Proposed Multi-Stage PSO Scheme for LTE Network Planning ... · PDF fileThe target area for LTE network planning is a part of Al-Resafa region in Baghdad city which has an area of

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 20 (2016) pp. 10199-10210

© Research India Publications. http://www.ripublication.com

10199

Proposed Multi-Stage PSO Scheme for LTE Network Planning and

Operation

Aied K. AL-Samarrie1, Hayam Alyasiri2 and Aseel H. AL-Nakkash3

1University of Technology, Dept. of Elec. Eng. Baghdad-Iraq.

2Minstry of Communication, Baghdad-Iraq.

3Collage of Electrical and Electronic Techniques,

Department of Computer Eng. Tech., Iraq.

Abstract

An e-government network based on LTE is designed for a

target area in Baghdad city center, in interference aware

framework. The network design is taken place in two phases.

In the first phase, a homogenous LTE network is planned

consisting of eight macro cells. These macro cells are being

selected in an optimal manner based on Binary Particle

Swarm Optimization (BPSO) algorithm to get minimum

overlap and acceptable covered users. In the second phase, a

new Multi-Stage Particle Swarm Optimization (MS-PSO)

scheme is proposed for implementing Heterogeneous Network

(HetNet). MS-PSO is composed of two interactive loops; the

outer loop is used for controlling the deployment of small

cells by activating or deactivating pico cells. The inner loop is

used for optimizing the active pico's power in such a way that

the pico's power will be only increased if the Down Link (DL)

data rate is increased. Controlling the HetNet operation using

the proposed MS-PSO is done adaptively with the Traffic

Load (TL) variation during the business day. Many analyses

are being done to evaluate the proposed scheme for enhancing

the network performance in terms of interfered area, served

users and Energy Efficiency (EE). The proposed MS-PSO

enhances the network EE by 79%, 78% and 42% for heavy,

moderate and low TL respectively comparing to homogenous

network, while the optimized HetNet gives an enhancement of

68%, 66%, and 56% for the same TL profiles in comparison

with the non-optimized HetNet.

Keywords: HetNet, PSO, Energy Efficiency, LTE, E-

government.

INTRODUCTION

The last decade witnesses a dramatic increasing of mobile

Users' Equipment (UEs), which in turns produces a persistent

need for reuse the limited resources frequently. This problem

attracts potential efforts of research communities and

standardization bodies. These efforts resulted in adopting the

LTE-A a new network design which is known as HetNet. The

HetNet is introduced to solve the problem of coverage holes,

providing flexible broadband services to the UEs anywhere in

the network and this will increase the spectral efficiency per

unit area. HetNet is a number of small cells with low power,

low cost and easy deployment overlaid the Macro cell e.g.

Pico cell, relay nodes, femto cell….etc [1].

HetNet is deployed with cochannel scenario due to the limited

spectrum, which produces an interference problem between

these small cells. Furthermore LTE Rel-8/9 allowing increase

the off-loading from macro cells by small Cells Range

Expansion (CRE), which may worsen cell-edge capacity [1].

Accordingly, many interference management techniques were

proposed to enhance the interference coordination and to

increase the spectral efficiency during the HetNet operation.

In LTE Rel-10, the enhanced Inter Cell Interference

Coordination (eICIC) was done through the resources

coordination in time domain, where blanked subframes,

referred to as Almost Blank Subframe (ABS) were dedicated.

This is done in order to serve the small cells' UEs without any

data transmission from the macro cell except that for some

necessary control signals [2]. The operation of HetNet during

the ABS allows small cells' range extension in order to receive

data (at the cell edge) with better conditions, but at the cost of

scarifying the macro's resources. To compensate for the

macro's performance degradation during the ABS, LTE-

Advance in Rel-11 introduces Further eICIC (FeICIC), which

has proposed to reduce the power of the Macro cell during the

ABS rather than muting it, to enhance the performance of the

Macro cell [3].

The FeICIC neither specify a limitation for sharing resources

(time, power and frequency) between macro and small cells,

nor determines the small cells' extension radius. In addition,

the size and the number of small cells being deployed

represent a great challenge against many factors such as

interference and cost. Accordingly planning and managing of

HetNet open a wide area for the researches to candidate how

to design and organize HetNet benefiting from the LTE-

Advance characteristics in optimal way. For example [4]

shows that the effectiveness of HetNet in improving the

network throughput for outdoor and indoor scenarios; and [5]

enhances the energy efficiency (throughput per power unit)

according to the percentage of resource block being used.

Also, optimizing the activation or deactivation of Base

Stations (BSs) was implemented in [6] based on greedy

algorithm for mapping them to different subframes. Moreover,



10200

a modified PSO algorithm is used to maximize the throughput

by examining the power of each small cell [7]. However, [8]

increases the throughput by adaptively changing the ABS

pattern between zero and low power; while [9] determines an

optimal small cell switching pattern to increase energy

efficiency.

This work introduce a new proposed MS-PSO for HetNet

planning and operation. The MS-PSO is implemented in

hierarchical steps within two phases. In phase one, LTE based

e-governmental network has been planned optimally for an

area at the center of Baghdad city, based on BPSO. In phase

two, the HetNet a is implemented by deploying pico cells

overlaid the macro cells wherever the need exists for solving

the coverage hole in terms of capacity. The operation of pico

cells is optimized in order to increase the EE based on a new

proposed optimization scheme. It is composed of PSO and

BPSO to optimize the number of activated pico cells with

their optimal power at each TL profile.

This paper is organized as follows: section 2 presents the e-

government network model and the UEs distribution; also the

proposed scheme procedure is explained. Section 3 introduces

simulation and analyses including discussions. Finally, section

4 illustrates the final conclusions.

PROPOSED MULTI STAGE PSO (MS-PSO) SCHEME

Network Model :

The target area for LTE network planning is a part of Al-

Resafa region in Baghdad city which has an area of 587.25

Km2. The UEs of the LTE Network represent 228

governmental buildings. A set of candidate locations

(represent the existing terminals of the national optical fiber

network) is being chosen for network macro cells installation.

Determining these locations and the network simulation is

done by one of the most powerful network planning tools,

which is the ICS Telecom software. With the aid of this

software the designer can define thresholds from Link budget,

checking network capacity against more detailed traffic

estimates, many test can be done with different network

configuration, BS parameter planning…etc. ICS telecom

allows network engineers to plan their networks, considering

the geographical area to be covered, the characteristics of the

transmitter/ receivers and the installation constraints. Then the

designer can state the network requirements and work in order

to achieve optimal performance with minimum deployment

cost [10].

Particle Swarm Optimization (PSO) :

PSO is a population-based algorithm, where the population is

called the "swarm" and its individuals are called the

"particles". Each particle represents a candidate solution in the

search space. Initially particles are randomly placed, and then

they are assumed to move within the search space iteratively

by adjusting their position using a proper position shift, called

"velocity". Each particle velocity is updated based on

information obtained from the previous steps of the algorithm.

This information include; each particle best position it has

ever visited during its search and best position ever visited by

all particles [11, 12].

The PSO algorithm has been used successfully and widely

employed in many applications such as: wireless networks

supporting both planning and operation phases for optimum

performance, speech and speaker recognition to determine the

optimal subset of features, power system optimization….etc.

[13].

The basic PSO deals with continues values represent the

possible solution for continues problem. Other problems'

solution may be discrete (integer, binary) in nature.

Accordingly many variants of the PSO algorithm are derived

from the basic one so it can process different problem entities

[14]. BPSO is a modified version from the original PSO,

where the new position of each particle is modified to {0,1}

only. This done by mapping the particle's velocity to binary

digit based on sigmoid function. BPSO is adopted in this work

to control the status of pico cell.

In this work the proposed MS-PSO is employed to meet the

UEs' requirements due to different daily work hours' TL in

interference aware manner. This is materialized by

minimizing the overlap and the power consumption during

both the planning and the operation phases.

LTE Network Planning (Phase One) :

The objective of this phase is to select m sites from a set of M

candidate sites to install the LTE network macro cells' tower.

BPSO approach is being used to determine the best sites in

terms of minimum overlap with a satisfying percentage of

covered UEs. At each BPSO iteration two parameters are

determined; the number of covered UEs and the overlap

percentage. First the serving BS must be identified, which is

the station that delivers the maximum power to the served UE

as in eqn. (1) under the condition given in eqn. (1.a).

𝑆𝑒𝑟𝑣𝑖𝑛𝑔 𝐵𝑆 (𝑗) = max(𝑃𝑟𝑘,𝑗𝑈𝐸) …………………… (1)

Subjected to: 𝑃𝑟𝑘,𝑗𝑈𝐸 ≥ 𝑃𝑚𝑖𝑛

𝑈𝐸 ……… ……… (1.a)

Where 𝑃𝑟𝑘,𝑗𝑈𝐸 is the power received from jth BS to the kth UE

subjected that the maximum power to the served UE must be

certainly exceeding or equals the minimum acceptable UE

received power donated by(𝑃𝑚𝑖𝑛𝑈𝐸 ).

The second parameter is the overlap percentage between sites.

The overlap can be characterized by ING (mxm) matrix, where

ING (j,j') = percentage of interfered area caused by j'th BS to

jth BS and j≠j'. This matrix can be extracted from the

simulation results implemented by ICs Telecom software. The

cost function is represented by the summation of all interfered

area as in eqn. (2).

𝐶𝑜𝑠𝑡 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 = 𝑎𝑟𝑔𝑚𝑖𝑛 ∑ ∑ 𝐼𝑁𝐺(𝑗, 𝑗′)𝑚𝑗′

𝑚𝑗 . 𝑥𝑗 . 𝑥𝑗′… (2)

Subjected to: C1: 𝑁𝑐𝑜𝑣𝑒𝑟𝑒𝑑

𝑈𝐸 ≥ 𝑇𝐻𝑁𝑈𝐸 ………. ….… (2.a)

C2: 𝑥𝑗 𝑜𝑟 𝑥𝑗′ 𝜖 {0,1} ………………….………..…… (2b)



10201

Where 𝑥𝑗 = { 0 𝑖𝑓 𝐵𝑆𝑗 𝑖𝑠 𝑑𝑒𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑒𝑑

1 𝑒𝑙𝑠𝑒

C1 is the 1st constraint which ensure that the number of

covered UEs ( 𝑁𝑐𝑜𝑣𝑒𝑟𝑒𝑑𝑈𝐸 ) is more than a predefined threshold

(𝑇𝐻𝑁𝑈𝐸).

C2 is the 2nd constraint which ensures that overlap will be

only calculated for active stations.

Through the BPSO iterations a set of macros' BS are activated

and deactivated till the dominate interferer site is cancelled

and minimum cost function is reached. After determination of

sufficient sites to be deployed under the coverage conditions,

the network performance is analyzed in terms of capacity and

served UEs to indicate which site is congested to be handled

in the next phase.

HetNet planning and operation (Phase Two) :

In order to solve the problem of limited resources and

increasing the served UEs, a set of Pico cells is proposed to be

deployed and overlaid the Macro cells which are suffering

from congested traffic. To perform this objective, two

challenges are to be processed; these are:

1) Determining the number of Pico cells sufficient to solve the

aforementioned problem which can't be calculated analytically

due to non-uniform distribution of the UEs.

2) How to configure the Pico cells in terms of power? How

much is the sufficient transmitting power to produce an

interference aware network?

The proposed MS-PSO scheme deals with these problems as

described below.

The M macro cell site is constructed with three-sectors of

each cell and P set of pico cell are dropped in the macros' area

at the coverage hole in terms of capacity. The proposed

scheme is performed through the ABS implementing eICIC,

in order to select the appropriate number of pico cells with an

appropriate CRE for each one, while the unselected pico cells

will be switched off (sleeping mode) till there will be a need

according to the TL profile.

According to the multi objective problems, a self-organization

procedure represented by new MS-PSO scheme is proposed

consisting of two loops depicted by the flow chart in figure

(1). The outer loop is implemented with BPSO to activate

sufficient cells to cover a maximum number of UEs, while the

inactive ones enter a sleep mode to verify efficient power

consumption.

For each iteration in the outer loop (with a set of active picos),

the picos' power are optimized by the inner loop by

formulating a multi objective cost function based on PSO.

The objective of inner loop is to increase the cell power only

if the DL data rate of this cell is increased. Both the outer and

the inner loop behavior are governed by the TL in such a way

that; the power is minimized as the TL decreases to reduce the

interference and prevent extra consumption expenditure.

Figure 1: Proposed MS-PSO procedure

As defined by the standard, the radius of the small cell is

extended by increasing the transmitting power by a certain

amount of power called bias. At each iteration of the inner

loop different biases associated to different BSs in the selected

group including the macro cell materialized by the random

transmit power vector Pw = [Pw1, Pw2, · · · Pwj, · · · Pwp, Pwm ],

The UEs in serving queue are associated sequentially to the

jth serving BS under two conditions; first, serving BS must

provide maximum received power, which in this case included

the biases (𝑃𝑟𝑘,𝑗𝑈𝐸 + 𝑏𝑎𝑖𝑠𝑗) as in eqn. (3).

Serving BS (j) = max (𝑃𝑟𝑘,𝑗𝑈𝐸 + 𝑏𝑎𝑖𝑠𝑗) ……….……… (3)

Subjected to

C1:𝑃𝑟𝑘,𝑗𝑈𝐸 + 𝑏𝑎𝑖𝑠𝑗 ≥ 𝑃𝑚𝑖𝑛

𝑈𝐸 …………(3.a)

C2: 𝐶𝑗𝐵𝑆 ≥ 𝐷𝑒𝑘

𝑈𝐸 ………………..…(3.b)

The kth UE is associated to the jth BS if C1 is verified; that

the maximum received power must be certainly exceeding or

equals the minimum acceptable UE received power donated

by ( 𝑃𝑚𝑖𝑛𝑈𝐸 ) as in eqn. (3.a). The second constraint is that, the

serving BS capacity (𝐶𝑗𝐵𝑆) must be exceeded or equals the

UE's demand (𝐷𝑒𝑘𝑈𝐸) as in eqn. (3.b).

The system capacity is determined, by the available Band

Width (BW) and the Signal to Noise Ratio (SNR) of the UE



10202

which in turn determines its spectral efficiency by mapping

the SNR to a specified modulation and coding schemes [15].

Accordingly, at each inner iteration the summation of UEs'

data rate become a function of the BSs' power. Increasing the

power results in increasing the served UEs but at the cost of

interference and power consumption.

Accordingly the inner optimizing procedure aims to determine

the optimal power (𝑝𝑤∗ ) for maximum DL data rate as

expressed by the cost function in eqn. (4).

𝐶𝑜𝑠𝑡 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑖𝑛𝑛𝑒𝑟 = 𝑎𝑟𝑔𝑚𝑎𝑥 ∑ ∑ 𝐷𝑅𝑘,𝑗𝑈𝐸 (𝑝𝑤𝑗

∗𝑘𝑗 ) ∗∝𝑘,𝑗…

(4)

Subjected to C1: 𝑝𝑤∗ ∈ 𝜉 , 𝜉 = [0, 𝑝𝑤𝑚𝑎𝑥] …….. (4a)

C2: 𝐷𝑅𝑘,𝑗𝑈𝐸 = 𝐷𝑒𝑘

𝑈𝐸 ……………….….... (4b)

C3: ∝𝑘,𝑗 ∈ {0,1} ⩝ 𝑘, 𝑗 ……………….. (4c)

𝑝𝑤𝑗 is the jth BS's power, 𝐷𝑅𝑘,𝑗𝑈𝐸 is the down link data rate

from jth BS of to the kth UE, which is a function of optimal

power. C1 determines the optimal power limitation values that

must be between zero (which indicates a sleep mode) and

maximum threshold value. C2 ensures that the UE is served

with its' full demand (FIFO is the scheduler used in this

work). C3 ensures that the served UE is associated to only one

BS.

At the end of inner loop the total number of served UEs can

be identified with an optimal performance for the selected

pico cells group. The total served UEs will be the cost

function of the outer loop as in eqn. (5).

𝐶𝑜𝑠𝑡 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛𝑜𝑢𝑡𝑒𝑟 = 𝑎𝑟𝑔𝑚𝑎𝑥 ∑ 𝑈𝐸𝑗𝑠𝑒𝑟𝑣𝑒𝑑

𝑗 ……… (5)

Where 𝑈𝐸𝑗𝑠𝑒𝑟𝑣𝑒𝑑 is the number of served UEs by the jth BS.

The interaction between the two loops will continue till

finding the sufficient number of pico cells with best energy

efficiency to satisfy at least 85% of served UEs.

The evaluation of the proposed MS-PSO will be as follows;

1- The planning of LTE network in the 1st phase is being

evaluated through the network simulation done by the ICs

Telecom software.

2- The second phase results is being evaluated (besides the

simulation done by the ICs Telecom software) using EE and

Energy Efficiency gain (𝐸𝐸𝑔𝑎𝑖𝑛). The BS's EE can be defined

as the ratio of the total amount achievable data rate and the

total power consumption as in eqn.(6) [5, 9].

𝐸𝐸𝐵𝑆𝑗 (𝑏𝑖𝑡 𝑠𝑒𝑐 𝐻𝑧 𝑤𝑎𝑡𝑡)⁄⁄⁄ =∑ 𝐷𝑅𝑘,𝑗

𝑈𝐸𝑘

𝑝𝑤𝑗

…………… (6)

And the total EE for the overall HetNet is the summation of

total down link data rate from all macro and pico cells divided

by their total power consumption as in eqn. (7)

𝐸𝐸𝐻𝑒𝑡𝑁𝑒𝑡 (𝑏𝑖𝑡 𝑠𝑒𝑐 𝐻𝑧 𝑤𝑎𝑡𝑡)⁄⁄⁄ =∑ ∑ 𝐷𝑅𝑘,𝑚

𝑈𝐸 + ∑ ∑ 𝐷𝑅𝑘,𝑝𝑈𝐸

𝑘𝑝𝑘𝑚

∑ 𝑝𝑤𝑚𝑚 + ∑ 𝑝𝑤𝑝𝑝 (7)

The 𝐸𝐸𝑔𝑎𝑖𝑛 indicates the EE improvement due to the proposed

MS-PSO scheme (𝐸𝐸𝑀𝑆−𝑃𝑆𝑂) comparing to a reference model

(𝐸𝐸𝑟𝑒𝑓.) (will be discussed later) as in eqn. (8).

𝐸𝐸𝑔𝑎𝑖𝑛 =𝐸𝐸𝑀𝑆−𝑃𝑆𝑂−𝐸𝐸𝑟𝑒𝑓.

𝐸𝐸𝑀𝑆−𝑃𝑆𝑂 × 100%............................... (8)

NETWORK SIMULATION, ANALYSES AND

DISCUSSION

Network simulation assumptions:

The nominal parameters of the simulated LTE network are

given in table (1), for both macro and pico cells. The Cost-231

Hata model is adopted for path loss calculation [16].

Table 1: Nominal parameters for LTE network

Parameter nominal values

Macro cell Pico cell

BS antenna height (metres) 35 20

BS Antenna gain (dBi) 14 4

BS Transmitted power (W) 20 0.4

BS TX antenna directional Omni

Channel Band Width 5 MHz 5 MHz

Carrier frequency 2.6 GHz 2.6 GHz

Symbol Guard Time interval normal normal

Thermal Noise Floor (KTB) -103 -103

Tx cable losses (dB) 1 1

Rx Cable Losses (dB) 1 1

Input impedance (ohms) 50 50

BS Noise Figure(dB) 4 4

Required SNR at cell edge(dB) 8 8

Parameter UEs

UE antenna height (metres) 6

UE Noise Figure(dB) 7

UE antenna gain (dBi) 5

UE scheduler scheme FIFO

Three different traffic profiles (voice dominate, data dominate

and mix services) are adopted in this work [17], which are

assumed arbitrarily to each UE as given in table (2). The daily

traffic profile is assumed to be as in table (3) according to the

daily work hours in Iraq.



10203

Table 2: Adopted traffic profiles of UEs

Demand

Kbps

Low

traffic

Demand

Kbps

Moderate

traffic

Demand

Kbps

Heavy

traffic

Number of deployments'

traffic profile

Active

deployments/location

UE

Mix Data voice

1213 4244 6064 100 222 100 400 M Ministry

792 2772 3960 80 120 100 300 H Hospital

144 504 720 20 20 12 50 Hh Health Centre

1213 4244 6064 100 200 100 400 U University

654 1551 2222 42 60 80 200 C Collage

554 2224 2522 22 22 22 262 O Office

122 352 512 - 22 22 62 X Telephone

exchanger

411 2365 1224 52 52 42 142 B Bank

606 2120 3355 122 22 62 240 I Institute

322 1331 1512 62 42 22 221 F Factory

Table 3: Assumed daily traffic percentage

Traffic Load Heavy traffic Moderate traffic Low traffic

% Demand 100% 70% 20%

Period 10am-1pm 8am-10am &

1pm-3pm

3pm-7am

Phase One Simulation Results:

Twelve candidate sites are chosen to be the input to the first

phase for LTE planning. Figure (2.a) represents the coverage

of these sites, where each colored point in the covered area

indicates the power received at that point as explained by the

color bar. The small arrows represent the UEs and the colors

indicate their affiliation as in table (2). The sites overlapping

area are indicated by pink color in figure (2.b). In this phase

BPSO is used to select the best set of sites in terms of

minimum overlap and maximum covered UEs.

(2.a) Macro cells coverage (2.b) Macro cells overlap

Total area=587.25 Km2

Covered area=472.2 Km2

Covered area%= 80.4

SSs covered due to field strength (FS) % =100%

Total SSs= 231.

Overlap%=47.5%

Figure 2: Set of candidate Macro sites.



10204

Four sites are eliminated (using BPSO algorithms explained in

section 2.3) represented by black tower or square in figure (3).

The remaining eight sites are sufficient to achieve a good

coverage where the overlap is reduced by 37%. After sites

selection, each site is provided by three directional antennas to

exploit 5MHz BW at each sector. Frequency reuse of 1x3x3 is

implemented in order to reduce the interference between

neighboring sectors. Figure (4) depicts the simulated LTE

network and each BS's associated UEs. Each BS is denoted by

Mj, where M referred to macro cell and j indicates the cell

number.

70% coverage

11% overlap

94% SSs covered due to FS

Increasing antenna high

100% SSs covered due to FS

Figure 3: LTE sites after optimization



10205

Figure 4: Eight sites-3sector-1x3x3 LTE network and their UEs

Network Capacity Analyses :

The network performance in terms of served UEs and DL data

rate is simulated and analyzed for various TL given in table

(3). Figures (5-8) depict the simulation results, where small

arrow represents served UE while the small square represents

the un-served one. It is obvious that the non-uniform

distribution of UEs leads to hot spot in certain sites, where

M1-M9 (enclosed by red circle in figure (5)) are considered as

congested sectors and they are suffering from high load.

Figure 5: Served UEs and congested sectors

Figure 6: DL bit rate and served UEs at Heavy traffic, 57.78% served UEs



10206

Figure 7: DL bit rate and served UEs at Moderate traffic, 68 % served UEs

Figure 8: DL bit rate and served UEs at Low traffic, 94.5 % served UEs

Phase Two Simulation Results:

In order to solve the problem of limited cell capacity, the

target cells M1-M9 are being chosen for HetNet deployment,

and then the HetNet operation is analyzed and optimized

using the proposed MS-PSO. First a set of pico cells are

deployed at the hot spot of these sites as depicted in figure (9).

The number of pico cells is being determined experimentally

in such a way that the served UEs are no less than 80% when

the picos' bais equals 1dB and 88% when the picos' bais

equals 10dB at heavy traffic.

M1,M2,M3 M4,M5,M6 M7,M8,M9

Figure 9: The (22) Pico cells for HetNet implementation



10207

To increase the percentage of served UEs, pico CRE is needed

which requires increasing the pico transmitting power in

optimal manner in order to introduce an interference aware

framework. A target site M7, M8, M9 is chosen to implement

and analyze MS-PSO proposed scheme. The bais of the pico

cells are being optimized in the range from 1dB to 10dB

according to the three different TL scenarios as depicted in

figure (10). The optimization is being carried out during

implementation eICIC at ABS. Figure (10) obviously shows

how the proposed MS-PSO control the cell size and cell

activity according to the TL, where red cross represents the

deactivation of pico cell during the sleep mode. Figure (11)

illustrated the related picos' transmitting power.

TL=100% TL=70% TL=20%

Figure 10: HetNet coverage optimization due to different TL

Figure 11: HetNet transmitting power with different traffic load profiles

To evaluate the performance with the proposed MS-PSO

scheme, other analyses and comparisons are performed

comparing other reference models performance. These

reference models are; 1) homogenous network with macro

cells only, 2) HetNet with maximum picos' power without

macro cell activation denoted by M-eICIC, 3) HetNet with

maximum picos' power and macro cell activation denoted by

M-FeICIC and 4) HetNet with optimized power for all pico

cells and macro cell denoted by PSO-FeICIC.

Figure (12) and (13) illustrate the improvement in HetNe

performance, in terms of percentage of served UEs and EE

respectively, due to performing the proposed MS-PSO in

comparison with the aforementioned four reference models.

The EEgain according to these comparisons is given in tables

(4)-(6) for various simulations.



10208

Figure 12: HetNet served UE% comparison

Figure 13: HetNet energy efficiency comparison

Table 4: Macro vs. non optimized HetNet ( FeICIC)

M7, M8, M9

TL=100% TL=70% TL=20%

EE-Macro EE-HetNet EE gain EE-Macro EE-HetNet EE gain EE-Macro EE-HetNet EE gain

9.910 11.95 17.08 11.14 13.113 15.02

25.92 16.295 -59.08

Table 5: Macro vs. optimized HetNet ( FeICIC-PSO)

M7, M8, M9

TL=100% TL=70% TL=20%

EE-Macro EE-HetNet-PSO EE gain EE-Macro EE-HetNet-PSO EE gain EE-Macro EE-HetNet-PSO EE gain

9.910 47.400 79.09 11.14 50.237 77.81 25.92 44.655 41.945

Table 6: Optimized (PSO-FeICIC) vs. non optimized ( FeICIC) HetNet

M7, M8, M9

TL=100% TL=70% TL=20%

EE-HetNet EE-HetNet-PSO EE gain EE-HetNet EE-HetNet-PSO EE gain EE-HetNet EE-HetNet-PSO EE gain

15.37 47.40 67.56 17.32 50.24 65.51 19.83 44.655 55.584



10209

From the above results, it is clear that the number of served

UEs depends on the traffic load, the number and the location

of pico cells. M-FeICIC and M-PSO-FeICIC achieve the

highest served UEs at all traffic load profiles. The best

performance, in terms of energy efficiency is achieved when

the proposed MS-PSO scheme is performed, while the worst

situation for heavy and moderate traffic load was obtained by

the first model, which indicates the improvement in

performance achieved by implementation of HetNet. At low

TL, the worst performance, in terms of energy efficiency, is

obtained by M-FeICIC, which indicates that increasing the

power without optimization will increase the served UEs but

at the cost of power consumption.

The results illustrated in tables (4)-(6) are verified these

conclusion. In general, the highest energy efficiency gain is

achieved at heavy traffic load for the optimized HetNet

compared to the homogenous network. The lowest energy

efficiency gain is achieved at low traffic load for the proposed

model compared to all reference models. That’s again ensure

that increasing the power not necessarily enhance the

performance as illustrated by the last column in table (4) at

low load.

The performance of a given site, in terms of interference, is

also evaluated before (for different picos' power) and after

implementing MS-PSO using ICs Telecom software at heavy

traffic as depicted in figure (15). The numerical simulation

results are represented in figure (16).

_Picos' biases= 1dB _Picos' biases = 10dB _Optimized Picos' biases

Figure 14: M7, M8, M9 Picos' power effect on the covered area in terms of (C/I+N)

Figure 15: Numerical analyses of Pico cells coverage in terms of C/(N+I)

The simulation results represented by figures (15) and (16)

verified that the efficient control on the power consumption is

achieved when implementing the proposed MS-PSO scheme,

where the served UEs are increased in an interference aware

frame work. It can be noticed that the covered area of the

optimized cells in terms of C/(N+I), represents a tradeoff

between the best and worst interference scenarios.

CONCLUSIONS

In this work a new MS-PSO for HetNet planning and

operation is proposed. MS-PSO is a self-organizing

procedure, where each macro cell can implement it to

optimize the number and the power of its pico cells according

to the TL profile. The implementation of the proposed MS-

PSO enables the macro cell to active a sufficient number of

small cells at heavy traffic. As the traffic decreases the



10210

proposed scheme optimize the small cells zooming by

controlling their power, in such a way that some or all these

small cells will be switched off and going in sleeping mode at

low traffic. In this way the MS-PSO schemes introduce an

interference aware framework for HetNet operation by

reducing the power consumption whenever there is no need to

increase it. The proposed MS-PSO is evaluated in terms of

served UEs and network EE and compared to other models. In

general the optimized HetNet achieved 38% , 44% and 6% of

served UEs compared to homogenous network at heavy,

moderate and low TL. The network EE is enhanced by 79%,

78% and 42% for heavy, moderate and low TL respectively

compared to homogenous network by implementing the

proposed MS-PSO. Also comparing the optimized HetNet

with the non-optimized one, 68%, 66%, and 56% of EE

improvement is being gained for heavy, moderate and low TL

respectively. The LTE network is simulated by ICs Telecom

software for evaluating the proposed scheme in terms of

interference, where the optimized HetNet interfered area

represents a moderate case between the best and worst case

interference scenarios. Generally, the proposed MS-PSO

shows good results in planning and managing the operation of

HetNet, which resulted in achieving higher percentage of

served UEs in an interference aware environment.

REFERENCES

[1] Z. Bharucha, E. Calvanese, J. Chen, X. (2012),"

Small Cell Deployments: Recent Advances and

Research Challenges", Proceedings 5th International

Workshop on Femtocells , King's College London,

UK.

[2] Hongyan Du, Lin TIan, Ling Liu1 and Z. (2015),"

An Interference-aware Resource Allocation Scheme

for Self-Organizing Heterogeneous Network," IEEE

Wireless Communication and Network Conference

(WCNC).

[3] Dahlman & Parkvall & Skold . (2014)," LTE-

Advanced .A Practical Systems Approach to

Understanding 3GPP LTE Releases 10 and 11 Radio

Access Technologies",CHP14., Elsevier’s Science &

Technology, UK.1029-1068.

[4] Ranplan Wireless Network Design Ltd. (2014),"

Outdoor LTE Small Cell Deployment on

Lampposts:A Paris City Study",

https://www.ranplan.co.uk/whitepaper/OutdoorSmall

CellRanplan_v4.pdf

[5] A. ABDULKAFI, W. (2014)," Energy Efficiency

Optimization of Next Generation Heterogeneous

Cellular Network", Ph.D. thesis, College of

Engineering Universiti Tenaga Nasional.

[6] V. Sciancalepore, V. Mancuso and A. B. (2013),"

BASICS: Scheduling Base Stations to Mitigate

Interferences in Cellular Networks", Proceedings of

IEEE World of Wireless, Mobile and Multimedia

Networks (WoWMoM), 1-9.

[7] H. Jiang, P. Li, Z. Li, E. Tong, Z. Pan, N. Liu, X.

(2014)," Improved MPSO Based eICIC Algorithm

for LTE-A Ultra Dense HetNets", Proceedings IEEE

Global Communications Conference, 3678 – 3683.

[8] Yanzan Sun, Zhijuan Wang, Yong Fang, Y. (2014),"

Resource management with multilevel interference

mitigation in Heterogeneous Network", Proceedings

International Wireless Communications and Mobile

Computing Conference (IWCMC), 1183 – 1187.

[9] Yinghao Jin, Ling Qiu, and X. (2015),"Small Cells

On/Off Control and Load Balancing for Green Dense

Heterogeneous Networks", Proceedings IEEE

Wireless Communications and Networking

Conference (WCNC), 1530 – 1535.

[10] ATDI. (2015)," ICS telecom Ng",

http://www.atdi.com/ics-telecom

[11] Satyobroto Talukder. (2011)," Mathematical

Modelling and Applications of Particle Swarm

Optimization",. Master’s Thesis, School of

Engineering at Blekinge Institute of Technology,

Sweden.

[12] Konstantinos E. Parsopoulos, M. (2012) Particle

Swarm Optimization and Intelligence: Advances and

Applications. IGI Global, New York. 26-28.

[13] Hector M. Lugo-Cordero, Abigail Fuentes-Rivera,

Ratan K. Guha and Eduardo I. O. (2011)," Particle

Swarm Optimization for Load Balancing in Green

Smart Homes", Proceedings IEEE Congress of

Evolutionary Computation (CEC), 715 – 720.

[14] Yamille del Valle, Ganesh Kumar

Venayagamoorthy, Salman Mohagheghi, Jean-Carlos

Hernandez, and R. (2008)," Particle Swarm

Optimization: Basic Concepts, Variants and

Applications in Power Systems", IEEE

TRANSACTIONS ON EVOLUTIONARY

COMPUTATION, VOL. 12, NO. 2.

[15] Mohammad T. Kawser, Nafiz Imtiaz Bin Hamid,

Md. Nayeemul Hasan, M. Shah Alam, and M.

(2012), "Downlink SNR to CQI Mapping for

Different Multiple Antenna Techniques in LTE",

International Journal of Information and Electronics

Engineering, Vol. 2, No. 5, 757-760.

[16] A. A. Ghaleb, A. S. Kaid, H. H. Ali, H. Esmaeel, M.

Sadeq, W. (2014)," LTE Network Planning and

Optimization", Graduation project, Faculty of

Engineering and IT, Communication Department,

Taiz University.

[17] Nokia Siemens Network. (2015)" Radio Planning

Capacity". white paper. http://www.slideshare.

net/mtrafi/ref-kpi

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7008954










Documents

Proposed Multi-Stage PSO Scheme for LTE Network Planning ... · PDF fileThe target area for LTE network planning is a part of Al-Resafa region in Baghdad city which has an area of