presentation_2.pdf

Multi-hop Cognitive Radio Networks with RF Energy
Harvesting
(PhD Pre-Submission Seminar)
Soumen Mondal
Reg. No: NITD/PhD/EC/2017/00945
Email: sm.15ec1103@phd.nitdgp.ac.in
Under the Supervision of
Prof. Sumit Kundu,
(Professor, Dept. of ECE, NIT Durgapur)
&
Dr. Sanjay Dhar Roy,
(Associate Professor, Dept. of ECE , NIT Durgapur)
Department of Electronics and Communication Engineering
National Institute of Technology Durgapur
Soumen Mondal (NIT DGP) PhD Pre-Submission Seminar February 2, 2022 1 / 26

Table of Contents
1 Introduction (Chapter 1)
2 Literature Survey (Chapter 2)
3 Multihop Relaying in Energy Harvesting Cognitive Radio Networks
(Chapter 3)
4 Adaptive Energy Harvesting for Relaying Networks (Chapter 4)
5 Performance of Relaying Networks with Non-Orthogonal Multiple Ac-
cess (Chapter 5)
6 Conclusions and Future Works (Chapter 6)
7 Publications
8 References

Chapter 1: Introduction
Cognitive Radio Networks: To address the imbalance of the spectrum
scarcity and spectrum utilization.
Multi-hop Networks: To extend coverage area.
RF Energy Harvesting: Self-sustainable.

Chapter 2: Literature Survey
Cognitive Radio Networks [1–3]
Underlay Mode:PU present
Overlay Mode:PU absent
Relaying Network: [4–6]
Dual hop: Single relay
Multihop: Multiple relay
CSI in CR network [7–9]
Perfect CSI
Imperfect CSI
RF Energy Harvesting [10–12]
Time Switching Relaying (TSR)
Protocol
Power Splitting Relaying (PSR)
Protocol
Adaptive Energy Harvesting [13–15]
NOMA in Relay Network [16–24]
Relay
Data Link Interference Link
SU-Tx
PU-Tx PU-Rx
SU-Rx
Figure 1: System Model for Cognitive Radio Relay Network.
Energy Harvesting
at Relay
Source-to-Relay
Information Transmission
Relay-to-Destination
Figure 2: Time frame for TSR protocol.
Source-to-Relay Information
Transmission
Energy Harvesting at Relay
Relay-to-Destination
Figure 3: Time frame for PSR protocol.

Chapter 3: Primary behaviour-based energy harvesting in multihop CR network
SR0 SR1 SR2 SRN-1
SRN+1
SRN
PB PR HARVESTING LINK
DATA LINK
INTERFERENCE LINK
Figure 4: System Model for a multihop CR network.
( )
1
T
N
t
-
+
At Source
At 1st
Node
At 2nd
Node
At Nth
Node
At Destination
Total Time =T
t
t
t
t
t
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
INFORMATION PROCESSING.
t t
ON ON
OFF
PU PRESENT
SU HARVEST
Figure 5: Time frame TPB ≥ T.
( )
1
T
N
t
-
+
At Source
At Nth
Node
At Destination
Total Time =T+τ
t
t
t
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
At 2nd
Node
t ( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
INFORMATION PROCESSING.
ON OFF
PU PRESENT
SU HARVEST
t t
t OFF
ON ON ON
t
At 1st
Node
t ( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
t
t
t
At 3nd
Node
t ( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
( )
1
T
N
t
-
+
These two nodes are involved in
communication rest nodes are in harvesting
mode
Figure 6: Time frame TPB < T

Primary behaviour-based energy harvesting
Performance
matric
Case 1: TPB ≥ T Case 2: TPB < T
SINR γi =
min



ηPP hPT,i−1
2
dm
PT,i−1
τ

T−τ
N+1
,
Ipdm
i,PR
hi,PR
2


 hi−1,i
2
N0
γi =
min



ηPP hPT,i−1
2
dm
PT,i−1
τr
T−τ
N+1
,
Ipdm
i,PR
hi,PR
2


 hi−1,i
2
N0
Outage
Probability of
SU
O1 = Pr

min
i=1,2,..N+1
(γi ) ≤ γth

=
h
1 −

1 − Fγi (γth) N+1
i
O21 = 1 −
{1 − F1 (γth)}K
...

1 − FH−1 (γth) K
{1 − FH (γth)}(M+1)−(H−1)K
O22 =
h
1 −

1 − Fγi (γth) N+1
i
Ergodic
Capacity
C = min{C1, C2, ...CN+1},Ci = E{log2(1+γi )} C = min{C1, C2, ...CN+1},Ci = E{log2(1+γi )}
Throughput ξ1 = T−τ
(N+1)T
C ξ2 = T−τ
(N+1)(T+(H−1)τ)
C
• The closed form expression of CDF of SNR at ith
node, i.e., Fγi (γth)
Fγi (γth) = 1 +
1
λz
v
u
u
t4
γthN0
Ai λx
+
Ip
Ai λy
!
λz K1


v
u
u
t4
γthN0
Ai λx
+
Ip
Ai λy
!
1
λz

 −
1
λz
s
4γthN0λz
Ai λx
K1
s
4γthN0
Ai λx λz
!
−
1
λz
s
4Ipλz
Ai λy
K1
s
4Ip
Ai λy λz
!
+
s
4Ip
Ai λz λy
K1
s
Ip
Ai λz λy
!
−
1
λy
v
u
u
u
t
4Ip
Ai λz

γthN0
Ipλx
+ 1
λy
K1


v
u
u
t 4Ip
Ai λz
γthN0
Ipλx
+
1
λy
!
where
∞
R
0
e
−
β
4x
−γx
dx =
q
β
γ
K1
√
βγ

, K1 (·) is the modified Bessel function of second kind first order.

Interference probability of primary network
χ =
h
Pr
n
Pimp
0 |h0,PR |2
IP
oi
+
h
Pr
n
Pimp
0 |h0,PR |2
IP
o
Pr
n
Pimp
1 |h1,PR |2
IP
oi
+ ... +
h
Pr
n
Pimp
0 |h0,PR |2
IP
o
...Pr
n
Pimp
N |hN,PR |2
IP
oi
=
N
X
k=0
Pr
n
Pimp
k |hk,PR |2
IP
o k−1
Y
l=0
Pr
n
Pimp
l |hl,PR |2
≤ IP
o
, Pimp
i−1 =
IP
|b
hi−1,PR |
(3.80)
Upper Bound : χUB
= 1 − 1
2N+1 • χUB
|N=1= 0.75, • χUB
|N=2= 0.875
To restrict the received interference probability of primary network within a
predefined probability (χ δ), we need to scale down the transmit power of
secondary nodes using a back-off power control scheme [8].
Ps,imp
inti−1
= ψPimp
inti−1
Ps,imp
i−1 = min(PEHi−1
, ψPimp
inti−1
)
(3.81)
The backoff coefficient (ψ) can be computed for a specified value of δ.
[8] V. N. Q. Bao, T. Q. Duong, and C. Tellambura, ”On the performance of cognitive underlay multihop networks with
imperfect channel state information,” IEEE Transactions on Communications, vol. 61, no. 12, pp. 48644873, 2013.

Results
0 5 10 15 20 25 30 35 40 45 50
10
−3
10
−2
10
−1
10
0
P
P
(dBW)
OP
of
the
Secondary
Network
η=0.4, ρ=1, TPB
=70ms, simulation
η=0.4, ρ=1, T
PB
=70ms, analytical
η=0.4, ρ=0.8, T
PB
=70ms, simulation
η=0.4, ρ=0.8, T
PB
=70ms, analytical
η=0.6, ρ=1, T
PB
=70ms, simulation
η=0.6, ρ=1, TPB
=70ms, analytical
η=0.6, ρ=0.8, TPB
=70ms, simulation
η=0.6, ρ=0.8, TPB
=70ms, analytical
η=0.8, ρ=1, TPB
=70ms, simulation
η=0.8, ρ=1, TPB
=70ms, analytical
η=0.8, ρ=0.8, T
PB
=70ms, simulation
η=0.8, ρ=0.8, TPB
=70ms, analytical
40 42 44 46 48 50
10
−3
10
−2
ρ=0.8
ρ=1
Figure 7: Outage probability of secondary network as a function of PP (dBW).
ρ = 1, PP ↑, PEHi
↑, O ↓, → η ↑, O ↓
ρ 1 • PP low, PEHi−1
Pimp
inti−1
, Pact = PEHi
, ψ = 1, Oip = Op
• PP high, PEHi−1
Pimp
inti−1
, Pact = Pimp
inti−1
, ψ ↓, Pact ↓, Oip Op

...Results
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
ρ
Interference
Probability
of
Primary
Network
N=2, PP
=60dBW
N=2, PP
=55dBW
N=1, P
P
=60dBW
N=1, P
P
=55dBW
χ
UB
=0.875, N=2
Without back−off power control
With back−off power control
χUB
=0.75, N=1
Figure 8: Interference probability of primary network as a function of ρ.
ρ ↑, δ ↓, • ρ → 1, δ → 0, N ↑, δ ↑,• χUB
N=2 = 0.875, χUB
N=1 = 0.75
ρ 1, δ ↑ but δ 0.3 → ψ 1, δ ↓
[JP8] S. Mondal, S. D. Roy, and S. Kundu, “Primary behaviour-based energy harvesting multihop cognitive radio
network,” IET Communications, vol. 11, no. 16, pp. 2466-2475, 2017.
[JP7] S. Mondal, S. D. Roy, and S. Kundu, “Energy harvesting based multihop relaying in cognitive radio network,”
Wireless Personal Communications, vol. 97, no. 4, pp. 63256342, Dec 2017.

Chapter Summary
The performance of the multihop relaying network under cognitive sce-
nario is investigated.
The periodic behavioural pattern of primary beacon and its periodicity
is seen to have significant impact on secondary network performance.
The outage performance of secondary network degrades when CSI of
the channel between secondary transmitting nodes and primary receiver
is imperfect as compared to the perfect case.
An optimum duration of harvesting is indicated which maximizes the
throughput.

Chapter 4: Multihop Network with Adaptive Energy
Harvesting
0 1 M M+1
g1 g2 gM gM+1
D
D/(M+1)
Figure 9: System Model for Energy Harvesting based Multihop Network.
β1: EH
(1-β1): IT-RX
TX
β2: EH
(1-β2): IT-RX
TX
βM: EH
TX
IT-RX
T/(M+1) T/(M+1) T/(M+1)
T/(M+1) T/(M+1)
T/(M+1)
T/(M+1)
T
gM+1
0
1
M
M+1
g1
g2
gM
(1-β2): IT-RX
Figure 10: Time frame structure for Energy Harvesting based Multihop Network.

Multihop Network with Adaptive Energy Harvesting
• The transmit power of ith
node : Pi = ηi Pi−1gi βi = Pt
i
Q
j=1
ηj
i
Q
j=1
gi
i
Q
j=1
βj
• The SNR at (i+1)th
node: γi+1 = (1−βi+1)Pi gi+1
N0
= Pt
N0
(1 − βi+1)
i
Q
j=1
ηj
i+1
Q
j=1
gi
i
Q
j=1
βj
• The SNR at (M +1)th
node: γM+1 = Pt
N0
M
Q
j=1
ηj
M+1
Q
j=1
gj
M
Q
j=1
βj , where, βM+1 = 0
• The end to end SNR of a DF relaying network : γe2e = min(γ1, γ2, ...., γM+1)
• To maximize the end to end SNR, the SNRs of all nodes should be same. In
our scheme, PS ratio of each relay node (βi ) is dynamically adapted to equalise
SNR at each node [25]. Hence, γ1 = γ2 = .... = γM = γM+1
(1−β1)ηPt g1
N0
= .. = (1−βM )β1β2..βM−1ηM
Pt g1g2..gM
N0
= β1β2...βM ηM
Pt g1g2...gM gM+1
N0
βi =
1
1 + (1 − βi+1)gi+1
=
M+1
P
m=i+2
M+1
Q
m
gm
1 +
M+1
P
m=i+1
M+1
Q
m
gm
, i ∈ (1, M) (4.46)
[25] M. Ashraf, J.-W. Jang, J.-K. Han, and K. G. Lee, ”Capacity maximizing adaptive power splitting protocol for
cooperative energy harvesting communication systems,” IEEE Commun Letters, vol. 22, no. 5, pp. 902-905, 2018.

Multihop Network with Adaptive Energy Harvesting
• The outage probability of the multihop network:
O = Pr {min(γ1, γ2, ...., γM , γM+1) γth} (4.47)
• The ergodic capacity at the ith
node
Ci = E{log2(1 + γi )} i =∈ (1, M + 1) (4.48)
• The throughput of the multihop network:
τth =
1
(M + 1)
min{C1, C2, ..., CM , CM+1} (4.49)
1 2 3 4 5 6 7
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Relay index (m
th
node)
Average
value
of
β
M=7
M=6
β
2 β
3
β4
β6
β7
β1
β5
Figure 11: Statistics of β with respect to relay index.

Results
1 2 3 4 5 6 7 8 9 10
10
−2
10
−1
10
0
Number of Relays (M)
Outage
probability
Adaptive
Fixed
Figure 12: Outage probability with respect to number of intermediate relays.
• M ↑, ηM
↓ d ↓ γi ↓ O ↑ • M ↑, ηM
↓ d ↓ γi ↑ O ↓
[JP3] S. Mondal, S. D. Roy, and S. Kundu, “ Outage and throughput performance of a multihop network with an
adaptive power splitting-based energy harvesting,” International Journal of Electronics Letters (Taylor Francis), pp.
1-14, 2020.

Chapter Summary
The harvesting time is dynamically adapted to achieve a transmit power
to a level of maximum allowable transmit power estimated by the tol-
erable received interference threshold at the primary receiver.
The statistical variation of β is shown, with its dynamic behaviour
to equalise SNR of all the nodes. We also developed a generalised
expression for β for each node.
The adaptive PSR based energy harvesting network maximizes the end
to end SNR of the multihop network and provides better performance
in terms of outage and throughput as compared with the conventional
PSR based energy harvesting schemes.

Chapter 5: Performance of Relaying Networks with
Non-Orthogonal Multiple Access
D1
D2
DM
NS
Feedback
R
EH link
Data transmission link
Feedback link
D1 signal
detection
SIC for D1
signal
D2 Signal
detection
DM signal
detection
SIC for D1, D2,..,DM
signals
Figure 13: NOMA system with M users.
• Transmit Antenna Selection scheme: i∗
= arg max
i∈{1,NS }
b
hSi R
2
• The PDF of channel gain between the source to the relay:
f|hSi∗R |
2 (x) = NS
h
F|hSi R |
2 (x)
iNS −1
f|hSi R |
2 (x) =
NS −1
P
n=0
NS −1
n

(−1)
n (n+1)
λSi R
e
−
(n+1)x
λSi R

Relaying Networks with Non-Orthogonal Multiple Access
• Imperfect CSI: The PDF of |b
hSi∗R |2
:
f
b
hSi∗R
2 (x) =
∞
Z
0
f
ĥSi R
2
hSi R
2 (x|y)f
hSi∗R
2 (y) dy
=
∞
Z
0
1
1 − ρ2

λ2
Si R
e
−
x+y

1−ρ2

λ
Si R I0


2ρ
√
xy
1 − ρ2

λSi R


NS −1
X
n=0
NS −1
n

(−1)
n (n + 1)
λSi R
e
−
(n+1)y
λSi R dy
=
NS −1
X
n=0
NS −1
n
(−1)n
(n + 1)
λSi R

1 + n 1 − ρ2
e
−
(n+1)x
λSi R
h
1+n

1−ρ2
i
(5.17)
• The received signal at the relay: yR (k) =
M
P
i=1
p
ai PS
b
hSi∗ R
2
x(k) + nR (k)
• The received signal at mth
user: yDm (k) =
p
amPR
b
hRDm
2
x(k)
| {z }
Desired Signal
+
M
X
i=1,i6=m
p
ai PR
b
hRDm
2
x(k)
| {z }
interference
+ nDm (k)
| {z }
noise
• The PDF of |hRDm |
2
with the aid of order statistics [26]:
f
hRDm
2 (y) = M!
(m−1)!(M−m)!
1
λRDi
m−1
P
j=0

m−1
j

(−1)j
e
−
M−m+j+1
λRDi
y
[26] J. Men and J. Ge, Performance analysis of non-orthogonal multiple access in downlink cooperative network, IET
Communications, vol. 9, no. 18, pp. 22672273, 2015.

Outage Performance with perfect SIC
Metrics At relay At destination
SINR γm
R =
amPS |ĥSi∗ R |
2
PS |ĥSi∗ R |
2 M
P
k=m+1
ak +N0
γm
Dm
=
amPR |hRDm |2
PR |hRDm |2
M
P
k=m+1
ak +N0
CDF Fm
γR
(x) =
Pr

min
k=1,2,..,m
(γk
R ) γth

Fm
γD
(x) =
Pr

min
k=1,2,..,m
(γk
m) γth

• The effective SINR of the mth
signal: f
γm
R = min
k=1,..,m
(γk
R ), g
γm
Dm
= min
k=1,..,m
(γk
Dm
)
• Outage probability of mth
signal: Om
= Pr
n
min

f
γm
R , g
γm
Dm

γth
o
O
m
=
NS −1
X
n=0
NS −1
n

(−1)
n
Ce
−Cδ
+
M!
(m − 1) ! (M − m) !
1
λRDi
m−1
X
j=0
NS −1
X
n=0
NS −1
n

m−1
j

(−1)
n+j C
B

BγthΓ (0, Cδ) +
∞
X
k=2
(−1)k
(Bδ)k
ζk k!



e
−Cδ
k−1
X
l=1
(l − 1) ! (−C)k−l−1
(k − 1) ! (γth)l
−
(−C)k−1
(k − 1) !
Ei (−Cδ)





(5.23)
where, B = M−m+j+1
λRDi
, and C =
(n+1)
λSi R
h
1+n

1−ρ2
i

Outage Performance with Imperfect SIC
Metrics At relay At destination
SINR γm
R,ipSIC =
amPS |b
hSi∗ R |
2
PS h0
Si∗ R
2 m−1
P
k=1
ak +PS |b
hSi∗ R |
2 M
P
k=m+1
ak +N0
γm
Dm,ipSIC =
amPR |hRDm |2
PR |h0
RDm
|
2
m−1
P
k=1
ak +PR |hRDm |2
M
P
k=m+1
ak +N0
CDF Fm
γR,ipSIC
(x) =
Pr

min
k=1,2,..,m
(γk
R,ipSIC ) γth

Fm
γD,ipSIC
(x) =
Pr

min
k=1,2,..,m
(γk
Dm,ipSIC ) γth

• The residual interference is assumed as Rayleigh faded distribution with zero
mean and ζλSi R variance i.e. h
0
Si∗ R ∼ CN(0, ζλSi R ) , (0 ≤ ζ ≤ 1)[27].
• The effective SINR of the mth
signal:
^
γm
R,ipSIC = min
k=1,..,m
(γk
R,ipSIC ), ^
γm
Dm,ipSIC = min
k=1,..,m
(γk
Dm,ipSIC )
• Outage probability of mth
signal :Om
ipSIC = Pr
n
min

^
γm
R,ipSIC , ^
γm
Dm,ipSIC

γth
o
• The upper bound of M: a1 − γth
M
P
l=2
al 0 =⇒ M =
j
2+γth
γth
k

Results
−20 −15 −10 −5 0 5 10 15 20
10
−3
10
−2
10
−1
10
0
PS
/N0
(dB)
O
ipSIC
m
S1
S
2
, ζ=0
S
2
, ζ=0.01
S2
, ζ=0.02
S
2
, ζ=0.03
ζ=0,0.01,0.02,0.03
Figure 14: Outage probability as a function of
PS
N0

for
different levels of imperfection
• PS
N0
↑, γm
R ↑ Eh ↑ PR ↑ γm
Dm
↑ Om
↓
−3 −2 −1 0 1 2 3 4 5
10
−4
10
−3
10
−2
10
−1
10
0
γth
(dB)
Outage
Probability
N
S
=1, Simulation
NS
=1, Analytical
NS
=2, Simulation
NS
=2, Analytical
NS
=3, Simulation
NS
=3, Analytical
N
S
=1,2,3
ρ=0.7, M=2,PS
=30dBW
Figure 15: Outage probability as a function of γth for different
number of antennas
• NS ↑, γm
R ↑, Eh ↑ PR ↑ γm
Dm
↑ Om
↓
JP1. S. Mondal, S. D. Roy, and S. Kundu, “Outage Analysis for NOMA based Energy Harvesting Relay Network with
Imperfect CSI and Transmit Antenna Selection,” IET Communications , 2020.

Chapter Summary
The outage probability of an energy harvesting relaying based NOMA
network with the TAS scheme is analyzed.
The imperfect CSI has significant impact on the outage probability of
a NOMA network.
The allowable order for NOMA network is estimated for a given target
SINR.
A multihop network is proposed which supports concurrent transmission
of dual hop and multihop network employing NOMA fundamentals.

Chapter 6: Conclusions
An analytical framework is developed to investigate the impact of en-
ergy harvesting on the throughput and outage of the multihop sec-
ondary network.
The impact of the presence of primary beacon and its periodic nature
on the outage and throughput of the secondary multihop network under
cognitive scenario is investigated.
An adaptive PSR scheme is proposed for a multihop network. The
proposed scheme outperforms conventional fixed energy harvesting.
The performance of an mth order NOMA system involving TAS scheme
at the source is analysed.
The imperfect CSI has significant impact on the performance of the
NOMA network.

...Conclusions
The imperfect SIC has detrimental effects on system performance, so
SIC design is an important issue in NOMA based network.
The essence of the proposed network is the co-existence of a multihop
and N dual hop transmissions in the same infrastructure as that of the
multihop network.
• The research carried out is useful for design and analysis of multihop
cognitive radio network involving energy harvesting. The research propose
several schemes for efficient use of energy harvesting in order to make mul-
tihop networks self sustainable. Specially, the proposed schemes in NOMA
network may be useful for future generation network.
• Future Works: Optimal use of non-linear energy harvesting, the trade-off
between spectrum efficiency and energy efficiency, advanced hybrid multiple access
between OFDMA and NOMA like MUST can be also a direction of research.
Multihop NOMA network is an open problem.

Research Publications
Published Journal Papers
JP1. S. Mondal, S. D. Roy, and S. Kundu, “Outage Analysis for NOMA based Energy Harvesting Relay Network with Imperfect
CSI and Transmit Antenna Selection,” IET Communications (IET Digital Library), 2020. [SCI indexed, IF: 1.779]
JP2. S. Mondal, S. D. Roy, and S. Kundu, “On performance of multihop energy harvesting CRN in presence of co-channel
interferers,” International Journal of Electronics Letters (Taylor Francis), 2020. [Scopus indexed, CiteScore : 1.2]
JP3. S. Mondal, S. D. Roy, and S. Kundu, “ Outage and throughput performance of a multihop network with an adaptive
power splitting-based energy harvesting,” International Journal of Electronics Letters (Taylor Francis), pp. 1-14, 2020.
[Scopus indexed, CiteScore: 1.2]
JP4. S. Ghosh S. Mondal, S. D. Roy, and S. Kundu, “ D2D Communication with Energy Harvesting Relays for Disaster
Management,” International Journal of Electronics (Taylor Francis), pp. 1-19, 2020. [SCI indexed, IF: 1.004]
JP5. S. Mondal, S. D. Roy, and S. Kundu, “ Performance Analysis of a Cognitive Radio Network with Adaptive RF Energy
Harvesting,” International Journal of Electronics Letters (Taylor Francis), pp. 1-14, 2019. [Scopus indexed, CiteScore:
1.2]
JP6. S. Mondal, S. D. Roy, and S. Kundu, “Closed-Form Outage Probability Expressions for Multihop Cognitive Radio Network
with Best Path Selection Schemes in RF Energy Harvesting Environment,” Wireless Personal Communications (Springer),
vol. 103 no. 3. , pp. 2197-2212, 2018. [SCI indexed, IF: 1.061]
JP7. S. Mondal, S. D. Roy, and S. Kundu, “Primary behaviour-based energy harvesting multihop cognitive radio network,”
IET Communications (IET Digital Library), , vol. 11, no. 16, pp. 2466-2475, 2017. [SCI indexed, IF: 1.779]
JP8. S. Mondal, S. D. Roy, and S. Kundu, “Energy harvesting based multihop relaying in cognitive radio network,” Wireless
Personal Communications (Springer), vol. 97, no. 4, pp. 63256342, Dec 2017. [SCI indexed, IF: 1.069]
JP9. K. Chandra, S. Mondal, S. D. Roy, and S. Kundu, “Outage probability analysis of a secondary user in an underlay dual
hop cognitive amplify and forward relay network,” Perspectives in Science(Elsevier), vol. 8, pp. 117-120, 2016. [Scopus
indexed, CiteScore: 1.5]

...Research Publications
Submitted Journal Papers
JS1. S. Mondal, S. D. Roy, and S. Kundu, “Partial Relay Selection in Energy Harvesting based NOMA Network with imperfect
CSI,” Wireless Personal Communications (Springer), 2018
JS2. S. Mondal, S. D. Roy, and S. Kundu, “Performance of a Hybrid Dual-hop and Multihop Network based on NOMA,”
Wireless Network (Springer)
Conference Papers
C1. S. Mondal, S. D. Roy, and S. Kundu, “Adaptie Energy Harvestig with Relay Selectin Schemes in an Ordered NOMA
Network”, in IEEE 26th
National Conference on Communications (NCC), Kharagpur, India, Feb, 2020, pp. 1-6.
C2. S. Mondal, S. D. Roy, and S. Kundu, “Outage Analysis in Dual Hop Relay Network with an Adaptive Energy Harvesting
Scheme”, in IEEE Calcutta Conference (CALCON), Kolkata, India, Feb, 2020, pp. 292-296.
C3. N. Mishra, S. Mondal, S. D. Roy, and S. Kundu, “Cognitive Machine to Machine Communication with Energy Harvesting in
IoT networks”, in IEEE 11th
International Conference on Communication Systems Networks (COMSNETS), Bangaluru,
India, January, 2019, pp. 672–677.
C4. S. Mondal, S. D. Roy, and S. Kundu, “Outage analysis of a multihop cognitive network with energy harvesting from a
primary cluster”, in IEEE Recent Trends in Electronics, Information Communication Technology (RTEICT), Bangaluru,
India, May, 2017, pp. 81-85.

References
[1] M. Marcus, J. Burtle, B. Franca, A. Lahjouji, and N. McNeil, “Federal communications commission spectrum policy
task force,” Report of the unlicensed devices and experimental licenses working group, 2002.
[2] J. Mitola, “Cognitive radio for flexible mobile multimedia communications,” in 1999 IEEE International Workshop on
Mobile Multimedia Communications (MoMuC’99)(Cat. No. 99EX384), IEEE, 1999, pp. 3–10.
[3] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “A survey on spectrum management in cognitive radio
networks,” IEEE Communications magazine, vol. 46, no. 4, pp. 40–48, 2008.
[4] Z. Wang, Z. Chen, L. Luo, Z. Hu, B. Xia, and H. Liu, “Outage analysis of cognitive relay networks with energy
harvesting and information transfer,” in 2014 IEEE International Conference on Communications (ICC), IEEE, 2014,
pp. 4348–4353.
[5] M. Mao, N. Cao, Y. Chen, and Y. Zhou, “Multi-hop relaying using energy harvesting,” IEEE Wireless Communications
Letters, vol. 4, no. 5, pp. 565–568, 2015.
[6] H. K. Boddapati, S. Prakriya, and M. R. Bhatnagar, “Outage analysis of cluster-based multi-hop cognitive networks
using blar routing protocol,” in 2016 International Conference on Signal Processing and Communications (SPCOM),
IEEE, 2016, pp. 1–5.
[7] J. Chen, J. Si, Z. Li, and H. Huang, “On the performance of spectrum sharing cognitive relay networks with imperfect
csi,” IEEE Communications Letters, vol. 16, no. 7, pp. 1002–1005, 2012.
[8] V. N. Q. Bao, T. Q. Duong, and C. Tellambura, “On the performance of cognitive underlay multihop networks with
imperfect channel state information,” IEEE Transactions on Communications, vol. 61, no. 12, pp. 4864–4873, 2013.
[9] X. Zhang, J. Xing, Z. Yan, Y. Gao, and W. Wang, “Outage performance study of cognitive relay networks with
imperfect channel knowledge,” IEEE Communications Letters, vol. 17, no. 1, pp. 27–30, 2013.
[10] A. A. Nasir, X. Zhou, S. Durrani, and R. A. Kennedy, “Relaying protocols for wireless energy harvesting and information
processing,” IEEE Transactions on Wireless Communications, vol. 12, no. 7, pp. 3622–3636, 2013.
[11] Y. Gu and S. Aı̈ssa, “Rf-based energy harvesting in decode-and-forward relaying systems: ergodic and outage capacities,”
IEEE Transactions on Wireless Communications, vol. 14, no. 11, pp. 6425–6434, 2015.
[12] C. Xu, M. Zheng, W. Liang, H. Yu, and Y.-C. Liang, “Outage performance of underlay multi-hop cognitive relay
networks with energy harvesting,” IEICE transactions on communications, vol. 6, no. 20, pp. 1148–1151, 2016.
[13] F. K. Ojo and M. F. M. Salleh, “Throughput analysis of a hybridized power-time splitting based relaying protocol for
wireless information and power transfer in cooperative networks,” IEEE Access, vol. 6, pp. 24 137–24 147, 2018.

Energy
Storage
Power Management
Module
RF Energy
Harvester
Application
Lower-power
Microcontroller
Lower-Power RF
Transceiver
Antenna
Antenna
DC
output
RF Energy Harvester
Capacitor Voltage
Multiplier
Impedance
matching
RF
input
Figure 16: A general transceiver architecture of an RF energy harvesting [28].

23], the highest power density (84 nW/cm2) that appears around 1800 MHz is three o
ude higher than the power density (0.18 nW/cm2) measured around 2.4 GHz.
Table 1. The power density of different energy sources.
Source Conditions Density
Unshaded 20 mW/cm2
Morning
Shaded by tree 3 mW/cm2
Unshaded 60 mW/cm2
Solar
[37]
Noon
Shaded by tree 10 mW/cm2
DTV 470–610 MHz 0.89 nW/cm2
GSM900 (MT) † 880–915 MHz 0.45 nW/cm2
GSM900 (BT) ‡ 920–960 MHz 36 nW/cm2
GSM1800 (MT) † 1710–1785 MHz 0.5 nW/cm2
GSM1800 (BT) ‡ 1805–1880 MHz 84 nW/cm2
RF
(Average)
[23]
3G (MT) † 1710–1785 MHz 0.46 nW/cm2
3G (BT) ‡ 2110–2170 MHz 12 nW/cm2
WiFi 2.4–2.5 GHz 0.18 nW/cm2
10 K temp diff 0.22 nW/cm2
Thermal [38]
Human body
(4 × 8 cm) 35 K temp diff 0.47 nW/cm2
Push bottom [39] 2 mJ/N
Piezoelectric
Human biomechanics [40] 7.34 µW/cm3
‡ BT, Base transmitter. † MT, Associated mobile transmitter.

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