Capacity Improvement in 5G Networks Using Femtocell

Abstract

Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in the dense femtocell. The significant increase in the number of internet users and the demand for higher data capacity per user mandates the development of complex and expensive telecommunication infrastructure that includes spectrum and power management, increased operation power and high-quality. In this work, we investigate the performance of femtocells in outdoor and indoor space based on the network’s capacity and resource management by extensive numerical modeling and simulations for different conditions of user’s location and femtocells in one cell.

Full text

Vol.:(0123456789)
Wireless Personal Communications
https://doi.org/10.1007/s11277-019-06134-2
1 3
Capacity Improvement in5G Networks Using Femtocell
MohammadGhanbarisabagh1 · GobiVetharatnam2· EliasGiacoumidis3·
SoheilMomeniMalayer4
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Allocation of resources between femtocell and macrocell is essential to counter the effects
of interference in the dense femtocell. The significant increase in the number of internet
users and the demand for higher data capacity per user mandates the development of com-
plex and expensive telecommunication infrastructure that includes spectrum and power
management, increased operation power and high-quality. In this work, we investigate the
performance of femtocells in outdoor and indoor space based on the network’s capacity
and resource management by extensive numerical modeling and simulations for different
conditions of user’s location and femtocells in one cell.
Keywords Femtocell· 5G· Power control· IoT· Telecommunications market·
Interference
1 Introduction
Presently, the proposed technologies for 5G and the internet-of-things (IoT) for increas-
ing and optimizing spectrum resources and cell’s density includes massive multiple-
input multiple-output (MIMO), spatial modulation [1], native support for M2M for
a massive number of connected devices with low throughput and for low power and
latency, device centric architecture [2], and visible light communication [3]. They are
being extended to be applied for the increase and the growth in cell’s intensity. With
remarkable surge in smartification and the internet and cloud cache, new modulation
scheme have been noticed significantly. On the other hand, this increasing require-
ment needs more development in software, hardware and base station. As the proposed
* Mohammad Ghanbarisabagh
m.ghanbarisabagh@iau-tnb.ac.ir; m.ghanbarisabagh@gmail.com
1 Department ofElectrical Engineering, Faculty ofElectrical Engineering andComputer Sciences,
Islamic Azad University North Tehran Branch, Tehran, Iran
2 Department ofElectrical andElectronic Engineering, Lee Kong Chian Faculty ofEngineering
andScience, University Tunku Abdul Rahman, KualaLumpur, Malaysia
3 The Rince Institute, School ofElectronic Engineering, Dublin City University, Glasnevin 9,
Dublin, Ireland
4 International Business School, Zahony Utca 7, Budapest1031, Hungary

M.Ghanbarisabagh et al.
1 3
technology has the highest compatibility with current technologies it can decrease costs
leading to higher speed, better quality, and delay elimination.
Using higher frequencies to decrease the waves is essential to decrease the penetra-
tion distance between the user and the base station resulting in power resource man-
agement and signal quality optimization which has further flexibility in network archi-
tecture to decrease expenses [4]. This causes a better presentation of support in more
dense parts of the network through the application of small cells backhaul problems
[5] could be less beside the increase in system’s security [6] capability and the net-
work capacity. Nowadays, femtocells are noticed significantly in cellular indoor network
due to their easy setup and maintenance, less expenses and better coverage compared to
other existing approaches such as repeater and das [7]. They also have resources man-
agement capabilities, security and less pack loss in comparison with Wi-Fi. But is there
any expectation of development for their application in all areas in the 5G?
The most critical problem of femtocells is their interference management. If interfer-
ence could be decreased through proper approaches, femtocells could be used instead
of increasing the base stations power, which comes with its associated setup expenses
and high maintenance. For cross tier interference management there are methods such
as Femto-aware spectrum management, clustering of femtocells [8], cognitive [9], beam
subset selection strategy [10], fractional frequency reuse [11], collaborative frequency
scheduling [12] and power control. The last two methods have the least complexity and
the highest efficiency. Power control approach can be combined with beamforming to
avoid power wastage in space or alternately we could increase the beam power in the
desired direction.
With the large increase of population in the cities, one of the challenges being faced is
the inability of microcells to provide coverage in populated areas. In addition to that, the
proliferation of smart devices with 5G and coming of IOT in wireless cellular technologies
compared to LPWAN, further aggravates this challenge. Consequently, the network’s archi-
tecture must be optimized to decrease expenses and increase the speed and smartification
[13]. For instance, in a smart city, one of the most important issues is smart traffic manage-
ment. Here, cellular tracking [14] of vehicles can be done at a lower cost instead of costly
GPS devices and GNSS satellites. Through this better traffic regulations, security, more job
opportunities and telecommunication network optimization would be achieved.
The motivation of this work is to decrease the cost by managing the interference
between femtocells and macrocells properly, and increasing the usage of indoor and out-
door femtocells.
Mobile Mega-Operators are promoting femtocells very seriously in order to decrease
tariffs, increase QOS and not to lose future consumption market. Through proper interfer-
ence management, femtocells can have high influence. Moreover, using indoor and outdoor
femtocells causes cost elimination and macrocells restriction.
Femtocells are imagined in indoor space in various studies and both path lost [15, 16],
and interference scenarios have been studied for indoor and outdoor users. However, out-
door femtocells are not noticed enough due to their less usage. In future tele-communica-
tion generation, outdoor femtocells must be used according to appropriate distance with
MBSs specially, beamformed outdoor femtocells. For instance, Swisscom and Ericsson
companies have used city manholes for outdoor femtocells. The way to increase the net-
work’s capacity with much lower costs will be more economical. Therefore, the main con-
tribution of this work is to improve the simulation results by special attention to beam-
formed outdoor femtocells.
The organization of the paper is as below:

Capacity Improvement in5G Networks Using Femtocell
1 3
In Sect.2 the system analysis and modeling has been discussed. This section determines
user inputs, system parameters and calculating path-loss based on population, urban den-
sity and telecommunication standards. Next section shows the simulation results. This sec-
tion mean CQI Based on SINR, demand based on the four important users, the amount of
added capacity coefficient to distance ratio without specifying the amount of bandwidth
allocated, spectrum efficiency in terms of distance ratio, and comparing MBS and Femto-
cells using a distance of more than 175m. Finally, in the last section the conclusion of the
work together with the future work has been discussed.
2 System Analysis andModeling
In this standard, outdoor wall loss is 20dB and indoor loss is 5dB. This method is more
appropriate for modeling.
(a) Fig.1 shows the coverage provided by mobile base stations (MBS) in Tehran. Based
on current MBS locations, significant parts of Tehran do not have adequate coverage.
Femtocells within this area can assist to improve the coverage of Tehran. Urban density
indicator can be used to approximate the building densities and indirectly the locations
of femtocells. With the consideration of beam forming for outdoor femtocells, the
number of users can be estimated and based on related variants, to base stations and
spectrum features modeling.
Using signal-to-noise-interference ratio variant, the network capacity and rate of
information transfer can be studied. Firstly, the path-loss information as calculated by
Fig. 1 MBS coverage

M.Ghanbarisabagh et al.
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different method is assessed. The best method is to use the model based on empirical
measurements and transmitter’s features, such as Hata & Okumura model [17], or using
pre-existing standards.
In this method waste gets estimated more comprehensively according to macro feed-
backs. The recommended amount is listed in the following for macrocell outdoor user,
femtocell outdoor user, macrocell indoor user and femtocell indoor user, respectively.
(b) The following formula indicates the calculation of SINR for MBS user in subcarrier
k, in which
PM,k
and
Gm,M,k
are the M MBS power and the antenna gain respectively
and M′ is the neighboring MBS power. N0 is the noise power spectrum density per
subcarrier spacing.
Furthermore, the interference of the femtocells is included.
In order to simplify the calculations we can consider this formula based on one exist-
ing main MBS and ignore the interference of other MBSs. Therefore, considering
the number of MBSs in city space B and calculating their advantage through beam
formed antenna sites and dividing users into categories of outdoor and indoor, formu-
las5 and 6 can be rewritten as following. They indicate SINR for MBS indoor users,
femtocell indoor users, MBS outdoor users and femtocell outdoor users respectively.
From these, the consequences of simulation can be studied in terms of accuracy.
(1)
PLmo(dB)=15.3 +37.6LOG R
(2)
PLfo(dB)=15.3 +37.6LOG R
(3)
PLmi(dB)=15.3 +37.6LOG R +LOW
(4)
PLfi(dB)=15.3 +37.6LOG R +Liw
(5)
SINR
m,k =
P
M,k
G
m,M,k
N
0
Δf+
∑M
P
F,k
G
m,F,k
+
∑M
�P
M
�
,k
G
m,M
�
,k
(6)
SINR
f,k =
P
F,k
G
f,F,k
N
0
Δf+
∑M
P
M,k
G
f,M,k
+
∑F
�P
F
�
,k
G
f,F
�
,k
(7)
SINR
mh,k =
P
M,k
G
mh,M,k
N
0
Δf+
∑F
�P
F,k
G
fh,F,k
(8)
SINR
fh,k =
P
F,k
G
fh,F,k
N
0
Δf+P
M,k
G
mh,M,k
(9)
SINR
mc,k =
P
M,k
G
mc,M,k
N
0
Δf+
∑B
P
Fc,k
G
fc,B,k
(10)
SINR
fc,k =
P
F,k
G
fc,F,k
N
0
Δf+P
M,k
G
mc,M,k
+
∑B
P
Fc,k
G
fC,B,k
(11)
G=10−
PL
∕10

Capacity Improvement in5G Networks Using Femtocell
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Specific parameters are required in order to perform the modelling, including the
number of points available, users and the ratio of indoor femtocells to urban cells
among others. Therefore, using the standard of [18] the cell features are identified
according to the Table1.
Moreover, calculated SINR can be used in the SHANNON formula to determine the
network capacity.
(c) For control of power, the system is defined based on population, urban density and
telecommunicate standards. Next, an estimate is made and the network is designed
according to the possibility of user’s presence in each areas. These values may change
floatingly. The system architecture is shown in Fig.2.
It is important to consider that 65% of this load is on indoor section based on pre-
sented statistics. Furthermore, 35% of urban users and 65% of indoor users are not satis-
fied with the presented services. Femtocells are required to move towards this section’s
supply. Moreover, this can be compared to user’s need service’s quality and be included
in the calculations related to power. This results in 30, 23, 35 and 12% for indoor MBS
user, indoor femtocell user, outdoor MBS user and outdoor femtocell user, respectively.
Matlab was used for modeling [19] and it has been optimized for the current usage as
shown in Fig.3.
For the purpose of simulation in urban space, Tehran is considered as an example.
Since Tehran’s building’s density is R122, it could be said that a piece of infrastructure
land of 250 square would be considered on the one hand. In this model density has been
simulated according to the number of existing house under MBS coverage. The gather-
ing of indoor femtocells is placed mostly further than MBSs in the simulation. Since,
(12)
Cm,k
=Δf.log
2(
1+∝SINR
m,k)
Table 1 System parameters System parameter Value/range
Macrocell radius 500m
Macrocell transmission power 46dBm
Carrier frequency 2GHz
Bandwidth 20MHz
White noise power density − 174dBm
Number of indoor femtocells 24
Number of outdoor femtocells 6
Number of macrocell active user 90
Number of femtocell active user 90
Fig. 2 System architecture User
inputs
calculate
path-loss
calculate
SINR
SINR-to-CQI
Mapping
OUTPUTS
& RESULTS

M.Ghanbarisabagh et al.
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the proportion of a street’s area to houses is 1– 5, the number of urban femtocells has
been allocated based on this proportion.
Based on beamforming in the length of street, a wide range of urban users are allocated
to femtocells. The number of users is based on active 1–5. However, the user’s proportion
will be increased very soon in Tehran.
In the identified section of Fig.4 the features related to user number 67 who is an out-
door one are calculated.
3 Simulation Results
From the simulation, SINR and the distance relevant to users were recorded. The data was
gathered and then for each group, CQI was extracted according to Fig.5; [20] and Table2.
Based on this, the average was achieved and eventually the Fig.6 was drawn.
Based on weight importance for each group and the percentage of estimated accidental
users according to area integral between 50m from MBS in Table3, Fig.7 is drawn.
On the distance, the amount of added capacity coefficient to the network by femtocells,
and using Eq.12 has been calculated cumulatively as shown in Fig.8.
Fig. 3 Simulated cell

Capacity Improvement in5G Networks Using Femtocell
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Fig. 4 Marking user number 67
Fig. 5 SINR-to-CQI mapping

M.Ghanbarisabagh et al.
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This coefficient has been achieved without specifying the amount of bandwidth allo-
cated and it is observed that the efficiency of indoor femtocell in increasing network capac-
ity of 6 is more than urban femtocell. Spectrum efficiency based on CQI, shown is Table4,
and distance of urban femtocell’s users used by the main base station has been illustrated
in Fig.9. It shows that in the distance of 135m, femtocell has higher spectrum efficiency.
As shown in Fig.10, half of the users keep a distance of more than 175m from MBS.
Through this, the CQI for outdoor femtocells is more than the CQI for the MBS. In such
a distance, outdoor femtocells have almost equal capacity with indoor femtocells in fewer
radiuses. Therefore, outdoor femtocells can increase the network capacity effectively.
Table 2 SINR-to-CQI mapping
SINR − 6.7 − 4.7 − 2.3 0.2 2.4 4.3 5.9 8.1 10.3 11.7 14.1 16.3
CQI index 1 2 3 4 5 6 7 8 9 10 11 12
Fig. 6 Mean CQI based on SINR
99
7
5
3
2
999
8
6
3
999
4
6
777
0
2
4
6
8
10
050 100 150 200 250
MEAN CQI
DISTANSE FROM MBS
MACRO UE IN MACRO UE OUT
FMTO UE IN FEMTO UE OUT
Table 3 Percentage of estimated
accidental users Distance from MBS 0 50 100 150 200 250
Percentage of population 0 4 12 20 28 36
Fig. 7 Demand based on the four
important users
0
1
2
3
4
0416 36 64 100
DEMAND
POPULATION
MACRO UE IN MACRO UE OUT
FMTO UE IN FEMTO UE OUT

Capacity Improvement in5G Networks Using Femtocell
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However, this distance can be changed by changing MBS transmission power. If the num-
ber of outdoor femtocells and indoor femtocells are 5 and 21, respectively, by consider-
ing the cost of each outdoor femtocells, each indoor femtocell and each MBS as 1.4k$,
Fig. 8 The amount of added
capacity coefficient to distance
ratio without specifying the
amount of bandwidth allocated
0
100
200
300
400
500
600
050 100 150 200 250
ADDED CAPACITY COEFFICIENT
DISTANCE FROM MBS
INDOOR OUTDOOR
Table 4 CQI to efficiency
mapping CQI index Modulation Code rate × 1024 Efficiency
0 Out of range
1 QPSK 78 0.1523
2 QPSK 193 0.3770
3 QPSK 308 0.6016
4 QPSK 449 0.8770
5 QPSK 602 1.1758
6 16QAM 490 1.9141
7 16QAM 616 2.4063
8 64QAM 466 2.7305
9 64QAM 567 3.3223
10 64QAM 772 4.5234
11 64QAM 873 5.1152
12 64QAM 948 5.5547
13 256QAM 772 6.0312
14 256QAM 948 7.3984
IS 256QAM 975 7.6171
Fig. 9 Spectrum efficiency in
terms of distance ratio
0
0.5
1
1.5
2
2.5
3
50 100 150200 250
SPECTRUM EFFICIENCY
DISTANCE FROM MBS
MACRO FEMTO

M.Ghanbarisabagh et al.
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1.19k$ and 30 k$, respectively [21], the total cost and capacity coefficient can be perse-
cuted in Fig.10.
Figure 10 shows by using the distance of more than 175m, outdoor femtocells can
achieve higher capacity with lower cost.
4 Conclusion andFuture work
Although femtocells are suitable for both indoor and outdoor applications, the demand for
outdoor femtocells is less than half of indoor femtocells. By using outdoor femtocells of
more than half of the MBS radius, the performance shows very good spectruonm and sig-
nal quality. Using the CQI coefficient, we can manage and release MBSs while increasing
network capacity so that more users could be served with higher bandwidth or used for
cellular tracking for each vehicle in a smart city. Outdoor Femtocells are appropriate and
efficient solution for accurate and extensive cellular vehicle tracking which can create great
potential and revenue. Decreasing the costs of femtocell’s production would be a good
challenge in the near future as there is a strong need to use the femtocells in the network.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Mohammad Ghanbarisabagh was born in Tehran, Iran. He obtained
his B.Eng degree in Electronics Engineering in 1990. He received his
M.Eng.Sc degree in Wireless Communications Engineering from Iran
University of Science and Technology in 1999. He then received his
Ph.D. degree in Optical Communications Engineering from Multime-
dia University, Malaysia in 2011. He was a Senior Lecturer in the
Department of Electrical Engineering, Faculty of Engineering, Univer-
sity of Malaya in Malaysia from April 2012 till September 2013. Since
October 2013, he joined as an Assistant Professor to the Department of
Electrical Engineering, Faculty of Electrical Engineering and Com-
puter Sciences, Islamic Azad University North Tehran Branch, in Teh-
ran, Iran. Dr. Ghanbarisabagh is a CEng as well as a member of IET.
He is a member of editorial board for some journals and is a reviewer
for more than 15 ISI Q1 journals like IEEE Transactions on Circuits
and Systems I: Regular Papers, IEEE Journal of Lightwave

M.Ghanbarisabagh et al.
1 3
Technology, IEEE Photonics Journal, IEEE Sensors Journal, OSA/Optics Express, OSA/Optics Letters,
OSA/Applied Optics, OSA/ Journal of Optical Communications and Networking, Elsevier/Optical Fiber
Technology, Taylor & Francis/Fiber and Integrated Optics, and …;. His research interests include the field
of wireless communications especially in OFDM and optical communications especially in Optical OFDM.
Gobi Vetharatnam was born in Malaysia. He obtained his B.E. degree
in Electrical Engineering in 1998 from University of Malaya. He
received his M.Eng.Sc and Ph.D in Engineering from Multimedia Uni-
versity in 2003 and 2011, respectively. He is currently an Associate
Professor in Lee Kong Chian Faculty of Engineering and Science, Uni-
versiti Tunku Abdul Rahman, in Sungai Long, Malaysia. He is a PEng
as well as a senior member of IEEE. His research interests include the
field of wireless communications, microwave remote sensing and radar
system, where he focus on the antenna system for these applications.
Elias Giacoumidis is a Marie-Curie Fellow at Dublin City University
& SFI CONNECT Research Centre of Ireland. His project tackles the
“capacity crunch” in optical fiber communications (EPIC: Energy-effi-
cient & Phase-Insensitive Coherent Communications). He has also
recently joined Xilinx-Ireland as a visiting-scholar for the realtime
implementation of machine learning based fiber nonlinearity compen-
sation. He has previously worked for various prestigious optical com-
munications research groups: Heriot-Watt University (cybersecurity
group), University of Sydney CUDOS (deputy photonics project
leader), Aston University, Bangor University (PhD scholarship), Ath-
ens Information Technology, and Telecom-ParisTech, where he was
also teaching optical system modelling. His research involves balanced
theoretical and experimental exploration in high-capacity optical trans-
mission systems with specialization in key modern digital signal pro-
cessing modulation techniques (OFDM, CAP, PAM-4 etc.) and nonlin-
ear photonics (e.g. Brillouin filtering and amplification) for
next-generation local, access networks and flexible long-haul optical
communications. Dr Giacoumidis is the principal investigator of the world’s-first direct-detected optical
Fast-OFDM transmission system. He was the first to implement digital-based machine learning in optical
communications (coherent optical OFDM) for fiber nonlinearity compensation. Dr Giacoumidis was nomi-
nated an outstanding reviewer of 2016 for IEEE Journal of Lightwave Technology.
Soheil Momeni Malayer obtained his B.Eng degree in Electrical Engi-
neering from University of Science and Culture, Iran in 2015. He then
received his M.Eng.Sc degree in Wireless Communications Engineer-
ing under Dr. Mohammad Ghanbarisabagh supervision from Islamic
Azad University North Tehran Branch, Iran in 2018. He is currently
studying German language at International Business School, Hungary.
His research interests include radio resource management and interfer-
ence management in mobile communications networks.