22 7404 latex power beaconb

Indonesian Journal of Electrical Engineering and Computer Science
Vol. 7, No. 3, September 2017, pp. 802 ∼ 808
DOI: 10.11591/ijeecs.v7.i3.pp802-808 802
Optimal Energy Harvesting Scheme for Power
Beacon-Assisted Wireless-Powered Networks
Dinh-Thuan Do
Faculty of Electronics Technology, Industrial University of Ho Chi Minh City
12 Nguyen Van Bao, Go Vap Dist., Ho Chi Minh City, Vietnam
e-mail: dodinhthuan@gmail.com
Abstract
In this paper, we consider one-way relay with energy harvesting system based on power beacon
(PB), in which the relay node harvests transmitted power from the PB station to forward signals to destination.
We also analyse the relay network model with amplify-and-forward (AF) protocol for information cooperation
and Power Splitting-based Relaying (PSR) protocol for power transfer. In particular, the outage probability
and optimal energy harvesting (EH) power splitting fraction of novel scheme in are presented. We obtain
analytical closed-form expression of optimal energy harvesting (EH) power splitting fraction to minimize the
outage probability of system. Using numerical and analytical simulations, the performances of different cases
are presented and discussed.
Keywords: power beacon, Energy Harvesting, power splitting-based relaying, Amplify and Forward
Copyright c 2017 Institute of Advanced Engineering and Science. All rights reserved.
1. INTRODUCTION
Recently, wireless access will be a reality in fifth generation (5G) wireless systems, with
a series of emerging technology, such as massive multiple-input and multiple-output (MIMO),
device-to-device communication and small cell architectures, which have launched a huge data
volume in wireless services, such as mobile gaming, mobile TV and mobile Internet. Furthermore,
with fast increasing number of users owning smartphones and tablets, and hence one of the most
critical problem is that limited operation lifetime of mobile devices due to finite energy ability. To
address this hard issue, radio-frequency (RF) based energy harvesting model has received a
considerable research interest [1]. A promising technology of RF-assisted energy signals enables
wireless energy transfer (WET) to provide continuous and convenient power supply to energy-
aware wireless terminals. As a result, wireless powered communication (WPC) system together
with wireless devices (WDs) is designed to transfer data to the information receivers (IRs), e.g.,
sensor nodes. Such terminals is powered up by the means of wireless powered equipment as a
dedicated energy transmitters (ETs). In fact, wireless recharging is one of the most desirable new
features for mobile devices to eliminate the need of power cords and power chargers. To realize
this wireless power transfer, a novel network architecture where stations called power beacons
(PBs) are installed in traditional cellular network for boosting operation efficiency of mobiles and
sensors via microwave radiation known as microwave power transfer (MPT)
In [2][9], the works have lately adopted some transmission policies for EH cooperative
networks. EH relays were first implemented in cooperative communication [2]. In [3][5], assuming
a perfect EH model under which the energy arrival time and the amount of harvested energy are
calculated prior to transmit such energy, regarding EH relay systems, there were some power
allocation policies given. Nevertheless, due to the random energy arrival time and the amount of
harvested energy, the deterministic EH model seems to be abstract. Thus, under general energy
harvesting profiles, the authors in [6][9] proposed a number of transmission policies. Particularly,
there were several joint relay selection and power allocation schemes given due to the stationarity
and erodicity of the energy harvesting process [6]. Likewise, in [7], [8], since during any time of
data transmission, energy can be scavenged, several power allocation schemes for cooperative
EH networks were put forward. The study in [9] based on what have been accomplished in [7]
Received April 22, 2017; Revised August 12, 2017; Accepted August 27, 2017

IJEECS ISSN: 2502-4752 803
and [8] to expand the buffer-aided link adaptive EH relay system, in which the change of EH rates
can be positively assisted.
In [10], the authors planned the novel impression of deploying dedicated power nodes
to enable wireless power transfer in an existing cellular network, named power beacons (PBs).
The densities and transmit power of PBs are investigated. By using tool of the stochastic ge-
ometry theory under data links’ outage constraint. With this PB based energy transfer, we thus
could consider a new network , so-called PB-assisted wireless networks. In such networks, each
user can harvest wireless energy from the dedicated PB. For this new model, the optimization
problem need be solved to optimally allocate the resources of powered network including power
and time fraction for wireless power processing and information transmission. To the best of our
knowledge, this is still an open question, which motivates this paper. In this paper, we consider
a PB relaying network consisting of one PB and relay-destination pair. We assume that the PB
are connected to constant power supplies. Each AP (access point) station aims to collect the
information from its associated source. It is assumed that each source has no embedded energy
supply but has the ability to harvest and store the energy from RF signals broadcast by its AP.
The PB is installed to assist APs during the energy harvesting phase. Specifically, we formulate a
closed-form expression of the optimal resource power allocation of energy harvesting protocol to
obtain optimal outage performance.
The rest of the paper is organized as follows. Section II describes the signal and channel
models of the EH enabled relaying network. In Section III, the outage probability and throughput
are formulated and solved in order to maximize performance. Numerical results and comparisons
are presented in section IV. Finally, summarizing remarks are given in section V.
2. SYSTEM MODEL
Figure 1. The system model
In this paper, we consider a PB-assisted relaying network consisting of one multi-antenna
PB and relay-destination pairs. In view of the state of art and trend of RF energy transfer ,
the considered network setup is very likely to find its applications in the practical scenario of
small cells, such as picocells (range from 10 to 100 meters) and femtocells (WiFi like range),
which has been regarded as one of the key enabling technologies of the upcoming 5G cellular
networks. It is also worth mentioning that although introducing the PB may result in some extra
cost and complexity to the system, this could be beneficial as a whole based on the following
considerations:
(i) The PB could be dedicated designed for power transfer only and thus can achieve a higher
energy harvesting efficiency by exploiting the benefits of energy beamforming enabled by
multiple antennas.
(ii) The PB design is adapted to applications required low power such as in small cell networks.
Optimal Energy Harvesting Scheme for Power Beacon-Assisted ... (Dinh-Thuan Do)

804 ISSN: 2502-4752
(iii) It can be found optimal energy level for energy harvesting phase and calculated at PB. As a
result, model of PB can provide wireless charging services in real life.
The entire communication consists of two different phases, namely, energy harvesting
and information transmission. Assuming a power fraction of transmitted power of PB during the
first phase is assigned to energy transfer, in which the AP station harvest energy from the PB
to forward signal to destination D. During the energy harvesting processing stage, the received
signal at the AP can be expressed as
yr = PShT
1 x + nr, (1)
where nr is the additive white Gaussian noise (AWGN) with with the variance as N0.
It is noted that the PB is equipped with multiple antennas, energy beamforming is de-
ployed to progress the performance of energy transfer, i.e.,
x = ws, (2)
where w is the beamforming vector with w = 1 and s is the energy symbol with unit power. There-
fore, the optimal beamforming vector is given by
w =
hH
h
2 , (3)
in which (.)
H
denotes the Hermitian transpose matrix.
Following the principle of power splitting -based relay prototcol, the first part in the re-
ceived RF signal is allocated for energy gathering with power fraction ρ ∈ [0, 1]. Applying novel
energy harvesting principle, the total expected energy at the end of the first phase at the AP can
be calculated as
Eh = ηρ PS h
2
+ N0 T, (4)
where η (0 < η ≤ 1) is the energy conversion efficiency of the EH circuit, T is block time of signal
processing period, PS is transmit power of the PB. The harvested power at the AP use to forward
information to the destination node, e.g. mobile users, this procedure is deployed in the second
stage.
Next, the transmitted power at the AP is expressed as
Pr =
Eh
T
= ηρ PS h
2
+ N0 (5)
In practical applications, Pr is greater than 0, which results in 0 < ρ ≤ 1. At the same time of
the first stage, the RF signal perform an allocation as
√
1 − ρyr for information processing. The
received signal in the information processing stage is given by
˜yr = 1 − ρ PShx + nr + np, (6)
in which np denotes is the additional baseband Gaussian noise with zero mean and variance
N0. In this scenario, the power splitting fraction is selected appropriately for obtaining the optimal
outage performance. In this model, AF scheme is applied in relay node, after that the transmitted
signal at relay in the second hop is expressed as
xr = PrG (ρ) ˜yr. (7)
In this proposed architecture, the AP is assumed that it equipped by amplify circuit to
broaden harvested power and then using such power for the next processing stage. The relay
amplifies the received signal and the transmitted signal from the relay by the factor as below
G (ρ) =
1
(1 − ρ) PS h
2
+ N0 + N0
(8)
IJEECS Vol. 7, No. 3, September 2017 : 802 ∼ 808

IJEECS ISSN: 2502-4752 805
In the second phase, AP transmits information to mobile user via channel link using the
energy harvested in the ﬁrst phase. Hence, the received signal As a result, received signal at
mobile user is formulated by
yd = gxr + nd
= (1−ρ)PS Pr
(1−ρ)(PS h 2
+N0)+N0
hgxS
+g Pr
(1−ρ)(PS h 2
+N0)+N0
√
1 − pnr + np + nd
(9)
where nd is additive Gaussian noise with zero mean and variance N0 and then, the signal-to-noise
ratio (SNR) at the end-user can be calculated as.
SNR =
PS h
2
|g|
2
|g|
2
N0 + |g|2
N0
1−ρ + N0
PrG(ρ)2
(1−ρ)
(10)
3. OPTIMAL OUTAGE PERFORMANCE
In this section, to address optimal transmission mode, a RTS (requestto-send)/CTS (clear-
to-send) scheme is deployed with assumption of faultless channel assessment. Before starting
the information transmission, the PB node sends a RTS packet to the the AP node. The AP node
analyse received pilot-driven data and can estimate the channel gain h. After receiving the RTS,
the AP node returns a CTS packet to the source PB. As a result, the AP node can obtain full CSI,
i.e., h before the entire transmission. To obtain the values of g at the PB, we assume to use similar
scheme. Similarly, the AP can also estimate g by itself and send feedback to the PB node. It is
worth noting that the effect of channel estimation error is out of the scope of our paper. Through
the RTS/CTS mechanism, which is compatible with IEEE 802.11 series standards, the AP and PB
node can obtain the full CSI before the entire transmission power and data signal. Surely, extra
overhead and energy consumption are added to the RTS/CTS processing mechanism in term of
channel estimation. But, this hard problem is out of topic of this paper In this investigation, the
problem here is that using the knowledge of h and g, we aim to minimize the outage probability
which corresponding to maximize the instantaneous SNR at the mobile user.
It is worth noting that the outage event can be expressed as
OP = Pr (SNR < γ0) , (11)
in which γ0 is the threshold SNR.
Therefore, the optimization problem of outage probability can be written as
maximize SNR (ρ)
s.t. 0 < ρ < 1
(12)
To derive optimal value of power splitting fraction of EH, it is noted that SNR (ρ) is a
nonnegative continuous function ρ . In fact, SNR (ρ) is a concave function on 0 < ρ < 1 . Here,
the ﬁrst order derivative of SNR (ρ) with respect to optimal power splitting fraction is derived as
result of below equation
∂SNR (ρ)
∂ρ
= 0, 0 < ρ < 1 (13)
Replacing expression of Pr and G, we obtain new expression of the end-to-end SNR as
below
SNR =
PS h
2
|g|
2
ρ (1 − ρ)
−|g|
2
N0ρ2 + 2N0|g|
2
− N0 ρ + N0 +
N2
0
h 2
PS +N0
(14)
For simplicity, it can be expressed SNR as
SNR =
ax (1 − ρ)
−bρ2 + cρ + d
(15)

806 ISSN: 2502-4752
in which, we denote
a = PS h
2
|g|
2
, (16)
b = |g|
2
N0, (17)
c = 2N0|g|
2
− N0, (18)
d = N0 + N2
0 / h
2
PS + N0 (19)
It is worth noting that the derivation of SNR is too complicated as below
∂ (SNR)
∂ρ
=
a d − 2dρ + (b − c) ρ2
(d + ρ (c − bρ))
2 (20)
To obtain extreme values, we need to solve the equation ∂γ(ρ)
∂ρ = 0. we can see the sign
of ∂γ(ρ)
∂ρ corresponds with the numerator term, f (ρ) = d − 2dx + (b − c) x2
Hence we just need to solve f (ρ) = 0 . In this proposed model, ρ∗
is found and then it
can achieve the maximum SNR
ρ∗ =
1
2 , ifb − c = 0
d−
√
−bd+cd+d2
b−c , ifb − c = 0
(21)
4. NUMERICAL RESULTS
In this section, the analytical expressions are derived to validate in the previous sections
by deploying Monte Carlo simulation results. All the simulation results are acquired by averaging
over 106
independent experiments. We set the fixed transmission rate R = 3bps/Hz , hence the
outage SNR threshold is given by γ = 2R
− 1 = 7. The energy harvesting efficiency is set to be
η = 0.9. Also, we set average channel gain as λh = λg = 1.5.
We observed that the outage probability achieved by the proposed power splitting policies
as shown in Fig. 2. As can be seen clearly, the average outage probability decreases when the
transmit power increases. It can be confirmed that the proposed power splitting policies attain
better performances than all the other patterns with fixed ρ. That is to say the policy with partial
CSI, which incurs less overhead, approaches the policy with full CSI closely. If the RTS/CTS
mechanism is not available, policy with partial CSI is suggested
In Fig. 3, it can be freely observed that driving more antennas at the PB can significantly
increase the achievable outage probability. This occurrence is quite intuitive, since increasing
the number of antennas can provide higher energy beamforming gain, hence, the amount of the
harvested energy at the source improves, which in turn reduces the outage probability of the
system. This confirms the role of PB in feeding energy to wireless node.
5. CONCLUSION
This paper considered a point-to-point wireless powered communication system, which
may find potential applications in future networks such as medical, sensor, and underwater com-
munications systems. A detailed investigation on the average outage performance of such sys-
tems was presented with optimal power splitting fraction of nergy harvesting protocol. For power
beacon equipped multi antenna for energy transfer transmission modes, the number of antenna
contribute to better the average outage performance as seen in numerical result. In addition, a
solution of the optimal splitting fraction to minimize the average ouatge was examined, and sim-
ulation were obtained, which were shown to be very accurate. Since the optimal split depend on
the instantaneous channel state information, it is a low complexity solution to enhance the system
performance.

IJEECS ISSN: 2502-4752 807
0 5 10 15 20 25 30 35 40
10
−6
10
−5
10
−4
10
−3
10
−2
10
−1
10
0
P
S
(dBm)
P
out
optimal ρ
fixed ρ=0.4
fixed ρ=0.6
Figure 2. Outage probability at destination node with respect to energy harvesting power splitting
fractions ρ
0 5 10 15 20 25 30 35 40
10
−6
10
−5
10
−4
10
−3
10
−2
10
−1
10
0
P
S
(dBm)
P
out
number of antenna = 2
Figure 3. Outage probability destination node with respect to number of antenna at PB
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808 ISSN: 2502-4752
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