2012 7th International ICST Conference on Communications and Networking in China (CHINACOM)

An Energy-efficient Macro-micro Hierarchical

Structure with Resource Allocation in OFDMA

Cellular Systems

Xin Chen*,Zhiyong Fengt and Dong Yang+

Key Laboratory of Universal Wireless Communications, Ministry of Education

Beijing University of P osts and Telecommunications, Beijing, P.R.China, 100876

Email: *chenxin06122@gmail.com. tfengzy@bupt.edu.cn .+yangdong.bupt@gmail.com

Abstract-Macro-micro networks, as typical hierarchical net­works, are advanced in increasing the network capacity as weD as in decreasing the network energy consumption during peak hours. However, the fluctuation of traffic load over space and time brings challenges and opportunities in energy saving. In this paper, we develop both resource optimization schemes and hierarchical cell structure switchings, which are rarely considered together, to lower the energy consumption. We first proposed a radio resource aUocation scheme to minimize energy consumption while satisfying given rate requirements in a single cell. Further­more, switching between different hierarchical cell structures, which can be accomplished by adjusting the coverage areas of base stations and switching off the micro stations, is considered to adapt the changing traffic load. Simulation results show that our structure can meet the transmission rate requirements and simultaneously decrease the energy consumption significantly.

I. INTRODUCTION

Prompt by the development of smart phones, the demand

of users to be provided with any service anywhere at anytime

keeps surging. Between year-end 2009 and year-end 2010,

wireless data traffic in the U.S. has more than doubled,

growing from 107.8 billion MB in the last half of 2009 to

more 226.5 billion MB in the last half of 2010 [1]. According

to [2], Information and Communication Technologies (ICT)

is estimated to account for a fraction of the world's energy

consumption ranging from 2% to 10%. From both economic

and environment aspects, energy saving approaches gain a lot

of attention [3], [4]. Especially, cellular networks are expected

to be energy-efficient on the promise that services for large

quantity of users with high quality can be provided.

Microcells are commonly developed to add network capaci­

ty to deal with the increasing data traffic as well as improve the

cell-edge data rate. Many research about resource allocation

focus on spectrum efficiency or area spectral efficiency while

neglects energy efficiency [5]. The energy consumption of

a certain base station is closely related to the transmission

power. And parameters of base stations for both macrocell

and microcell can be optimized to improve the energy effi­

ciency [7] by decreasing the transmission power. We propose

a radio resource allocation algorithm to reduce the energy

consumption after meeting the transmission rate requirement.

Moreover, energy is also consumed by functions such as power

amplification and site cooling even when there is no traffic.

The development of microcells introduces additional energy

consumption when there is less traffic. Fortunately, a Hierar­

chical Cell Structure (RCS) [6] can be constructed to facilitate

the process of reducing overall energy consumption. In this

paper, we proposed an energy-efficient RCS for Orthogonal

Frequency Division Multiplexing Access (OFDMA) cellular

system to make energy consumption scale with traffic load.

OFDMA is increasingly popular with booming developmen­

t of broadband wireless communication systems especially

Long Term Evolution (LTE) systems due to its various ad­

vantages [8]. In such systems, a macrocell is usually overlaid

on several microcells, and interference canceling is one of

the hot spot issues. By applying OFDM technical, intra-cell

interference in overlaid area can be canceled. We employ

the hierarchical structure with resource allocation to OFDMA

cellular network aiming at providing attractive user experience

without consuming so much energy.

The rest of this paper is organized as follows. Section

IT illustrated the hierarchical structure in OFDMA cellular

system. Section III provides analytical models and optimality

study of microcell switch. Section IV studies the resource

allocation scheme when microcells are switched off. We eval­

uate the proposed scheme on both communication and energy

saving performance, and the simulation results are presented

in Section IV. The paper is concluded in Section V.

II. SY STEM MODEL

In this paper, we study a two-tier cellular network. The

first tier and the second tier are macrocells and micro cells

respectively. We use the term region to refer to an equally

sized hexagonal area of side length L throughout the paper. In

each region, there is a central macro base station surrounded

by six micro base stations in the middle of the region edge as

depicted in Fig.l. The coverage area of both macro and micro

base stations can be configured by adjusting the maximum

transmission power. Additionally, micro base stations can be

switched on and off. All the adjustment and switches are on

the principle of not forming coverage holes.

519 978-1-4673-2699-5/12/$31.00 © 2012 IEEE

/7/\ / \ / \ / \ / \ / \ / \ / \ / ... \

I Macro \ I base station \ / \

L

Micro base station

(

Fig. 1. A region and the two-tier cellular network

A. Propagation model

Commonly, signal propagation in a radio mobile environ­

ment is related to three causes: determined path loss related to

propagation distance, random slow fading related to shadowing

effect, and random fast fading related to multipath effect. It is

well known that path loss is proportional to the propagation

distance, G P L ex: d-a, a is the path loss exponent. We

model the fading effect using random variable G f . The signal

propagation model capturing both path loss and fading effects

is formulated as:

G=GpLGf =K ·d-a .Gf (1)

where K is a constant to further adapt the model. While this

model is suitable for theoretical analyze, we use the modified

COST231 Hata propagation model for urban area [14] in the

simulation. This practical model depends on carrier frequency

and deployment scenario (e.g. line of sight condition, shad­

owing deviations). Moreover, not only mobile station antenna

height but also base station antenna height is considered in

this model. This model suits the two-tier cellular network

well, because there is a significant difference in the height of

base station between macro and micro cells. The distribution

followed by G f is also given in this model.

B. Power consumption model

To analyze the energy efficiency, the energy consumption

of each base station has to be evaluated. The total energy con­

sumption consists of two parts. The first part of energy, known

as circuit power, is denoted as pc and exists whenever the base

station is switched on. This part of energy consumption is a

constant [10] [11].

Obviously, the other part is transmission power which de­

pends on the propagation distance as well as the transmission

rate. Take Additive White Gaussian Noise(AWGN)channel as

an example, the transmission power pt to achieve data rate

within bandwidth W given by Shannon formula is:

pt(R, d) =(eR/W - I)NoWjG =(eR/W - I)NoW . da jK· Gf (2)

The total energy consumption of macro base station and

micro base station can be expressed separately as follows:

p mac =aP;"ac + P�ac Pmic =j3P;"ic + P�ic (3)

where a and j3 are the energy coefficients of macro base station

and micro base station. The typical value of them as well as

P�ac and P�ic can be found in [10].

III. NETWORK OP TIMIZATION

In this section, we study how the energy consumption for

a region is impacted by the resource allocation scheme (i.e.

channel allocation and power allocation) as well as the deploy­

ment factor (i.e. cell size) in the two-tier cellular network.

A. Resource allocation

Energy efficiency is defined as the ratio of transmission

rate to total energy consumption. However, is it necessary

to provide users with the highest available transmission rate?

Improve energy-efficiency only may waste energy in getting

unnecessary improvement of transmission rate. Considering

the circuit power keeps unchanged, the energy-efficient opti­

mal problem turns to be a total transmission power minimize

problem under transmission constraint. For a single cell with

M sub carrier to support N users (M;:::N) , We assume that a

user can only access one subcarrier and a subcarrier can be

allocated to at most one user.The optimization problem can be

formulated as :

N M

min L L Pm,n . Pm,n n=l m=l

M

s.t. L Rm,n . Pm,n ;::: 7/Jn "in E {I, 2, ... , N} m=l

M

L Pm,n = I "in E {I, 2, ... , N} m=l

N

L Pm,n:SI "imE{I, 2, ... , M}, Pm,nE{0, I} (4) n=l

where,

(5)

Where r

is the signal-to-noise ratio gap indicating how far

the system is operating from capacity. The difference between

theoretical system and practical system may be due to errors

occurs at bottom layer. According to [13], r

has a simple

relationship with the required bit error rate(BER)

r =

_ In(5BER) 1.5

(6)

We solve this optimization problem based on Lagrangian

Dual Decomposition (LDD). The Lagrangian function can be

520

formed for (4) as:

2(P,p,J.L) N M N M

= L L P m,n . P m,n - L J.Ln( L R m,n' P m,n - '¢n) n=l m=l n=l m=l

(7)

For any feasible subcarrier allocation P , we first consider

the problem of power distribution. Hence, the optimal solution

for (4) can be equivalently solved by solving

maxinf 2(P, p, J.L ) I-' P

So the optimal power distribution follows equation:

n _ rNoB(:!I!:a ) Lmn - -- 2 B - 1 , G m,n

(8)

(9)

After we have calculated all the power distribution Pm,n,

our task is to assign different subcarrier to individual users,

making sure that the sum of power is minimum. That is to get

the optimal P m,n. A greedy algorithm is to assign the user unassigned subcar­

rier with lowest power in order so as to reduce the sum power.

This method is simple, easy to understand, but can only get a

suboptimal solution.

The other methods is to transform our problem into a

bipartite graph of the optimal matching problem. The famous

Kuhn-Munkras algorithm can get the optimal solution.

B. Hierarchical cell Structure

As can be deduced from our previous analysis in section II,

power consumption is closely related to propagation distance

which is determined by the random user location. However,the

maximum value of propagation distance can be reduced by

reducing the cell size. The special case is that when the

cell size reduced to zero, the base station is switched off

and a substantial amount of circuit power can be saved as

well. But in the mean time, when the micro base station

is switched off, users in the previous microcell service area

handover to macro base station, resulting in greater pass-loss

and possible degraded received signal power, thus decreasing

system capacity. A close examination of energy consumption

is in demand for accurate evaluation of the three structures.

The total energy consumption within the region can be

calculated by:

Ptotal = P mac + N!fc . P mic (10)

where, N!fc is the number of micro base station.

Next, we assumes that users are randomly distributed in

region A. Ar indicate the area with distance r from the station.

Then, the average transmission power consumption over the

random position of the desired user in a cell can be calculated

as:

n=l

-N rD 27rr rNoBra (:!I!:a ) - i

0 7r D2 KG f 2 B - 1 dr

2 rNoB :!I!:a =N· (- )--(2 B - l)Da a + 2 KGf

(11)

In [9], the impact of cell size on energy efficiency in

cellular networks has been analogized. It has been presented

that reducing cell size may increase the energy efficiency. For

example, one macrocell can be subdivided into seven cells by

deploying micro base stations. The areas covered by the micro base station enjoy higher Signal to Noise Ratio (SNR) due to

favorable path loss conditions as well as propagation distances.

Thus, less power is consumed to get the same SNR value.

Furthermore, micro base stations positioned on the edges of

macrocell could improve cell-edge date rate which is one of

the most important intentions in LTE.

We designed three HeSs for different traffic load shown in

Fig.2. a) shows the most common structure. Each region is

covered by a center macro base station with six surrounded

micro base stations. On average, there are three micro base

stations per region. When there is not much traffic on the

cell-edge, the coverage area of macro base station zooms

out as shown in b). Because the total transmission power

decreases with the cell size, energy consumption of the region

can be reduced. This structure also can be seen as cell

splitting. Additionally, c) shows the coverage structure without

deploying micro base stations.

To avoid the complexity brought by different '¢n, we analyze

the power consumption in achieving equal user performance

within a cell. Taking macrocell as an example, the overall

power consumption can be calculated as follows:

E[P] =pc + aN rD 27rr pr

dr io 7rD2K·r-a·Gf

=pc + aN_2_. � .pr a + 2 K·Gf

(12)

Thus, the total power consumption in one region for the three

structures mentioned above can further be calculated according

to equation 10.

NUE Pa =( P�ac + N !fcP�iC

) + (aN;:'�c + j3 :;iC )Po

R _(PC + NBS pc ) (NUE j3NUE)Po b - mac mic mic + a mac + mic 2a

p _pc + NUE R c - mac a r egion 0

(13)

(14)

(15)

h R 2 La pr' h . .

w ere, 0 = a+2' K

.Gf . IS t e average transrrnsslOn power

supporting single user in the region, and N;;'r;, is the number of

users in all microcells. It is obvious that structure a) consumes

more energy, but supports more users at the cell edge. By

521

a) Overlapping cells b) Subdividing cells

c) Macrocells only

Fig. 2. Zooming structure for cells

solving the inequality Pc < Pb while assuming a = /3, we

can infer that structure b) is more energy consuming when

the number of user in one region goes beyond big:

NUE NBS 2ap�ic (16) r egion> mic' (2a - I)Po

It is reasonable to infer that, despite the number of users

in the region, structure c) is more suitable for low path loss

exponent as well as high circular power to transmit power

ratio. According to the analysis above, the system is expected

to be energy-efficient by switching between structures with the

changing number of users.

IV. PERFORMANCE EVALUATION

The exact analytical value of total power consumption,

averaged over the random positions of the desired users,

requires a vast number of integrations. This integral process

is of high computational complexity. Therefore, we present

the numerical result based on previously mentioned SPPP

user distribution using Monte Carlo Simulations instead. In

this section, numerical results are presented to illustrate the

performance of proposed algorithm and architecture. System

parameters are listed in Table 1. First, we justify the proposed

resource allocation algorithm is energy-efficient while satis­

fying the rate requirement. Secondly, we compare the energy

consumption with and without the structure switch in a certain

time duration.

The power allocation algorithm is based on the premise

that required transmission rate is fulfilled. We apply static

simulation with randomly selected user distribution pattern

within one cell to evaluate the transmission rate of all these

TABLE I SYSTEM PARAMETERS

Parameters Region radius

Surrounded basestations N ;;,rr Carrier frequency

Subchannel bandwidth User antenna height

Macro BS antenna height Propagation model

Energy model coefficient a, f3 Circuit power P;'ac' pc .

Maximum transmit power P mac' P' . Thermal noise power No

Values 500m

6 2.3 GHz 15 KHz

1.5 m 32 m

modified Hata model 30, 20

150W, 60W 44dBm, 33dBm

-174 dBmlHz

users. Fig. 3 depicts the transmission rate provided to the users

in one specific process of power allocation. The total number

of users is 50, which is a constant number. The transmission

rate is normalized by the constant rate constraint. It is fairly

apparent the proposed algorithm meet the rate constraint

demand despite a little fluctuation which is anticipated and

tolerable.

Besides achieving the required transmission rate, the algo­

rithm is expected to be energy-efficient. For each fixed user

number, we applied the simulation with different user location

distribution for 20 times. As shown in Fig.4, the proposed al­

gorithm always outperforms the uniform allocation. Moreover,

the energy-efficiency increases with the number of users in the

cell. This is because, the ratio of transmission power to total

power grows as there are more users in the cell. In other words,

a bigger quotient of total power contributes to the date rate

increase.

Fig. 5 illustrates the power consumption of three different

architectures. Through the comparison, it is obvious Structure

C (macrocells only) boasts lower energy consumption in the

case of light traffic load, however, as the number of service

increases, Structure B (subdividing cells) can be more energy­

saving. This is because there is tradeoff between signal-to­

noise ratio condition and energy when microcell is shut down.

The saved energy might not compensate for the deterioration

in system performance. Therefore, it is recommended, through

our study, to dynamically adjust the cell architecture according

the the traffic load. Microcell enhances the capacity of a

system. It is not recommended to use Structure C in heavy load

scenario since nor does it attain any enhancement in energy

saving, even worse, it may cause degradation of system QoS.

In fact, to cope with this potential problem, we recommend

to switch system architecture from Structure C to Structure

B when the number of users goes above 40 in our simulation

environment. As the number of users in the region keeps gown­

ing, the system architecture has to switched from Structure

B to Structure A, which is the most energy consuming one.

Because the outage rate in the microcell covered area may

become unacceptable when there are more than 210 users in

the region. By choosing proper structure switch points, this

is a energy-saving solution while providing the guaranteed

transmission rate.

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V. CONCLUSION

This paper has proposed a novel macrocell-microcell hierar­

chial structure in which macrocell and microcell jointly under­

take the task of communication. Theoretical analysis and simu­

lation both confirm the energy saving performance under rate­

constant and rate-various constraints scenario. The proposed

channel allocation algorithm and power allocation algorithm

successfully accomplish the task of high-efficient calculation

and simulation. Results indicate a dynamic adjustment and

transformation between the structure can considerably enhance

energy efficiency. Future work may includes the study of both

spatial and temporal distribution of service in order to obtain

an integrated and dynamic view of the issue.

ACKNOW LEDGMENT

This work is supported by National Basic Research Program

(973 Program) of China with NO.2009CB320400.

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1.12 r-.----.----.----.----.----..---..---r==:===il

2.5 .

2 .

1400

� 1200

! 1000 ..

400

200

Fig. 4.

10 15 20 25 User Index

30 35 40 45 50

Fig. 3. Date rate performance

30 Number of Users

� Proposed Allocation ---11--- Uniformed Allocation

35 40 45

Energy-efficiency versus number of users

50

�-.-�-�-�--.-�-�-�.---.-�-. :it'_/_.K

-.I("':...

-.. _ ...... -... -..... -.... -............... -... -.... -.-.. -...... ,,-

o�-�--�--�--�--�--�-� o 50 1 00 150 200 250 300 350

Number of Users in the Region

Fig. 5. Total power consumption versus number of users