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: *[email protected]. [email protected] [email protected]
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