
Eth
Emax
5
:
:
:
:
:
access probability
minimum access probability
current energy level
threshold energy level
maximum energy level
Network System Lab.
Proposed Energy Level based MAC (EL-MAC) Protocol

Differentiated contention window size
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An SU decides the contention window size (CW) based on its energy level
An SU randomly selects a backoff value from the [0, CW-1]
A low energy SU has the smaller CW than a high energy SU
A low energy secondary user is more likely to win the contention

Emax
CW  CWmin  2
i
CW
CWmin
CWmax
l
Eth
contention window size
minimum contention window size
maximum contention window size
backoff stage
l  log 2
< Battery of SU >
Sungkyunkwan Univ.
:
:
:
:
 i  Eth

( l 1) 

 Emax  Eth

6
CWmax
CWmin
Network System Lab.
Proposed Energy Level based MAC (EL-MAC) Protocol
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Example of the proposed EL-MAC protocol
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SU 2 determines not to participate in contention
SU 1, 3, and 4 become contending users
The CW of SUs 1, 3 and 4 are 16, 32 and 8, respectively
SU 4 succeeds to make a reservation
i 5
i2
16
Sungkyunkwan Univ.
i4
i 1
64
32
7
Network System Lab.
Performance Analysis – Access Probability
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Markov chain model – SUs‘ state
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SUs consume one energy block when sensing and transmission
SUs charge one energy block in every superframe
SUs can have up to m energy blocks
pstate, energy _ level : state = {S, A}, energy level = {0, 1, 2, … , m}
 : prob. of success in the contention period
1
1
S,2
S,m-1
1
S,1
  PAcc, 2


1  PAcc,m  2
PAcc,3 (1   )
PAcc, 2 (1   )
1
S,0
1  PAcc, 2
1  PAcc,m 1
PAcc,m 1 (1   )
A,2
  PAcc,3
1
PA,m (1   )
A,m-1
  PAcc,m1
  PAcc, 4
1 PAcc,m
A,m
  PAcc,m
Prob. of the SU is active state : pactive  p A, 2  p A,3    p A,m  f (  )
Num. of contending SUs : ncontend  n  pactive  n  f (  )
n : total number of SUs
Sungkyunkwan Univ.
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Network System Lab.
Performance Analysis – Access Probability

Probability of success in contention
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In a contention period, SUs perform backoff contention
Using Bianchi’s model[1], the probability of success can be evaluated
In the steady state, the proper  and ncontend can be obtained
-
Number of contending SUs ( ncontend )
Prob. of success with certain number of contending SUs
SU state
Contention
pactive  f (  )
  g (ncontend )
ncontend  n  f (  )
-
Prob. of success in contention period (  )
SU states are expressed in terms of 
[1] G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,”
IEEE Journal on Selected Areas in Communications, vol. 18, no. 3, pp. 535-547, Mar. 2000.
Sungkyunkwan Univ.
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Network System Lab.
Performance Evaluation
Performance comparison – Simulation/analysis

Throughput : transmitted bits per certain time (bits/s)
Energy efficiency : transmitted bits per Joule (bits/Joule)
Both throughput and energy efficiency are well matched with simulation
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6
5
x 10
3500
EH Analysis - CW = 8, 16
EH Simulation - CW = 8, 16
EH Analysis - CW = 16, 32
EH Simulation - CW = 16, 32
EH Analysis - CW = 32, 64
EH Simulation - CW = 32, 64
EH,AP Analysis - CW = 8, 16
EH,AP Simulation - CW = 8, 16
EH,AP Analysis - CW = 16, 32
EH,AP Simulation - CW = 16, 32
EH,AP Analysis - CW = 32, 64
EH,AP Simulation - CW = 32, 64
4.5
3000
4
2500
Energy efficiency (bits/J)
3.5
Throughput (bit/s)
3
2.5
2
1.5
1
0.5
0
50
EH Analysis - CW = 8, 16
EH Simulation - CW = 8, 16
EH Analysis - CW = 16, 32
EH Simulation - CW = 16, 32
EH Analysis - CW = 32, 64
EH Simulation - CW = 32, 64
EH,AP Analysis - CW = 8, 16
EH,AP Simulation - CW = 8, 16
EH,AP Analysis - CW = 16, 32
EH,AP Simulation - CW = 16, 32
EH,AP Analysis - CW = 32, 64
EH,AP Simulation - CW = 32, 64
60
70
80
90
2000
1500
1000
500
100
Sungkyunkwan Univ.
110
120
130
Number of SUs
140
150
160
170
180
190
0
50
200
10
60
70
80
90
100 110 120 130 140 150
Number of secondary users
160 170
180
190
200
Network System Lab.
Performance Evaluation

Throughput
Access probability makes some users to go into sleep mode
The throughput improves in the proposed EL-MAC protocol
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Energy efficiency
SUs have more chance to go to the sleep mode
The energy efficiency of the proposed EL-MAC is the best


3.8
180
3.6
160
3.4
140
Throughput (Mbps)
3
Energy Efficiency (bits/J)
15%
improvement
3.2
0.1
Conventional scheme - E
 h=0.1
Proposed with access probability - E
h =0.1
0.1
2.8
0.1
EL-MAC protocol - Eh=0.1
0.01
Conventional scheme - E
 h=0.01
2.6
Proposed with access probability - E
h=0.01
0.01
0.01
EL-MAC protocol - E
 h=0.01
2.4
120
100
Eh = 0.1
0.1
Conventional scheme - 
Eh= 0.1
0.1
Proposed with access probability - 
80
(l )
Eh = 0.1
0.1
EL-MAC protocol - 
0.01
Conventional scheme - Eh= 0.01
60
Eh= 0.01
0.01
Proposed with access probability - 
Eh= 0.01
0.01
EL-MAC protocol - 
40
2.2
20
2
1.8
50
55
60
65
70
75
80
Number of SUs
85
Sungkyunkwan Univ.
90
95
100
0
50
100%
improvement
55
60
( Eh )
65
70
75
80
Number of SUs
11
85
90
95
100
Network System Lab.
Conclusion

We have proposed a new Energy Level based MAC (EL-MAC) protocol


We have considered the access probability to decrease the number of
contending SUs
We have adopted the differentiated contention window based on the energy
level to decrease the energy consumption

We have proposed a Markov chain model to analyze the behavior of
the SUs for tractable performance

The proposed protocol can improve the throughput and the energy
efficiency in cognitive radio networks
Sungkyunkwan Univ.
12
Network System Lab.