Power Consumption Modeling and Analysis of Integrated Optical-Wireless Access Network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 6, December 2017, pp. 3475~3483
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i6.pp3475-3483  3475
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Power Consumption Modeling and Analysis of Integrated
Optical-Wireless Access Network
A. Ramli1
, N. Zulkifli2
, S. M. Idrus3
Lightwave Communication Research Group (LCRG), Fakulti Kejuruteraan Elektrik,
Universiti Teknologi Malaysia, Johor, Malaysia
Article Info ABSTRACT
Article history:
Received Aug 29, 2016
Revised May 28, 2017
Accepted Jun 20, 2017
The integration of optical and wireless technologies at access networks are
considered as a future solution which provide both high bandwidth and high
mobility in an efficient way. GPON is a suitable candidate for optical
backhaul due to the combination of higher data rates, greater split ratio and
support for triple play services hence it offers maximum flexibility and cost
advantages. On the other hand, recent developments of new radio access
technologies and introduction of femtocell base stations provide the potential
of offering broadband services and applications to everyone and everywhere.
However, the power consumption of this network demands a particular
attention because access networks are the largest contributor the network
related electricity consumption. Therefore, in this paper we evaluate the
power consumption of integrated optical-wireless access network which is
based on independent ONU-BS architecture. We proposed a power
consumption model for such network and the assessment has been done
under different simulation scenarios. The constructed model will provide
insight of the energy performance of the integrated access network so that in
the network design process, focus can be done to the most energy saving
strategies.
Keyword:
Optical access
Wireless access
GPON
Femtocell
Power consumption model
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
A. Ramli,
Lightwave Communication Research Group (LCRG),
Fakulti Kejuruteraan Elektrik,
Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia.
Email: arnidza@fke.utm.my
1. INTRODUCTION
Research on passive optical network (PON) continues at a remarkable pace where the future
solution for PON with extended data rates in order to meet broadband consumer demand was recommended
[1],[2]. The planning was divided into two phases; NG-PON1 and NG-PON2. NG-PON1 is expected to
deliver data rates up to 10 Gbps based on the existing Gigabit-capable PON (GPON) legacy whereas NG-
PON2 include the research area of time and wavelength division multiplexed PON (TWDM-PON) which is
capable to provide data rates no less than 40 Gbps. In addition to the high bandwidth demand, increasing
mobility requirements for access networks also present new challenges for service providers. Mobility is
highly desirable for users, because it enables access to the Internet regardless of location. According to
surveys, mobile data traffic has grown 4000-fold over the past 10 years and will be increased nearly eightfold
between 2015 and 2020 [3]. Moreover, as the Internet of Things (IoT) becomes a reality, there will be
massive growth in the number of connected devices which is expected to be around 12 billion devices by
2020.
The integration of optical and wireless networks is a good alternative to overcome both issues to the
complementary features of wide bandwidth and user mobility in optical and wireless networks, respectively.
However, due to the expansion of network connectivity and the increment of data rate of the network, the

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power consumption is expected to increase. The optical access network consumes 60-80% of the total power
consumed by a wired network [4],[5] where the power consumption is dominated by the Optical Network
Units (ONUs). On the other hand, wireless access network consumes 9% of ICT power consumption [6]
where 80% of the power is consumed by the Base Stations (BSs) [7]. Therefore, the study on the energy
consumption of such network will allow energy performance optimization, network architecture
improvement and other network parameters enhancement to be applied. Thus, the power consumption and
energy efficiency of the integrated access network become a desirable objective in the current research
direction.
1.1. Integrated Optical-Wireless Access Network
Optical access networks are being widely deployed nowadays where it can support high capacity
and longer distance communication. PON is the most widely deployed optical access technology. To date,
several PON standards have been proposed in order to improve the performance of PON technologies such as
EPON and 10G-EPON standardized by IEEE and GPON and NG-PON by ITU-T. Table 1 provides summary
of the enabling PON technologies. On the other hand, wireless access networks have evolved from the first
generation (1G) until the fifth generation (5G), improving the transfer speed and including new technologies
to provide service for new packet formats, applications and requirements of users. Currently, we are at the 4G
which refers to all-IP packet-switched networks, mobile ultra-broadband (gigabit speed) access and multi-
carrier transmission. Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access
(WiMAX) is a version of 4G which promise data rates of 100 Mbps. Table 2 summarized the characteristics
of these wireless generations. Apart from access technology evolutions in order to satisfy increasing
bandwidth demand, other trends to emerge are femtocells. Femtocells are small, low power BS typically
design for use in a home or small business. It can significantly boost up wireless network capacity [8] and
studies in [9] has shown that the deployment of femtocells can significantly increase both access data rates
and energy efficiency in areas with high population densities thus making the femtocell as an attractive
solution in order to increase network capacity and coverage in an economical way.
Table 1. Characteristics of different optical access technologies
Data rate (Gbps) Wavelength (nm) Typical Max.
Splitting ratio
Typical Max.
Distance (km)DS US DS US
GPON 2.44 1.24 1490 1310 1:64 20
EPON 1.25 1.25 1490 1310 1:16 20
10G EPON 10
10
10
1
1575 – 1580 1260 – 1280 1:16
1:32
20
NG-PON1 10 2.5 1575 – 1580 1260 – 1280 1:128 20
NG-PON 2 40 40 N/A 1:1024 100
Table 2. Generation in wireless access technologies
Year Introduced Technology Multiple Access System Data Rates Operating frequencies
1G 1980s AMPS, NMT, TACS FDMA 2.4 kbps 800 MHz
2G 1993 GSM, GPRS, EDGE TDMA, CDMA 64 kbps 900 MHz, 1800 MHz
3G 2001 UMTS, HSPA CDMA 3.1 Mbps 2100 MHz
4G 2009 LTE, WiMAX CDMA 100 Mbps 850 MHz, 1800 MHz
5G 2020 (expected) Beyond LTE CDMA 1 -10 Gbps Sub-GHz range
Trends have shown that traffic from wireless and mobile devices will surpass traffic from wired
devices by year 2018 [10]. Moreover, future broadband access networks not only have to provide access to
information anywhere, anytime and in whatever format we need it, but also more importantly have to bridge
the digital divide and offer simplicity and user-friendliness in order to stimulate the design of new
applications and services. To achieve this goal, optical and wireless access networks can be integrated in a
complementary way. Some works have been focused on this integrated network are reported in [11],[12].
According to [12], there are potentially four integrated architectures; namely the independent ONU-BS,
hybrid ONU-BS, unified connection-oriented and microwave over fiber. Independent ONU-BS directly
connects ONU to the BS through standard Ethernet interface. Contrary, hybrid ONU-BS integrates ONU and
BS in both hardware and software. Therefore, modification on the ONU/BS architecture is required. Unified
connection-oriented deploys similar architecture as hybrid ONU-BS. However, the unified architecture can
be run like a WiMAX network with the ability to grant bandwidth finely using WiMAX’s connection-
oriented. On the other hand, microwave over fiber modulate WiMAX signal on a wireless carrier frequency

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and then is multiplexed and modulated together with the baseband optical signal onto a common optical
frequency at the ONU-BS. In this work, we consider the independent ONU-BS architecture because the ONU
and BS acts independently thus no modification on the ONU and BS is required. Consequently, the energy
modeling can be done separately for both ONU and BS providing ease of modeling development.
1.2. Related Work
To date numerous studies have been done in the field of integrated optical wireless technologies in
order to meet the cost challenges and provides ubiquitous broadband connections for the next generation
broadband access network. The integration work has been focused on i) physical layer, ii) MAC layer and iii)
network layer. In [13], the authors proposed hybrid BS scheme based on reflective semiconductor optical
amplifier (RSOA) that enables both wireless and wired services together by making it laser free. However,
the scheme is relatively expensive since the optical carrier is required to be delivered to each hybrid BS.
Thus, they later proposed and experimentally demonstrate hybrid BS architecture that uses self seeding of the
RSOA [14]. As a result, a secondary feeder fiber is not required and therefore reducing the cost of
deployment. In other hand, authors in [15] have proposed a similar hybrid BS architecture as previous but
instead of using RSOA, they used vertical cavity surface emitting laser (VCSEL) as the optical source which
further provides cost effectiveness without any temperature control.
Authors in [16] have presented a dynamic scheduling algorithm namely up-link scheduler with the
purpose of improving upstream bandwidth utilization for WDM EPON. In [17], the authors have proposed a
centralized scheduling mechanism that shortens the packet delay and the time of transmitting data upon
requesting bandwidth. Meanwhile, the work on energy efficiency of the integrated access network can be
found in [9],[18],[19]. In [18], the authors discussed the energy efficient bandwidth allocation mechanism
that adopts an optical burst switching technique for long reach network. Meanwhile, authors in [19] have
reported the sleep mode algorithm for energy efficient FiWi networks. In [9], the authors presented the
energy efficiency analysis for the integrated optical wireless access networks deploying femtocells. To the
best of our knowledge, research on energy efficiency of the integrated access networks generally focus on
Layer 2 and 3 solutions. Unfortunately, the power consumption study on the integration of wireless networks
with the existing fiber based PON remains significantly unexplored. Eventhough analysis of power
consumption for such networks have been evaluated in [10], the power consumption model for the network
elements is derived based only on the values provided in the datasheets. These values are often
oversimplified and do not represents the real consumption. Therefore, in this paper we provide the detailed
modeling of the power consumption of the network elements where we considered the power consumption
dependence on the traffic load and site factors.
2. RESEARCH METHOD
The architecture of the considered access network is shown in Figure 1. The wireless access is
optically backhaul where we considered GPON for optical network and femtocell base station (FBS) for
wireless network. In this integrated architecture, the ONUs and FBSs are connected via common Ethernet
interfaces. Such integrated architecture is known as Independent ONU-BS (IOB). We predicted that in the
future demand for high mobility will be increased since it enables access to the Internet anytime and
anywhere. In addition, surveys have projected that there will be 11.6 billion mobile-connected devices by
2020 [3]. Hence, we assume all ONUs within the network are connected with the FBSs.
In our modeling approach, the total power consumption of this integrated network is the sum of
contribution from OLTs and IOBs connected within the network. We set our system boundaries at network
terminals thus the power consumption of end-user equipment such as personal computers (PCs), laptops and
smartphones is not taken into account. In addition, mobile devices consume just a small fraction of the
energy consumed by optical and wireless access network element since they are optimized for low energy
consumption. Therefore, the total power consumption of the integrated access network is:
𝑃𝐼𝑂𝑊 = ∑ 𝑃𝑖
𝑂𝐿𝑇𝑀
𝑖=1 + ∑ 𝑃𝑖
𝐼𝑂𝐵𝑁
𝑖=1 (1)
in which the power consumption of IOB 𝑖 is expressed as follows:
𝑃𝑖
𝐼𝑂𝐵
= 𝑃𝑖
𝑂𝑁𝑈
+ 𝑃𝑖
𝐹𝐵𝑆
(2)
where 𝑀 and 𝑁 are the number of OLT and IOB respectively, and 𝑃𝑖
𝑂𝐿𝑇
, 𝑃𝑖
𝐼𝑂𝐵
, 𝑃𝑖
𝑂𝑁𝑈
and 𝑃𝑖
𝐹𝐵𝑆
are the power
consumption of OLT, IOB, ONU and FBS respectively. 𝑀 is determined by using this equation:

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𝑀 = ⌈
𝑁
𝑆𝑅
⌉ (3)
where SR is the splitting ratio of the passive splitter.
Figure 1. Integrated optical and wireless access network
2.1. Power consumption model of OLT
The power consumption of OLT is assumed independent of traffic load. Instead, it is influenced
more by the ambient temperature, electronic components and power conversion efficiency which will be
taken into account in our modeling later. In addition, it is challenging to put OLT into sleep mode since it
might cause a synchronization problem for the ONUs [20]. Therefore, we consider the power consumption of
OLT is fixed hence the power consumption of the OLT is modeled based on its functional blocks and the
components that consume power was identified. The generic structure of the OLT is depicted in Figure 2.
Figure 2. Generic structure of OLT
The OLT element usually comprises an uplink port, a switching module and a number of OLT line
cards. Therefore, the power consumption of OLT is the sum of the contributions from OLT PON ports,
general OLT functions such as L2/L3 switching, packet processing and traffic management and uplink ports.
Power conversion efficiency and site factors such as AC/DC rectifiers, ventilation and air conditioning,
auxiliary power units and batteries are also considered in this modeling approach. For the OLT 𝑖, the power
consumption model is expressed as follows:

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𝑃𝑖
𝑂𝐿𝑇
= (𝑃𝑝𝑜𝑟𝑡𝑠 + 𝑃𝑐𝑜𝑛𝑡𝑟𝑜𝑙 + 𝑃 𝑈𝐿) ×
1
𝜂 𝐷𝐶/𝐷𝐶
× 𝑆𝐹 (4)
where 𝑃𝑝𝑜𝑟𝑡𝑠, 𝑃𝑐𝑜𝑛𝑡𝑟𝑜𝑙 and 𝑃𝑈𝐿 are power consumption of OLT PON ports, general OLT function and uplink
ports respectively, 𝜂 𝐷𝐶/𝐷𝐶 is power conversion efficiency and 𝑆𝐹 is site factor.
2.2. Power consumption model of ONU
Figure 3 illustrated the generic structure of the ONU. The network model element for the ONU is
divided into three modules which are represented by different color codes; Optical transceiver, System on
Chip (SoC)/signal processing and Interfaces. The power consumption of the ONU was modeled based on its
dependence on the traffic loads. The power consumption of the ONU was experimentally measured by using
real GPON testbed in which we utilized Arduino-based energy meter for power monitoring and
measurement. Based on the obtained results, we model the power consumption of ONU 𝑖 as:
𝑃𝑖
𝑂𝑁𝑈
= 𝛼 𝑜 𝑟𝑜 + 𝛾𝑜 (5)
where 𝛼 𝑜 is the power consumed by the ONU to transmit or receive 1 bit of information, 𝑟𝑜 is the average
access data rate per ONU and 𝛾𝑜 is the power consumption of ONU when idle.
Figure 3. The generic structre of ONU
2.3. Power consumption model of femtocell base station (FBS)
The FBS’s generic structure can be found in Figure 4. We consider femtocell BSs because it is the
most energy efficient compared to other type of BSs [9]. Similarly to ONU, the BS can be classified into
three basic modules; RF transceiver, DSP/signal processing and interfaces. The power consumption model
for FBS used in this study is based on the work previously reported in [21]. They used similar approach as
our modeling for the ONU which is the power consumption for the FBS is measured by using an Arduino-
based energy meter. For the FBS 𝑖, the power consumption model is as follows:
𝑃𝑖
𝐹𝐵𝑆
= 𝛼(𝑑)𝑟𝑤 + 𝛽(𝑑) + 𝛾 𝑤 (6)
where 𝑟𝑤 is the average data rate of wireless users, 𝛽(𝑑) is the dependence of power consumption on
datagram length and 𝛾 𝑤 is the power consumption when FBS is idle. 𝛼(𝑑) is given by the equation:
𝛼(𝑑) = 𝛼 (1 +
𝛼1
𝑑
) (7)
where 𝛼0 is the factor converting between units of bytes and energy, 𝛼1 is the per datagram overhead and 𝑑 is
the datagram size in Bytes.

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Figure 4. The generic structure of FBS
2.4. Considered scenarios
This section describes the assumptions made and the scenarios considered in this study. Several
deployment scenarios are defined for a fixed network coverage area and different FBS range. The FBS is a
small cell which typically has coverage radius less than 50m. From the value of cell coverage radius or range
𝑅, we can determine the cell coverage area 𝐴 by using equation (8) [22]:
𝐴 =
3
2
√3𝑅2
(8)
In our simulation scenario, we assume coverage area of 100 km2
and vary the FBS range from 10m
to 50m. These parameters were then used to determine the number of IOBs 𝑁:
𝑁 = ⌈
𝑆
𝐴
⌉ (9)
where 𝑆 is the network coverage area and 𝐴 is the cell coverage area. Additionally, we assumed that there is
no more than one active user within a femtocell at a time.
Other parameters considered in the simulation are shown in Table 3. We assume datagram size of
1470 B. The average access data rates for wired users is assume at 10 Mbps based on the typical data rate
subscribe by the residential users. In other hand, the average data rates for wireless users is based on the data
provided in [3] which is 3.1 Mbps for the year of 2016.
Table 3. Parameters considered in the simulation [21],[23],[24]
OLT ONU FBS
Parameter Value/Unit Parameter Value/Unit Parameter Value/Unit
𝑷 𝒑𝒐𝒓𝒕𝒔 41.5 W 𝛾𝑜 11.53 W 𝛼 0.06 W/Mbps
𝑷 𝒄𝒐𝒏𝒕𝒓𝒐𝒍 33.3 W 𝛼 𝑜 0.00237 W/Mbps 𝛼1 70 B
𝑷 𝑼𝑳 2.3 W 𝑟𝑜 10 Mbps 𝑑 1470 B
𝜼 𝑫𝑪/𝑫𝑪 0.9 𝑟𝑤 3.1 Mbps
𝑺𝑭 1.7 𝛽(𝑑) 0.27 W
𝛾 𝑤 7.77 W
We also considered the effect of power consumption performance when the network is connected to
different wireless access technologies. We take into account the wireless technologies that applicable for
small cell application such as UMTS, HSPA, WiMAX and LTE.
3. RESULTS AND ANALYSIS
The simulation results are presented in this section. The power consumption model developed and
described in the previous section is applied to estimate the total power consumed by the integrated optical
wireless access network as shown in Figure 5. The power consumption is plotted versus the coverage area of
100 km2
and the cell range was varied from 10m to 50m which is in the range of typical coverage radius of
femtocell BS.

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Figure 5. Total network’s power consumption with different FBS range
Figure 5 shows the total network power consumption of the integrated optical-wireless access
network with different femtocell BS range versus coverage area from 0 to 100 km2
. The difference on the cell
range has result in the different coverage area of the femtocell BS as refers to Equation 7. Consequently, the
number of IOBs connected to the OLT will also be affected depending on the area that the FBS is able to
cover.
As can be seen in Figure 5, a deployment of wider cell range leads to decreased total network power
consumption. This is due to the fact that lower number of FBSs are needed to cover the considered area.
Since the power consumption is proportional to the number of network elements thus, less number of FBSs
can be achieved by deploying high coverage range resulting in the lowest value of total network power
consumption. It can also be found that the difference in the power consumption from the highest
consumption to the lowest consumption is also decreased in which for coverage area of 100 km2
, the highest
difference is 75% and reduced to around 36% as the range increases. Thus, it is better to utilize the optimum
range which can provide better coverage with low power consumption.
In our simulation, we also considered the implementation of different wireless access technologies
to the GPON backhaul of the integrated access network. We evaluated its effect to the power consumption
performance of such network. The simulation result is presented in Figure 6 in which we applied several
wireless access technologies that represent the different wireless generation. Since the developed power
consumption of the integrated network is suitable for small cell application, therefore we only consider the
technologies from 3G to 4G which is applicable to the femtocell BS. From the previous results, it can be
seen that the FBS range of 50 m consumed the least power. However, the wider range of femtocell BS might
reduces the signal strength [25],[26]. Therefore, in this simulation we take into account the femtocell range of
30 m which offers acceptable amount of power consumption with better network coverage.
Figure 6. Total network power consumption with different wireless access technologies
0 10 20 30 40 50 60 70 80 90 100
0
1
2
3
4
5
6
7
8
9
10
Coverage Area (km2)
Totalnetworkenergyconsumption(MW)
R=10m
R=20m
R=30m
R=40m
R=50m
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Coverage Area (km2)
Totalnetworkenergyconsumption(MW)
UMTS (3G)
HSPA (3.5G)
WiMAX (4G)
LTE (4G)

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Figure 6 depicted the total network power consumption of integrated GPON with different wireless
technologies i.e UMTS, HSPA, WiMAX and LTE versus coverage area of 0 to 100 km2
. Note that all
parameters are common for all four wireless technologies. However, the parameter 𝑟𝑤differs where it is set at
2 Mbps, 14.4 Mbps, 75 Mbps and 100 Mbps for UMTS, HSPA, WiMAX and LTE respectively. The
differences are due to the fact of the difference peak data rate offer by these technologies in accordance to
which wireless generation they are in.
By referring to Figure 6, it was found that higher wireless generation connected to the GPON
backhaul resulting in the higher power consumption of the network. The wireless technologies have evolved
from 1G to 4G and in the future to 5G in order to satisfy user demand for high bandwidth and high speed
services. Thus, the data rates offer by these technologies increased in accordance to the higher wireless
generation. As a result, the power consumption of such technologies connected to the network is increased.
Unfortunately, the power consumption is expected to further increase due to the growing demand for
broadband services in the future. As a result, the implementation of power saving technique to the broadband
access network is significantly essential towards greener network environment.
4. CONCLUSION
In conclusion, we presented a novel model for evaluating the power consumption of the integrated
optical wireless access network which is based on independent ONU-BS architecture. Typically, the network
elements in optical access network are assumed always on and their consumption is both traffic and time
independent. In our model, we take into account the dependence of both ONU and BS to the average traffic.
We demonstrate the use of this model to predict the power consumption of the integrated access network
particularly for small cell application. It was found that for wider BS range, the power consumption is
significantly lower. Additionally, the difference in power consumption becomes smaller where the smallest
difference shows a percentage at approximately 36%. Meanwhile, for the higher wireless generation attached
to the GPON backhaul, the higher the power consumption is observed. Although the power consumption is
increased, it is worth noted that the data rates offer by the latest technologies is also greater. In other words,
these technologies might have potential to offer more energy efficient network. Thus, the future direction of
this work will focus on the assessment of energy efficiency and the potential of energy saving technique to
this integrated access network.
ACKNOWLEDGEMENTS
This work is supported by Ministry of Education and the administration of Universiti Teknologi
Malaysia (UTM) for the project financial support through FRGS Fund vote number 4F407. The authors
acknowledge the Universiti Teknologi Malaysia for the financial support through SLAI funding to the first
author.
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BIOGRAPHIES OF AUTHORS
Arnidza Ramli received both the Bachelor degree in Electrical Telecommunication and Master
degree in Electrical from the Universiti Teknologi Malaysia in 2007 and 2009 respectively. She
is currently a PhD candidate at the same university. Her current research activity is mainly
focused on energy efficiency for the integrated optical and wireless network. Other research
interests include power consumption measurement, performance evaluation and green
networking.
Nadiatulhuda Zulkifli is a Senior Lecturer in the Department of Communication Engineering,
Faculty of Electrical Engineering, Universiti Teknologi Malaysia. She obtained a Bachelor
degree in Electrical Telecommunication from UTM and received her masters and PhD degrees
from University of Essex, UK. She has authored a number of international journal and
conference papers. Her research interests cover optical communication and networking, mobile
commerce and innovation adoption.
Sevia Mahdaliza Idrus is the Deputy Director Innovation of the UTM Innovation and
Commercilisation Centre and faculty member of the Faculty of Electrical Engineering and
Innovative Engineering Research Alliance, UTM. She received her Ph.D in 2004 from the
University of Warwick, United Kingdom in optical communication engineering. She has served
UTM since 1998 as an academic and administrative staff. Her main research interests are optical
communication system and network, optoelectronic design, and engineering management. Her
research output have been translated into a number of publications and IPR including a high-end
reference books, ‘Optical Wireless Communication: IR Connectivity’ published by Taylor and
Francis, 49 book chapters and monographs, over 150 technical papers, 2 patents granted, 35
patent filings and holds 28 UTM copyrights.

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