Inter-cell Interference Coordination in Heterogeneous

Network: A Qualitative and Quantitative Analysis

M. K.Hasan, A.F. Ismail, Aisha H. A, Khaizuran

Abdullah, HAM Ramli, Shayla Islam,Nazmus Nafi

Department of Electrical and Computer Engineering

International Islamic University Malaysia

hasankamrul@ieee.org

Hafizal Mohamad

MIMOS Berhed

Kuala Lumpur, Malaysia

hafizal.mohamad@mimos.my

Abstract—Heterogeneous Networks (HetNet) comprise

networks with specially designed architecture. To cover large

areas, established base stations (macro sites) are used; for coating

buildings, microcells are employed; for covering individual levels

or floors in a building, Pico-cells are utilized; and finally for

providing wireless coverage to individual apartments, small

offices, and home based businesses, femtocells (HeNodeBs) are

exploited. The goal behind the implementation of HetNets

involves incrementing the capacity, modifying spectrum use,

lowering the capital and operating costs, as well as offering

steady user-based experience of network architecture.

Furthermore, HeNodeBs have been developed for voice, data,

video services in home and offices and a certain outdoor scenario

with a very limited geographical coverage. However, these

random HeNodeB deployments raises co-tier and cross-tier inter-

cell interference which results system performance degradation.

The existing approaches have tried to solve Inter-cell

Interference (ICI) issues. Yet, these techniques are become

unsatisfactory for recent communications as aggressive

frequency reuse is desired to maintain high data-rates. In this

paper, we investigated and identified recent issues of inter-cell

interference coordination problems. The related schemes and

techniques are also analyzed in order to highlight the pros and

cons of each of the schemes.

Keywords—inter-cell interference, cognitive Radio, LTE-

Advanced; femtocell

I. INTRODUCTION

Impetuous degradation of performance level will be caused by

the core network from both of the HeNodeB-UE and the

people who have collaboration with the principal cellular

network. Certain conundrum will be developed with the

unexpected performance level degradation caused by the by

both the HeNodeB-UEs and people who may be working

together through the principal cellular network. Nonetheless,

for the enhancement of the total performance of the network,

an appurtenant interference management is required in

OFDMA for the HetNet of LTE-A systems.HetNets are

expected to be one of the major performance enhancement

enablers of LTE-A[1]. Surrounded by the low-power base

stations in HetNets, HeNodeBs are very important part

intended for capacity and coverage improvement within the

macro-eNodeB coverage.Furthermore, Deploying large

number of HeNodeBs in the indoor/outdoor environment is

also a critical problem for the inter-cell interference. The co-

channel deployment in macro-eNodeB and HeNodeBs could

increase the capacity of the network manifold through high

spatial frequency reuse [2],[3]. However, co-channel

deployment in macro-eNodeB and HeNodeBs results

interference in the network which becomes a key challenge in

HetNet. This paper is organized as follows: section 2 presents

heterogeneous network architecture. Section 3 presents problem statementsand section 4 presents the qualitative performance analysis. Conclusion is given in Section 5.

II. HETEROGENEOUS NETWORK ARCHITECTURE

A typical HetNets consists with small cells like HeNodeBs,

Pico-eNodeBs, RRH and macro-eNodeBs as represented in

Fig. 1. The indoor serving base stations are called underlay

network and outdoor serving base stations can be identified by

overlay network. The small cells such as HeNodeB are

connected to the operators via a third party internet service

provider. The macro-eNodeBs are connected by X2 interface

to another macro-eNodeBs. The underlay serving base stations

are applied due to its highly expendable data rates for voice,

video and applications.

Fig. 1. Representation of HetNets Architecture

III. PROBLEM STATEMENTS

LTE-A systems’ interference is considered to be a core

challenge in HetNets, where HeNodeBs are takings the

prevalence of the identical licensed frequency spectrum with

macro-eNodeB. In order to ensure an efficient manifestation

2013 IEEE 11th Malaysia International Conference on Communications

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of the deployment of HeNodeBs in HetNet, several core

matters with respect to the interference should be looked upon.

The HeNodeBs will apply the spectrum formerly, which is

allocated for cellular telecommunications, thus increasing the

dubiety to further its exploration. The HeNodeBs will be

arranged according to where it can be shown in terms of ad-

hoc fashion; denuded of the network planning which is

generally considered to be part of the deployment of cellular

telecommunications base stations. The main interference

scenarios has found with macro-eNodeB to HeNodeB (cross-

tier) and HeNodeB to HeNodeB (co-tier) for both

uplink/downlink [2]. Additionally, in the downlink and closed

access problems occur where, non-authorized user in the

neighborhood of the HeNodeB may experience severe

interference coming from the HeNodeB.Thereupon, the cross-

tier interference will be increasing more quickly. Cross-tier

interference represents the signal radiated on small cells UEs

because of the concurrent transmissions of neighboring macro-

eNodeBs and vice versa. In Fig. 2 inter-cell interference is

illustrated.Nonetheless, a breakout of cross-tier interference

can be done by initiating a new interference coordination

model in macro-eNodeB–Pico-eNodeB and HeNodeB

HetNets.

Fig. 2. HeNodeB interference scenarios

To overcome the inter-cell interference in HetNets the existing

schemes such as frequency scheduling scheme [5],adaptive

FFR scheme [6],[7], energy efficient interference mitigation

approach [8]-[10], joint and disjoint sub-channel allocation

scheme [11], resource sharing techniques [12] areproposed in

self-organizing networks.The existing schemes are basically

uses frequency reuse, resource scheduling, spectrum sharing,

and Cognitive Radio (CR) techniques. A CR for HetNets

basically works with licensed Primary Users (PU) available

spectrum to allocate the channels due tomitigate the

interference issues [4]. A CR HeNodeB adjacent network

scenario is depicted in Fig. 3. Each of the

approaches/techniques is illustrated in the next sections.

Fig. 3. Representation of cognitive radio in HetNets [4]

IV. PERFORMANCE ANALYSIS

As the inter-cell interference problems are identified in

HetNets, some of the existing schemes are promising in order

to coordinate interference[5]-[14]. In this section the

performance of these mechanisms are analyzed in order to

highlight the leads as well as limitations.

A. Frequency Scheduling Techniques

Various types of interference challenges have been observed

under the authors in [5], where inter-carrier- interference (ICI)

is analyzed. It is crucial to control the received interference

by disregarding the utilization of those frequency resources at

the HeNodeB network and use the spectrum resources

dexterously. To alleviate the Close Subscriber Group (CSG)

HeNodeB deployment Co-channel interference(CCI) quandary

between the HeNodeBs and macro-eNodeB UEs for both UL

and DL presence, a proposal has been made by the authors

with a framework of interference handling near-by macro-

eNodeB UEs and HeNodeBs. HeNodeB senses the spectrum

during UL, analyze the close-by users through UL scheduling

information, and abstain from using their spectrum specified

in DL scheduling information. However, this scheme takes

advantage of the use of the spectrum sensing result in

reference to the energy detection in reference to the distance

as possibility of information scheduling. An was proposed [5]

where receivers send information about their throughput,

interference, and signal levels by the means of broadcasting

messages tied to data reception. There is a relation between

the proposed scheduler and the without interference

consciousscheduler. With the assistance of numerical

instances, the performance of interference-aware scheduler is

estimated, and a comparison has been between two schedulers.

The global best possible transmission schedule is attained by a

central scheduler that possessescomplete system wide

information. Essentially, the confrontation stands that of the

scalability with the amount of users per cell. It becomes

important to time-domain multiplex of the UE over frames, or

subdivides the resource units to finer granularity, in case the

amount of UEs per cell increments. Therefore, this would

mean that there will be a loss of constancy or an increased

overhead. To reduce the Inter-Cell Interference (ICI) a novel

adaptive fractional frequency reuse scheme (AFFR) is

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proposed [6] for multi-cell OFDMA based IEEE 802.16e

network, where it is managed and operated by the Access

Service Network Gateway (ASN-GW),which coordinates a set

of BSs (one cluster). The authors enhanced this scheme

through applying Channel State Dependent Scheduler (LL-

CSDS) and utility based scheduling (LL-Until) [7]. Utility

based scheduling at BS (Proposed-Util) has proposed using

LL-Util, combining LL-CSDS at ASN-GW developed AFFR,

and the CSDS scheduling at BS come out with proposed-

CSDS. The cell area is virtually sorted to make the radio

resources into the fractional frequency reuse (FFR) zone

where the users are suffering from high ICI, from the

neighboring cells and the Full Usage (FU) area where ICI is

avoided. The base station BS allocates users to every zone

dynamically depending on their channel state information.

ASN-GW selects the set of subcarriers allocated to the FFR

zone within each BS. In the FU zone all subcarriers accessible

in the system may be used. Fig. 4 illustrated that the proposed

scheme at ASN-GW get better the system capacity contrasted

to LL ASN-GW method as soon as either combined with

utility-based or CSDS. It is observed from Fig. 5 that when

duration of ICIC_PERIOD increases the throughput degraded.

Meanwhile, with minimum ICIC_PERIOD, throughput is

maximized. However, from the analysis, it is obvious that all

of the studies formulated the orthogonal resource allocation

among adjacent cells as a vertex coloring issue, which has

been verified to be an computational complexity which is

Non-deterministic Polynomial-time hard (NP hard) problem.

In order to mitigate the interference region based technique

[13] is proposed to allocate frequency for dynamic cells.

However, the techniques is not bandwidth efficient. To lessen

the interference authors [14] proposed interference graph that

awareness and an algorithm on graph coloring. However, Cell

throughput is lower compare with static FFR.

B. Energy efficient interference mitigation approach

The authors in [8] have proposed interference mitigation

approach for energy efficient HeNodeB neighbor network

comparing with game theory [9]. The approach is handover

supported mutual protocol through CR along with game

theory methods to diminish interference. In order to improve

the performance a mutual aid has used to lessen energy

consumption, improve channel state as well as increase system

capacity [9]. In the model CR techniques applied with the

notation PU for primary HeNodeBA and SU for secondary

HeNodeBB. From the Fig. 6 it can be seen that the PU has the

interference boundary to restrict the SU. Through the CR a SU

can easily sense PUs from the far distance. The approach

include the PU and SU capacity which has expressed

respectably by the equation 1 through calculating SINR of SU

in equation 2, where PUs transmitted power ! has indexed by

k with its m users and "#$! is the channel gain from PU and

%& represents the noise variance [9].

Fig. 6. HeNodeB neighbor network CR scenario [8]

'! ( )*+& ,- . /0120$0134 /5125$013673859:

; (1)

<=>?#$! ( /5125@$5134 /A8A9:AB5

12A@$5136@/0120@$513673 (2)

To lessen interference along with fading consequences for

SUs the authors applied power control mechanism thereby the

interference power (=/C expressed by the equation 3 which is

applied in equation 2 [10]. Then the transmitted power ( #C to

the desired SU, the expression is in equation 4. where D is the

SU and "E@$# represents the gain of SU D to destined UE F. It

is found [10] that the all of SUs transmitted powers should be

less than the interference constraint G HC shown in equation 5

with the PU verified probability outage in equation 6 [9].

=/ ( 4 EIA9:AB51"E@$#1& .@ !1"!@$#1& . %& (3)

# ( JKLM5$0$KN125@$513 (4)

Fig. 4. System Capacity versus SINR_TH [7]

Fig. 5. Percentage of Degradation in

Throughput vs. ICIC_PERIOD Duration [7]

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4 #I59: 1"!@$#1& O H (5)

PQR0 S T*UV'! O W!X O PQR5YZ$@@@@[@!\]$&$^^^_` (6)

The maximum outage probability denoted by abcdefg and hi

is the transmitted data rate

(a) System capacity with the SUs for Cell 1

(b) System capacity with the SUs for Cell 2

Fig.7. System capacity for different cells [9]

Fig. 8.Transmitted power for SUs with handover [9]

It is observed from the Fig. 7 that the system capacity

enhanced with the number of SU which means the serving

HeNodeB can get better channel gain. In Fig. 8, it can be seen

that the scheme gets better energy competence and decrease

the power utilization. However, this scheme didn't concentrate

the interferences for macro-eNodeBs UE with HeNodeB.

C. A joint and disjoint sub-channel allotment technique

A joint and disjoint sub-channel allotment technique has

proposed for open and close access HeNodeBs to lessen the

macro-eNodeB with HeNodeB interferences [11]. The authors

has regard as the entire number of vacant spectrum has

bandwidth (W) and the total number of subcarriers to form

sub-channel (B) are accessible in (W/B). In order to minimize

the control signals this method use the consecutive fixed

number of subcarriers for generate the sub-channels. For the

sub-channel allocation, the scheme taken in account three sub-

channel techniques. Disjoint sub-channel for HeNodeB which

is unused by macro-eNodeB, meanwhile joint sub-channel

distribution technique applied for full spectrum utilize by both

macro-eNodeB as well as HeNodeB. Another category was

used for open access which can be mentioned by broad-

spectrum. Therefore, authors offered a policy to utilize the

shared sub-channel which is depicted in Fig. 9. Where

j k l@mn@opj q lp O pr q lp Which means user lst are

designated macro-eNodeB as well as r k l@mn@opj q lp upr q lp, lst are designated HeNodeB, o is the constant for

open access bounds as v w o u -.

Fig. 9. Sub-channel allocation policy [11]

The best possible sub-channel allocation has shown in Fig. 10

with the macro-eNodeB-HeNodeBs density. The scheme has

shown that it can provide better throughput in the case of

sparse as well as heavily dense HeNodeB network as depicted

in Fig. 11.

Fig. 10. Sub-channel allocation with respect to macro-eNodeB -HeNodeB

density ratio [11]

D. Resource Sharing Technique

The spectrum sharing scheme has proposed to mitigate

interference through exploiting cognitive information [12].

The authors Shin-Ming Cheng et al. (2012) represented

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interweave techniques, underlay as well as underlay

techniques for same spectrum reuse. However, it can be seen

from the analysis that the methods throughput improvement is

fluctuated. Moreover, energy efficiency as well as frequency

reuse has not considered. Furthermore, this scheme has

considered only downlink interferences. Therefore, the

scheme can be improve with the stable throughput in downlink

as well as uplink interference scenarios. It can be observed

that controlled underlay techniques has the capability to

improve the utilization of same spectrum as well as can reduce

the cross-tier interference. In Fig. 12 represented the

TABLE 1.COMPARISON INTERFERENCE MITIGATION APPROACHES

Approaches

Mechanism

Performance matrices

Improvements

Shortcoming

Fre

quen

cy S

ched

uli

ng

Sch

emes

Interference-

aware radio

resource

management

scheme [5]

• This approach is OFDMA based

system model on mutually closed,

open and hybrid access for uplink

and downlink.

• For uplink spectrum detected by

energy detection threshold

• Energy detection

threshold

• Power

• Path loss

• Delay

• Better performance,

• High competence

• Utility optimization

issues still remain.

• Severe co-tier and cross-

tier interference

• Performance evaluated

in computer based

simulation. An empirical

study is necessary.

Adaptive FFR

scheme [6]

Scheme is for multi-cell OFDMA

based IEEE 802.16e network,

managed and operated by the Access

Service Network Gateway (ASN-

GW), The cell area is virtually sorted

to allocate resource

• ICIC period for

system performance,

• SINR for threshold

• Inter-cell Interference

is minimized

• Static, bandwidth

inefficiency

• Vertex colouring

problem for orthogonal

adjacent cells,

• Computational

complexity

Region based

FFR [13] Technique developed with inner

region FFR which is nearby to the

macro-eNodeBs and outer region for

the UEs which are stayed in the

macro-eNodeB boundary zone or

cross boundary.

• Least allocation,

• System resource block

(RB) allocation

• Distance

• Path loss

• SINR.

• Total cell throughput

is satisfactory level

through varying the

inner radius as well as

frequency allocation

• Static frequency

allocation which is not

bandwidth efficient,

• Difficult to allocate

frequency for dynamic

cells.

Dynamic

FFR scheme [14]

This scheme taken account load

distribute to different traffic cells

where unequal and time-varying. To

administrate interference proposes

two phases:

• A interference graph that awareness

of FFR, as well as network topology

• An algorithm on graph colouring

• Cell throughput

• Service rate

• Power

• Considering of cell

throughput and service

rate,

• performance

improvement

• Cell throughput is lower

compare with static FFR.

• Computer based

simulation has proved,

test bed implementation

required to check the

performance.

Energy efficient

interference

mitigation approach

[8]-[10]

• The approach is handover supported

mutual protocol through CR along

with game theory methods to

diminish interference

• power control mechanism

• SNR

• system capacity

• power

• lessen energy

consumption,

• improve channel state

• increase system

capacity

• interferences for macro-

eNodeBs UE with

HeNodeB hasn't

considered.

• cross tier interference

Joint and disjoint

sub-channel

allocation

Schemes [11].

• Applied joint and disjoint sub-

channel allocation to mitigate cross-

tier interference.

• Open access policy for both

macro/HeNodeB to share the sub-

channels from available spectrum.

• Density ratio

• SNR

• Optimal sub-channel

Maximum throughput

observed during light

and dense HeNodeB

network

• Throughput instability.

• energy effectiveness and

spatial reuse never

considered.

Resource sharing

techniques [12]

.

• The scheme is based on interweave,

underlay and controlled underlay

techniques.

• Transmission capacity

• SINR as threshold

• Transmission radius

• Improved same

spectrum utilization

gain and

• Transmission

Capacity.

• Channel with location

information is needed.

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transmission capacity on different transmission power.

However, the schemes gain of the spatial reuse accomplished

through obtaining channel along with location information.

The pros and cons of each schemes performances are

summarized in Table 1

Fig. 11. Throughput with respect to macro-eNodeB -HeNodeB density ratio[11].

Fig. 12. Transmission radius on transmission capacities [12]

V. CONCLUSION

Coordination of inter-cell interference in HetNet is urging for

the operators in order to improve the system performances as

well as get the full benefit of 4G communications. In this

paper, we investigated and analyzed the existing schemes

strengths and limitations in terms of interference mitigation

for both co-tier and cross tier issues.

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2013 IEEE 11th Malaysia International Conference on Communications

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978-1-4799-1532-3/13/$31.00 ©2013 IEEE 366