Interfered Users Protection Algorithm for Self-Organizing ... · Interfered Users Protection Algorithm for Self-Organizing Networks . Chih-Hsiang Ho 1, Li-Sheng Chen , Wei-Ho Chung2,

Interfered Users Protection Algorithm for Self-Organizing

Networks

Chih-Hsiang Ho1, Li-Sheng Chen

1, Wei-Ho Chung

2, and Sy-Yen Kuo

1

1 Department of Electrical Engineering, National Taiwan University, Taipei, 10617, Taiwan

2 Research Center for Information Technology Innovation, Academia Sinica, Taipei, 11529, Taiwan

Email: {d97921025, d99921032, sykuo}@ntu.edu.tw; [email protected]

Abstract Small cells are generally used by telecom operators

to effectively share the loading of conventional macrocells and

backbone networks, because they cost considerably less to

deploy and are also capable of improving communication

quality and capacity. However, radio frequency spectrum

resources are limited and expensive, so using the same

frequency band to deploy small cells in densely populated

metropolitan environments could lead to overlapping signal

coverage between neighboring cells. If a user is situated in such

areas, this can result in severe radio signal interference and

result in a significant decline in network transmission

performance. This study therefore proposes combining cell

grouping and base station power adjustment, as well as radio

resource allocation algorithms, into an interference mitigation

method for small cells. According to simulation results, the

proposed Interfered Users Protection (IUP) algorithm produces

a significantly higher signal-to-interference-plus-noise ratio

(SINR) under varying numbers of base stations deployed, and

the effect is stronger under densely deployed base stations with

severe interference. Approximately 56% of the interfered users

maintained a data rate of 250 Kbps and above. In addition, the

average SINR of regular users were not affected; 67% of the

regular users were still capable of maintaining this transmission

rate. The IUP algorithm is capable of effectively improving

interfered users’ signals and transmission capacity, while

accounting for the transmission performance of the overall

network. Index Terms—Interfered user, regular user, cell grouping,

power adjustment, radio resource allocation

I. INTRODUCTION

Fourth Generation (4G) mobile communication

technology provides a higher data transfer rate and wider

signal coverage compared to its second and third

generation (2G and 3G) counterparts. However, several

studies and field investigations have discovered that over

90% of network services and two-thirds of the phone

calls in the cellular network occur in indoor environments

[1]. Simply deploying more macrocells is no longer

sufficient for improving communication quality and

capacity at a lower cost with high efficiency, as well as

mitigating the ever increasing volume of network traffic.

Therefore, the use of small cells in 4G technology and

standards (such as Long Term Evolution, LTE) has been

Manuscript received January 12, 2017; revised April 24, 2017.

doi:10.12720/jcm.12.4.234-239

proposed as the technological solution to improve

communication quality and offload macrocell and

backbone network traffic. Because small cells may be

deployed closely together in densely populated

metropolitan areas, this often results in overlapping

coverage between cells, as well as between small cells

and macrocells. The radio signals transmitted by Base

Stations (BS) with overlapping coverage can interfere

with each other and severely diminish network

performance. Automatic interference detection and

management have always been an essential research topic

in the Self-Organizing Networks (SON) field.

Interference management technology is a vital function of

self-optimization, which includes interference detection

and interference mitigation. As shown by the network

architecture of interference management in Fig. 1,

positioned at the back end of the Evolved Packet Core

(EPC) is a control center called the SON server, which is

responsible for the centralized collection and control of

BS and network parameters, as well as the execution of

interference detection and interference mitigation

algorithms [2], [3].

Fig. 1. Network architecture of interference management

Currently, analyses and improvements of network

interference problems are being actively explored and

researched. The problem of intercell interference was

noted in [4], and adaptive interference cancellation

Journal of Communications Vol. 12, No. 4, April 2017

234©2017 Journal of Communications

—

technology has been proposed to mitigate its effects.

Several studies have also employed approaches such as

radio resource management or power adjustment to solve

or reduce the interference [5]. A general description and

investigation of interference management and radio

resource allocation was offered in [6], whereas further

allocation of radio resources to improve use efficiency in

overlapping coverage is explored in [7]. A femtocell-

based management system that calculates and allocates

subcarriers to each femtocell was proposed in [8]. The

conceptions in [9] and [10] were constructed based on

graph coloring theory to develop an interference

reduction mechanism, whereas [11] and [12] used game

theory to consider the interference factors to construct the

optimal strategy for BS actual behavior prediction and

power adjustment. A distributed power adjustment

method was proposed in [13] to effectively improve the

signal quality for users at the cell edge of the base

stations. Power adjustment approaches were proposed

based on the Quality of Service (QoS) in [14] and [15] to

improve signal quality in environments with interference.

In addition, several studies on interference reduction used

the cognitive technology of wireless channels to avoid

interference on the basis of cognitive network technology,

as seen in [16] and [17], whereby cognitive radio was

used to allocate radio resources and transmit power.

Despite the many advantages of small cells, the severity

of their impact on communication quality due to

interference problems determines to a large extent their

actual transmission efficiency and application. In order to

overcome the interference problem, this study proposed

the interfered user protection (IUP) algorithm to improve

signal and connection quality to users in the interference

area. This study proposes using cell grouping, power

adjustment, and dynamic radio resource allocation

scheme with interference mitigation technology to inhibit

interference and improve overall network transmission

efficiency.

This paper is organized as follows. The system model

is described in Section II, IUP algorithm is proposed in

Section III, and performance evaluations are described in

Section IV. Concluding remarks are presented in Section

V.

II. SYSTEM AND PROBLEM MODELING

Under the dense and co-channel deployment of small

cells, the coverage of small cells overlaps with

neighboring small cells and macrocells. While user

moves into the overlapping area, the physical-layer signal

experiences severe interference, and the communication

quality and capacity are severely degraded. This section

elaborates and defines the system. Considering that the

small cell experiences interference from neighboring

small cells and macrocells, the SINR of the user j

connected to small cell i is expressed as:

𝑆𝐼𝑁𝑅 𝑖,𝑗

𝑘=

𝑎𝑖,𝑗𝑘 𝑝𝑖,𝑗

𝑘 𝑔𝑖,𝑗𝑘 𝑃𝐿𝑖,𝑗

∑ 𝑝𝑖′, 𝑗𝑘 𝑔

𝑖′𝑘

𝑖≠𝑖∗ 𝑃𝐿𝑖′,𝑗

+ 𝑁0 (1)

where 𝑎𝑖,𝑗𝑘 represents whether the physical resource block

(PRB) k at base station i has been allocated to user j ,

where 𝑝𝑖,𝑗𝑘 represents the power of the PRB k at the small

cell i received by user j, 𝑔𝑖,𝑗𝑘 represents the antenna gain

of PRB k at small cell i, 𝑃𝐿𝑖,𝑗 represents the path loss

between base station i and user j, 𝑝𝑖′, 𝑗𝑘 represents the

power of PRB k at another small cell 𝑖′ received by user j,

𝑔𝑖′ ,𝑗𝑘 represents the antenna gain for PRB k at another

base station 𝑖′, 𝑃𝐿𝑖′,𝑗 represents the path loss between the

small cell 𝑖′ and user j, and 𝑁0 represents white Gaussian

noise. The data rate DRi,j for user j at small cell i can be

expressed as:

𝐷𝑅𝑖,𝑗 = ∑ 𝑎𝑖,𝑗 𝑘 . 𝐵 . 𝑙𝑜𝑔2 (1 + 𝑆𝐼𝑁𝑅

𝑖,𝑗

𝑘)

|𝐾|𝑘 (2)

where 𝑎𝑖,𝑗𝑘 represents whether the PRB k at small cell i

has been allocated to user j; B represents the bandwidth

of each PRB; and 𝑆𝐼𝑁𝑅 𝑖,𝑗

𝑘 represents the SINR for user j,

who is connected to small cell i and who has been

allocated PRB k.

The objective function of the proposed algorithm can

be expressed as follows:

𝑀𝑎x 𝛼 . ∑ ∑ 𝐷𝑅𝑖,𝑗

| 𝑈𝑅𝐶𝑈𝑖 |

𝑗=1 + 𝛽 . 𝑅𝑅𝐶𝑈

|𝑆|𝑖=1 (3)

Subject to:

𝐷𝑅𝑖,𝑗 ≥ 𝐷𝑖,𝑗_𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 , 𝑗 ∈ 𝑈𝑅𝑈𝑖 (4)

∑ 𝑎𝑖,𝑗𝑘 =

|𝑈𝑖|

𝑗=1 1 (5)

𝛼 + 𝛽 = 1 (6)

where S represents the set of small cells, 𝑈𝑅𝐶𝑈𝑖 represents

the set of users that can successfully connect to base

station i after recovery, 𝑅𝑅𝐶𝑈 represents the proportion of

users in the interference area whose connections are

restored after effective recovery (i.e., recovered users,

RCUs), 𝐷𝑅𝑖,𝑗 represents the data rate for user j at cell i,

𝐷𝑅𝑖,𝑗_𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 represents the threshold data rate of

regular user, 𝑈𝑅𝑈𝑖 represents the set of regular users at

small cell i, 𝑎𝑖,𝑗𝑘 represents whether PRB k at small cell i

has been allocated to user j, and 𝛼 and 𝛽 are weight

values.

The IUP algorithm is proposed to improve the signal

and transmission quality of the interfered users through

BS power adjustment and radio resource allocation

without reducing the connection quality of other regular

users, thereby enhancing the overall network capacity.



III. I PROTECTION ALGORITHM

Before the BS can perform dynamic power

adjustments, the SINR of the downlink from the BS to

UE must be obtained, as well as the reference signal

received power (RSRP) from the other interfering BS. If

the SINR of a user is smaller than the SINR threshold,

and the interfering RSRP is larger than the interfering

RSRP threshold, we define this user as the interfered user.

The pseudocode for our proposed IUP algorithm is shown

in Fig. 2.

After classifying the interfered user, the SON server

calculates the number of interfered user in each BS and,

based on this number of interfered user, categorizes the

base stations. The base stations with higher numbers of

interfered user are placed in the high-priority group.

Power adjustments are then performed. Generally, a

higher transmitting power of a BS results in a higher

receiving power for the users associated with it, and a

higher interference for users associated with other base

stations. However, the lower transmitting power of a BS

results in lower receiving power for its associated users,

and lower interference for users associated with other

base stations. The base stations in the higher priority

group have more interfered users, and their power

adjustments are expected to induce a higher influence on

the overall SINR. Therefore, power adjustments are first

performed on the base stations in the higher priority

group. The power is initially increased by 1 unit from its

initial power level, and then decreased by 1 unit from the

initial power level; the average power levels

corresponding to the two adjustment directions are

checked, and the power level corresponding to the higher

average SINR is maintained.

Subsequently the power adjustment continues in the

direction of increasing the average SINR until one of the

following conditions is met: (1) the average SINR of

interfered user ceases to increase; (2) the upper bound or

lower bound of power levels is reached; (3) the power

adjustment leads to the downgrade of SINR level in a

user; (4) any of the regular users’ status is changed to

interfered user. The base stations without interfered user

continue to decrease transmission power until one of the

above four conditions are met. Finally, the power levels

of each BS can be obtained and set.

IUP algorithm

Notation:

𝑔: The groups with number of interfered user of BS

G: The set of base station group

BS𝑔: The BS is belong to the group 𝑔

𝑝𝑜𝑤𝑒𝑟𝑔: The initial tx power of BS of group 𝑔

𝑝𝑜𝑤𝑒𝑟𝑔∗: The adjusted tx power of BS of group 𝑔

SINR𝑟ui.j: The SINR of regular user j of cell i

SINR𝑟𝑢𝑖,𝑗

lower_bound: The SINR threshold of regular user j of cell i

𝜏: The constant guard interval of SINR for power adjustment

process

∆P: The minimum adjustment step of tx power forBS

𝑂𝑏𝑗+: The value of objective function after increasing power of

BS𝑔by ∆P 𝑂𝑏𝑗−: The value of objective function after decreasing

power of BS𝑔by ∆P

𝑂𝑏𝑗∗: The current value of objective function

K: The set of PRBs

U: The set of users

𝑢𝑖,𝑗 : The user 𝑗 of the cell i

𝜔𝑖,𝑗 : The weighting factor of user 𝑢𝑖,𝑗

𝜑𝑢𝑖,𝑗: The number of PRBs allocated to 𝑢𝑖,𝑗

σui,j: The number of PRBs requested by 𝑢𝑖,𝑗

𝑆𝐼𝑁𝑅𝑖,𝑗𝑘 : The SINR at PRB k of 𝑢𝑖,𝑗

𝑎𝑗∗ 𝑘𝑖 : The PRB k at small cell i has been allocated to user 𝑗∗

𝑆𝑗∗𝑖 : Non-interference relationship set of 𝑢𝑖,𝑗∗

𝐵𝑛𝑘 : The updating user set for resource allocation

BEGIN

Grouping base stations with the number of interfered users into

groups

FOR 𝑔 = |𝐺| to 1

Frist try to increasing and decreasing power of BS𝑔by ∆P

IF 𝑂𝑏𝑗+ > 𝑂𝑏𝑗− && 𝑂𝑏𝑗+ > 𝑂𝑏𝑗∗ then

𝐑𝐄𝐏𝐄𝐀𝐓

𝑂𝑏𝑗∗ = 𝑂𝑏𝑗+

𝑝𝑜𝑤𝑒𝑟𝑔= 𝑝𝑜𝑤𝑒𝑟𝑔 + ∆𝑃

calculate 𝑂𝑏𝑗+

𝑝𝑜𝑤𝑒𝑟𝑔∗ = 𝑝𝑜𝑤𝑒𝑟𝑔

UNTIL 𝑂𝑏𝑗+ < 𝑂𝑏𝑗∗ || 𝑝𝑜𝑤𝑒𝑟𝑔∗ is maximum tx power

of BS || SINR𝑟ui.j+ 𝜏 ≤ SINR𝑟𝑢𝑖,𝑗

lower_bound ||

regular user become the interfered user

RETURN 𝑝𝑜𝑤𝑒𝑟𝑔∗ and 𝑂𝑏𝑗∗

ELSEIF 𝑂𝑏𝑗− > 𝑂𝑏𝑗+ && 𝑂𝑏𝑗− > 𝑂𝑏𝑗∗ then


𝑂𝑏𝑗∗= 𝑂𝑏𝑗−

𝑝𝑜𝑤𝑒𝑟𝑔 = 𝑝𝑜𝑤𝑒𝑟𝑔 − ∆𝑃

calculate 𝑂𝑏𝑗−


UNTIL 𝑂𝑏𝑗− < 𝑂𝑏𝑗∗ || 𝑝𝑜𝑤𝑒𝑟𝑔∗ is minimum tx power

of BS || SINR𝑟ui.j+ 𝜏 ≤ SINR𝑟𝑢𝑖,𝑗

lower_bound ||

regular user become the interfered user


ELSE

without changing the current power

ENDIF

ENDFOR

FOR 𝑔 = 0


𝑂𝑏𝑗∗ = 𝑂𝑏𝑗−

𝑝𝑜𝑤𝑒𝑟𝑔 = 𝑝𝑜𝑤𝑒𝑟𝑔 − ∆𝑃

calculate 𝑂𝑏𝑗−


UNTIL 𝑂𝑏𝑗− < 𝑂𝑏𝑗∗ || 𝑝𝑜𝑤𝑒𝑟𝑔∗ is minimum tx power of BS



NTERFERED USER

|| SINR𝑟ui.j+ 𝜏 ≤ SINR𝑟𝑢𝑖,𝑗

lower_bound || regular user

become the interfered user


ENDFOR

Execute the dynamic radio resource allocation after the power

adjustment

FOR 𝑘 = 1 to |𝐾|

𝑛 = 1 ;

𝐵𝑛𝑘 = U ;

DO

𝑢𝑖,𝑗∗ = argmax𝑗∈ 𝐵𝑛

𝑘 𝜔𝑖,𝑗

𝜑𝑢𝑖,𝑗/𝜎𝑢𝑖,𝑗

. 𝑆𝐼𝑁𝑅𝑖,𝑗𝑘

𝑎𝑗∗ 𝑘𝑖 = 1

𝑆𝑗∗𝑖 is set to empty set

Find the non-interference relationship set 𝑆𝑗∗𝑖 of user

𝑢𝑖,𝑗∗

n = n + 1;

𝐵𝑛𝑘 = 𝐵𝑛−1

𝑘 ∩ 𝑆𝑗∗𝑖

WHILE 𝑘 ≤ |𝐾| 𝑎𝑛𝑑 𝐵𝑛𝑘 ≠ ∅

ENDFOR

END

Fig. 2. Pseudocode for IUP algorithm

Because the interfered users differ from regular users

who possess more stable radio resources allocated from

the BS, the interfered users should have a higher priority

than the regular users in obtaining radio resources during

the process of BS power adjustment and radio resource

allocation. Therefore, the dynamic radio resource

allocation mechanism was added, where 𝜔𝑖,𝑗 represents

the weight of the j-th user in the i-th BS. If j belongs to

the interfered user set, then 𝜔𝑖,𝑗 will be higher; 𝜑𝑢𝑖,𝑗

represents the number of PRBs allocated to 𝑢𝑖,𝑗, and 𝜎𝑢𝑖,𝑗

represents the number of PRBs requested by 𝑢𝑖,𝑗. For the

proposed PRB dynamic allocation mechanism, each PRB

is first individually allocated to the 𝑢𝑖,𝑗∗ that yields the

highest 𝜔𝑖,𝑗


. 𝑆𝐼𝑁𝑅𝑖,𝑗 . The PRB can also be

allocated to users in other cells who do not have an

interfering relationship with 𝑢𝑖,𝑗∗ [18]. The priority

allocation of higher quality PRBs to interfered users was

achieved through 𝜔𝑖,𝑗 to restore the stability of the

interfered users’ signal and attain an adequate

transmission quality; the fairness of allocation was

simultaneously improved through 𝜑𝑢𝑖,𝑗/𝜎𝑢𝑖,𝑗

to maintain

the transmission quality of other regular users.

IV. PERFORMANCE ASSESSMENT

The proposed IUP algorithm was compared to the QoS

priority-based dynamic frequency allocation algorithm

(QPDFA) [13] and the opportunistic spectrum access and

channel dynamics algorithm (OSACD) [14] to assess its

performance. In the simulation environment, the distance

between base stations was defined as 50~200 m, the

antenna downtilt was defined as 15°, the maximum base

station output power was defined as 30 dBm, the

maximum UE output power was defined as 20 dBm, and

the antennas were defined as 3GPP models [19]. Table I

lists these and other relevant simulation parameters.

Algorithm performance was assessed by four indicators:

(1) the average SINR of interfered users; (2) the average

SINR of regular users; (3) the cumulative distribution

function (CDF) of the data rate among interfered users;

(4) the CDF of data rate among regular users.

TABLE I: SIMULATION PARAMETERS

Number of BS 10~50

Inter-site distance 50~200 m

Antenna downtilt 15°

System bandwidth 10 MHz

Maximum Base Station

output power 30 dBm

Maximum UE output

power 20 dBm

Path Loss 128.1 + 37.6 log10

r, with r in km

Antenna model 3GPP model [19]

Noise level -199 dBW/Hz in DL, -195

dBW/Hz in UL

Service

Generic elastic data service

with a requested throughput of

0.5 ~ 1 Mbps (DL)

Fig. 3 shows the average SINR of the interfered users

regulated by the three interference mitigation algorithms

under differing numbers of base stations deployed (i.e.,

the average SINR of the interfered users was significantly

higher regardless of the number of base station deployed).

The IUP algorithm displayed a superior performance

especially in the case of a large number of BSs (dense BS

deployment) with more severe interference.

Fig. 3. Average SINR of interfered users under differing

Fig. 4 shows the average SINR of regular users by IUP

algorithm was also maintained above a specific level,



numbers

of base stations

meaning that the transmission quality of the other regular

users were not sacrificed to enhance the average SINR of

the interfered users. In terms of the SINR of the regular

user, despite the inclination of the IUP algorithm to

increase the SINR of the interfered user, it was also

careful not to diminish the SINR of other regular user to

values lower than the minimum of the current SINR level

during the power adjustment process, and improved the

allocation fairness through 𝜔𝑖,𝑗


. Therefore, the

transmission quality for regular user was not reduced

while protecting the interfered user.

Fig. 4. Average SINR of regular users under differing number of

small cells

Fig. 5. Data rate CDF of interfered users

Increasing the SINR and allocating more radio

resources to the interfered users resulted in a more stable

signal quality for interfered users and a higher data rate.

As shown in Fig. 5, 56% of the interfered users’ data

rates were increased by the IUP algorithm to 250 Kbps or

above, whereas the other two algorithms achieved only

40%. Nor did the IUP algorithm sacrifice the

transmission quality of the other regular users while

maintaining the interfered users’ data rates. As shown in

Fig. 6, 67% of regular users maintained a data rate of 250

Kbps and above, a result similar to those of the other two

algorithms. The reason was because before the resource

allocation prioritization mechanism was initiated, the

proposed method did not reduce the SINR of regular

users to a level lower than that of the current regulation,

meaning it did not sacrifice the transmission quality of

the other regular users to enhance the transmission

efficiency of the interfered users.

Fig. 6. Data rate CDF of regular users

V. CONCLUSION

To address the interference problem of small cells, this

study proposed an IUP algorithm, using interfered user

determination, cell grouping to dynamically adjust the

transmission power of base stations and radio resource

allocation for improved performance. The effects of

interference on users can be mitigated to improve signal

quality without affecting the connection quality for other

regular users. The simulation results demonstrated that

compared with the other methods, the proposed method

resulted in both a higher SINR and data rate for interfered

users. The IUP algorithm effectively improved the signal

quality of the interfered users to facilitate a stable

transmission, and simultaneously maintained the fairness

and transmission efficiency of other regular users.

REFERENCES

[1] J. Zhang and G. D. L. Roche, Femtocells: Technologies

and Deployment, United Kingdom: John Wiley & Sons Ltd,

2010.

[2] 3GPP TS 32.500, “Telecommunication management; Self-

Organizing Networks (SON); Concepts and requirements

(Release 11),” 2011.

[3] 3GPP TS 28.627, “Telecommunication management; Self-

Organizing Networks (SON) Policy Network Resource

Model (NRM) Integration Reference Point (IRP);

Requirements, (Release 13),” 2015.

[4] Z. Jun and J. G. Andrews, “Adaptive spatial intercell

interference cancellation in multicell wireless networks,”

IEEE Journal on Selected Areas in Communications, vol.

28, pp. 1455-1468, Dec. 2010.

[5] H. C. Lee, D. C. Oh, and Y. H. Lee, “Mitigation of inter-

femtocell interference with adaptive fractional frequency

reuse,” in Proc. IEEE International Conference on

Communication, May 2010, pp. 1-5.

[6] M. Peng, C. Wang, J. Li, H. Xiang, and V. Lau, “Recent

advances in underlay heterogeneous networks: Interference

control resource allocation and self-organization,” IEEE

Commun. Surveys Tuts., vol. 17, no. 2, pp. 700-729, 2015.

[7] K. I. Pedersen, “Enhanced inter-cell interference

coordination in co-channel multilayer LTE-Advanced



Networks,” IEEE Wireless Commun. Mag., vol. 20, no. 3,

pp. 120-27, June 2013.

[8] H. C. Lee, D. C. Oh, and Y. H. Lee, “Mitigation of inter-

femtocell interference with adaptive fractional frequency

reuse," in Proc. IEEE International Conference on

Communication, May 2010, pp. 1-5.

[9] T. Li, Z. Feng, W. Li, Z. Jing, and T. A. Gulliver, “Graph

coloring based spectrum allocation for femtocell downlink

interference mitigation,” in Proc. IEEE Wireless

Communication Networking Conference, Mar. 2011, pp.

1248-1252.

[10] S. Uygungelen, G. Auer, and Z. Bharucha, “Graph-based

dynamic frequency reuse in femtocell networks,” in Proc.

IEEE Vehicular Technology Conference, May 2011, pp. 1-

6.

[11] L. Zou, B. Liu, and C. Chen, “Bayesian game based power

adjustment scheme for inter-WBAN interference

mitigation,” in Proc. IEEE Global Communications

Conference, 2014, pp. 240–245.

[12] Y. Zhang and Q. Zhao, “Channel selection with dynamic

users: A game-theoretic approach,” in Proc. WCSP IEEE,

2014.

[13] V. Chandrasekhar, J. G. Andrews, Z. Shen, T.

Muharemovic, and A. Gatherer, “Distributed power

adjustment in femtocell-underlay cellular networks,” in

Proc. IEEE GLOBECOM, 2009, pp. 1-6.

[14] I. Ahmad, Z. Kaleem, and K. H. Chang, “QoS priority

based femtocell user power adjustment for interference

mitigation in 3GPP LTE-A HetNet,” J-KICS, vol. 39A, no.

2, pp. 61-74, Feb. 2014.

[15] Z. Kaleem, B. Hui, and K. H. Chang, “QoS priority-based

dynamic frequency band allocation algorithm for load

balancing and interference avoidance in 3GPP LTE

HetNet,” EURASIP J. Wireless Commun. Netw., vol. 2014,

pp. 1-18, Nov. 2014.

[16] Y. Zhang, Y. Xu, Q. Wu, et al., “Optimal opportunistic

spectrum access with heterogeneous and unknown channel

dynamics in cognitive radio network,” KSII Transactions

on Internet and Information Systems, vol. 8, no. 6, pp.

2675-2690, 2014.

[17] H. Kpojime and G. Safdar, “Interference mitigation in

cognitive radio based femtocells,” IEEE Commun. Surveys

Tuts., vol. 17, no. 3, pp. 1511-1534, 2015.

[18] L. Narayanan, “Channel assignment and graph

multicoloring,” in Handbook of Wireless Networks and

Mobile Computing, Wiley, 2002, pp. 71–94.

[19] 3GPP TR 36.814, “Further advancements for E-UTRA–

Physical layer aspects,” v1.0.1, 2009.

Chih-Hsiang Ho received his B.S. and

M.S. degrees in aeronautical engineering

and applied mechanics from the Tamkang

University and National Taiwan

University, Taiwan, in 1996 and 1998,

respectively. Since 2000, he has been

with Institute for Information Institute

(III), Taiwan. Currently, he is a section

manager of III and working towards his Ph.D. degree in the

Department of Electrical Engineering at National Taiwan

University. His research interests include self-organized

network algorithms in HetNets, hybrid home networking and

broadband wireless communication such as LTE, WiMAX.

Li-Sheng Chen received the M.S. degree

in Electronic Engineering from National

I-Lan University, I-Lan, Taiwan. He is a

software engineer of Institute for

Information Institute (III) and working

toward his Ph.D. degree in the

Department of Electrical Engineering at

National Taiwan University. His research

interests include the self-organized network, wireless

communication and computer networking.

Wei-Ho Chung received received the

B.S. and M.S. degrees in elctrical

engineering from National Taiwan

University, Taipei, Taiwan, in 2000 and

2002, respectively, and the Ph.D. degree

in electrical engineering from the

University of California, Los Angeles, in

2009. From 2002 to 2005, he was with

ChungHwa Telecommunications

Company. In 2008, he was involved on CDMA systems with

Qualcomm, Inc., San Diego, CA. Since 2010, he has been an

Assistant Research Fellow in Academia Sinica, Taiwan, and

was promoted to the rank of an Associate Research Fellow in

2014. He holds an adjunct associate professor positions at the

Department of Electrical Engineering, National Taiwan

University, and the Department of Communication Engineering,

National Taipei University. He leads the Wireless

Communications Laboratory, Research Center for Information

Technology Innovation, Academia Sinica. His research interests

include communications, signal processing, and networks.

Sy-Yen Kuo received the B.S. degree in

electrical engineering from National

Taiwan University, Taipei, Taiwan, in

1979, the M.S. degree in electrical and

computer engineering from the

University of California, Santa Barbara,

CA, USA, in 1982, and the Ph.D. degree

in computer science from theUniversity

of Illinois at Urbana-Champaign, Champaign, IL, USA, in 1987.

From 1982 to 1984, he was an Engineer with Fairchild

Semiconductor, Sunnyvale, CA, USA, and Silvar-Lisco, Menlo

Park, CA, USA. From 1988 to 1991, he was a Faculty Member

with the Department of Electrical and Computer Engineering,

University of Arizona. He was the Chairman in the Department

of Electrical Engineering, NTU, from 2001 to 2004. He took a

leave from NTU and served as a Chair Professor and Dean of

the College of Electrical Engineering and Computer Science,

National Taiwan University of Science and Technology, from

2006 to 2009. He was the Dean of the College of Electrical

Engineering and Computer Science, NTU, from 2012 to 2015.

He is a Distinguished Professor with the Department of

Electrical Engineering, NTU. His current research interests

include dependable systems and networks, mobile computing,

cloud computing, and quantum computing and communications.

He also received the Best Paper Award in the International

Symposium on Software Reliability Engineering in 1996, and

the IEEE/ACM Design Automation Scholarship in 1990 and

1991, respectively.



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Interfered Users Protection Algorithm for Self-Organizing ... · Interfered Users Protection Algorithm for Self-Organizing Networks . Chih-Hsiang Ho 1, Li-Sheng Chen , Wei-Ho Chung2,