A Dynamic Load Balancing Algorithm in Heterogeneous Network

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A Dynamic Load Balancing Algorithm in Heterogeneous Network

Zhixiong Ding, Xingjun Wang and Wenming Yang

Shenzhen Key Lab of Information Security and Digital Content Protection Technology Division of Information Science and Technology

Graduate School at Shenzhen Tsinghua University Shenzhen, China

[email protected], [email protected], [email protected]

Abstract — Heterogeneous network (HetNet) is one of the key architectures in 3rd Generation Partnership Projects (3GPP), in which lower-power but more flexible Pico base station (PBS) is appended. The original homogeneous base station stays the same and is called Macro base station (MBS) in HetNet. The traditional network admission and handover is performed by the reference signal received power (RSRP), which is positively related to transmission power. Consequently, the lower-power PBS will be unattractive to user equipment (UE), resulting in feeble performance enhancement of HetNet and imbalance load between MBS and PBS. This paper investigates a dynamic load balancing algorithm to solve this load-imbalance problem that deteriorate 3GPP LTE network system performance. The algorithm is based on the heterogeneous network access function (HNAF) which is deduced from network fairness analysis according to load status of different bases. Meanwhile, the algorithm is realized through traffic transfer scheme, and is designed as a suboptimal but practical solution. Simulation results show that the proposed algorithm gains a better performance on throughput, access success rate and the interlayer fairness compared with existing scheme.

Keywords - HetNet; 3GPP LTE; load balancing; heterogeneous network access function

I. INTRODUCTION

With the improvement of internet and wireless communication technology, there is obvious demand growth for high data rate services, including multimedia services, high quality of service (QoS) and abundant network capacity [1]. Significant growth also exists in the amount and diversity of existing and forthcoming connecting devices including mobile phone, tablet, laptop and intelligent terminal. Meanwhile, device that is centrally distributed in some hot spots like bazaar, square, marketplace and large inner office space results in inhomogeneity of network resource distribution. In order to solve this problem, the HetNet architecture is introduced, and also designed to increase network coverage in obstacle areas and original network-edge areas. HetNet is adopted in LTE radio access network (RAN) architecture. And due to this performance of HetNet, the next generation communication system will also accept it as one basic network architecture [2].

The transmission power of PBS (around 30dBm) is much lower than MBS (around 46dBm), and UE will connect the base station (BS) with max reference signal received power (Max-RSRP) according to current communication standard [3]. Max-RSRP scheme leads to high priority for UEs to

connect with MBS, resulting in imbalance load between MBS and PBS. Besides, the load-imbalance problem is much more severe because PBS is usually deployed to hot spots with more UEs. In this context, there have been fruitful researches on load balancing, which can be mainly divided into two aspects: one is on load balancing technology, while the other is on the definition of network load index for UEs to make decisions, on which this paper focuses. Moreover, load balancing technology can be divided into channel borrowing scheme (CBS) and traffic transfer scheme (TTS): CBS mainly refers to the notion that heavily loaded cell (HLC) borrow unused channel from lightly loaded cell (LLC) or preempt the shared channel [4]; TTS refers to the notion that UEs are transferred from HLC to LLC with reasonable allocation of network resources between MBS and PBS. TTS is more frequently used in this stage, and is also the basis of t research.

This paper propose a HetNet access function based on multiple load factors in BS, and accordingly implement a suboptimal access control algorithm. We firstly design a communication simulation model as a foundation for the analysis of algorithm, then analyze the network fairness indices from which the HetNet access function is derived, and finally propose a suboptimal but practical algorithm after considering actual implementation. In the following simulation, the proposed algorithm is compared with the same kinds of algorithm to obtain the corresponding conclusions.

The rest of this paper is organized as follows: Section II introduces related work in load balancing in HetNet. Section III-A presents the analytical model for load balancing, section III-B describes the network fairness index and deduce the heterogeneous network access function, and section III-C introduces the dynamic load balancing algorithm and corresponding flow chart. Section VI evaluates the performance of the proposed algorithm via simulation at system level, and compares with congener algorithms and section V draws conclusions.

II. RELATED WORK

The multi-layer and multi-cell heterogeneous network is faced with many challenges. We should consider load extent of PBS under coverage of MBS when measuring cell load in multi-layer cell. Subsequently, the network load metric needs to be distinguished according to the base station type in order to achieve load balancing between MBS and PBS and in MBS and PBS. The author in [6] proposed a dynamic

cell-Corresponding author: Dr. Xingjun Wang

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size control (cell breathing) scheme that is an adaptive transmission power control scheme via which heavily loaded cell decreases its coverage and lightly loaded cells expands its coverage by adjusting transmission power. However, adjusting transmission power easily results in network coverage hole, which is hard to optimize [7]. Thus, the notion of cell range expansion (CRE) has been introduced [8]. In CRE a biased received power value, which is also called range expansion bias (REB), is added to the RSRP of PBS. For example, once the REB is assigned the value of 10dB, UEs will access MBS in condition that the RSRP of MBS is higher than the RSRP of PBS 10dB.

However, it is difficult for the fixed REB value to adapt to dynamic heterogeneous networks [9]. It is also hard to understand why UEs should choose PBS with a lower RSRP unconditionally. [10] proposed a cell specific offset to dynamically adjust REB. Authors in [11] used a global convex optimization module to analyze load balancing problem, working on optimal REB. But these methods are centralized implementations to achieve optimal performance in overall network, with a relatively high complexity and difficulty to practically implement. Our algorithm is based on the local implementation to comprise the computational complexity and optimal performance as an instead.

III. PROPOSED METHOD

A. System model

A two-tier heterogeneous network consisting of high-power MBS and low-high-power PBS is considered as shown in Fig. 1.

Figure 1. Illustration of MBS-PBS HetNet

The number of MBS is

N

_M , the number of PBS is

N

_P, the total number of overall base stations is

N

_total, and the number of UEs is

N

_U . Assume that UEs are evenly distributed in the cell which is the basic scenario in the Network. The distance between MBS and MBS maintains relatively certain and further, while the distribution of PBS is stochastic. It is more likely to appear that the PBS is set up in the network edge or some hot spots, which also can be seen from Fig. 1. Note that the PBS close to the MBS is more susceptible to high-power MBS, resulting in UE under the PBS coverage choosing to access to the MBS, and thus causing overload in MBS and underload in PBS. Meanwhile

UEs in the network edge tend to access PBS because of the use of CRE technology, which causes overload in PBS. The assumption of these actual scenes shows the necessity for us to take consideration for load balancing problem in HetNet.

Assume that UEs access the cell with best RSRP signals by default. The Max-RSRP scheme can be described as

( )

arg max

u Max RSRP i i S

Cell

₋

u

RSRP

∈

=

(1)

where

S

_u is the set of base stations UE u can receive RSRP signals. As in [9-11], the notion REB is put forward that the RSRP of PBS is biased up with a certain REB value, and the RSRP of MBS remains unchanged. Thus the RSRP-REB can be defined as follows.

( )

arg max

Fixed REB i i S

MCell

₋

u

RSRP

∈

=

(2)

( )

arg max{

}

Fixed REB i i S

PCell

₋

u

RSRP

REB

∈

=

+

(3)

In the system model, the channel state information is periodically sent back to its serving cell, and the interference from the same cell is ignored. So the received signal-to-noise-plus-noise ratio (SINR) of user

u

from cell

i

2 , , 2 , 0 ,

SINR

i u i u i j u j j S j i

g p

g

p

N

∈ ≠

=

+

¦

(4)

where

p

_i is the transmit power of BS

i

,

g

_{i u}_, is the channel gain between BS

i

and UE

u

. Thus the RSRP that UE

u

receive from BS

i

can be calculated as

RSRP

_i

=

g p

_{i u}2_, _i. 0

N

represents the noise power that is usually regarded as additive white Gaussian noise. The channel gain

g

_{i u}_, is calculated by the formulation

g

_{i u}_,

=

h h

_S _{Li u}_, .

h

_S and

h

_{Li u}_,

represent the small scale fading factor that is modeled as a Gaussian random variable with zero mean and unit variance, and large scale fading factor that is denoted as

/10

10

PL

L

h

=

− , where

PL

is the path loss according

[12].

3GPP LTE system is based on the data service which is mainly evaluated by the overall throughput. So the Shannon correction formulation [13] is used to map the SINR to throughput , min , 2 , min , max , max max 0 ( ) log (1 ) ( ) ( ) i u i u i u i u i u SINR SINR

C SINR SINR SINR SINR

SINR SINR C

α

< ° =® + < < ° _> ¯ (5) where

α

is attenuation coefficient in downlink channel (We mainly analyze the downlink environment),

SINR

_min is the minimum target

SINR

for Adaptive Modulation and Coding (AMC),

SINR

_max is the max

SINR

when AMC obtains maximum throughput, and

C

_max is the maximum throughput.

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B. HetNet Access function

Let’s re-consider parameters when UEs access to the BS. Instead of the only parameter of

RSRP

, the actual load condition including the number of UEs, throughput in the serving cell and

RSRP

together with the fairness should be taken into consideration. First of all, there is need to apply a suitable fairness evaluation system in HetNet to assess the overall level of network equilibrium. In terms of fairness index, Jain’s fairness index [14] is widely used

2 1 2 1

(

)

N i i N i i

X

N

X

ξ

= =

=

¦

(6)

where the value of

ξ

is in

[

1 ,1]

N

. The value of

ξ

closer

to 1, the more balanced of the network load. When the value

is

1 N

, the network load is the most imbalanced that all of

network resources are centralized on one BS cell.

The Jain’s fairness index ignores the differences of MBS and PBS. There is no doubt that MBS and PBS differ in network capacity, the total UE number and other network resources. Consequently, HetNet is regarded as a two-tier network, and tiered fairness index [14] is used here,

β

_P is used to measure the average layer-2 network load relative to the average level of the layer-1 network load. The

β

_P is defined as follows. , 1 , 1

1

M P N M i i M P N P i i P

X

N

X

N

β

= =

=

¦

(7)

Then the fairness index is revised with tiered index.

2 , , 1 0 2 2 , , 1 1

(

)

(

) )

M P M P N N M i P P i i i N N total M i P P i i i

X

N

X

β

ξ

β

= = = =

+

=

+

¦

(8)

where the value of

ξ

is also in

[

1 ,1]

total

N

, and the

expression can reflect the fairness among two layer.

To achieve load balancing among two-tiered HetNet, we need to find a suitable index when describing the degree of resource utilization. After considering several major factors affecting network load, this paper defines the load index as

1 2

(

,

)

i i i

X

=

w U w Z

JJG

(9) 2 2 2 2 1 2

||

i i i i

X

=

JJG

X

=

w U

+

w Z

(10)

where

JJG

X

_i represents network load vector, and the

X

_i is absolute value of

JJG

X

_i,

U

_i is current served UE ratio of BS

i

to the max served UEs number , and

Z

_i is real-time traffic (throughput) ratio to the max throughput of BS

i

.

w w

₁

,

₂ is the weight of

U

_i and

Z

_i, and

w

₁

+

w

₂

=

1

.

w w

₁

,

₂are assigned as the same value (0.5) in this paper. The reason why the load index is defined as a vector is that different factors influencing the network load can be accurately considered. There is also flexible scalability that other factors will be added into this load index vector like physical resource blocks utilization and multimedia service type.

With the summary of above work, the HetNet access function (HNAF) is defined as

1 2 2

sgn(

_i

,

_cur

) sim(

_i

,

_cur

)

i i cur

X X

G

μ

RSRP

ξ

= −

+

JJG JJJJG

(11) 2

||

|| ||

||

sgn(

,

)

(||

||

||)

i cur i cur i cur

X

X X

X

−

=

−

JJG

JJJJG

JJG JJJJG

JJG

JJJJG

(12)

(

,

)

sim(

,

)

||

|| * ||

||

i cur i cur i cur

X X

X

=

JJG JJJJG

JJG

JJJJG

(13) where

JJG

X

_i represents load condition of BS

i

,

JJJJG

X

_cur

represents average load condition of overall network,

sgn(

JJG JJJJG

X X

_i

,

_cur

)

is used to get the signal of the difference between

X

_i and

X

_cur ,and

sim(

JJG JJJJG

X X

_i

,

_cur

)

is used to characterize the similarity of

JJG

X

_i and

JJJJG

X

_cur . Note that the

sim(

JJG JJJJG

X X

_i

,

_cur

)

is larger, the load gap between

JJG

X

_i and

cur

X

JJJJG

is smaller. To discriminate the load-imbalanced BS, we use the square of

ξ

in the denominator. And RSRP value is added in the function since RSRP is always an important condition for UE to access the network normally.

The HNAF

G

_i is used to decide which cell to access. If

i

G

is relatively large, the BS

i

load is light or the

RSRP

_i

is large and UE tends to access, or vice versa. UE calculates

i

G

when choose to access the

BS

_i, and the global optimum of _ξ is converted to distributed local optimum of

G

_i, with the computational. These centralized schemes in [10], [11] are achieved by brute force search (BFS) with the computational complexity increasing as

(N

2Ntotal

)

U

O

. This

distributed local sub-optimal scheme can be achieved with the complexity increasing as

O

(N N

_U2 _total

)

, which is easier to implement.

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C. Dynamic Load Balancing Algorithm

With summary of the above discussion, a dynamic load balancing algorithm (DLBA) is proposed as shown in Fig. 2 to balance the overall network load in HetNet.

Figure 2. The flow chart of DLBA

In the proceedings of the proposed algorithm, UEs firstly detect RSRP and judge the type of BS in every cell. If there is only MBS or PBS, the difference between two BS types need not to be considered. So UE calculates

G

_i of neighbor MBS and PBS, and correspondingly access the BS with max

i

G

. On the contrary, if there are two types of BSs, UE preferentially choose the MBS with max RSRP, and the RSRP of this MBS is supposed as

RSRP

_i. UEs sort the serving PBS under the coverage of MBS in descending order, and neglect these PBS j that meets the condition

i j

RSRP

>

RSRP

+

REB

, on which the UE will access MBS. For those PBS j meeting the condition

i j

RSRP

≤

RSRP

+

REB

, UEs calculate the HetNet access function

G

_i and connect the BS with the max

G

_i.

Let’s relatively analyze four main state of BS according to RSRP and

G

_i: small RSRP and small

G

_i, small RSRP and large

G

_i, large RSRP and small

G

_i ,and large RSRP

and small

G

_i. There is no doubt that UEs will access the BS with large RSRP and large

G

_i , and ignore the BS with small RSRP and small

G

_i. But the BSs with small RSRP but large

G

_i, and the BS with large RSRP but small

G

_i are hard to distinguish in other conventional algorithm, which is also a key problem in dynamic load balancing. As for the proposed algorithm DLBA, if the BS RSRP is large while with a relatively small

G

_i, the UE will ignore it, and if the BS RSRP is small while with a relatively large

G

_i, the UE will still access. The network load is balanced through these proposed scheme. And the network throughput is promoted because of the increase of UE received SINR.

IV. SIMULATION

A practical scenario is simulated to evaluate the performance of our proposed algorithm. Models and parameters are set with reference to 3GPP standard [3] as shown in Table I.

TABLE I. SYSTEM PARAMETERS FOR SIMULATIONS

Parameter Value

Center frequency 2GHz

Bandwidth 10MHz Number of cells 19 macro-cells, 3 sectors per macro-cell;

4 low power node-cells per cell Inter site (MBS distance) 500m (3GPP Case 1) Macro cell coverage radius 1km

Pico cell coverage radius 150m

Pico cell placement Random as in 3GPP TS 36.814 [12]

MBS Tx power 46dBm

PBS Tx power 30dBm

Shadowing standard deviation Macro: 8dB; Pico: 10dB;

Path loss model

As in 3GPP TS 36.814 [12]:

10

128.1 37.6 log+ d(MBS, d in km)

10

140.7+37.6 log d(PBS, d in km) Minimum SINR threshold -10dB

Maximum SINR for AMC 22dB Measurement interval 40ms

Number of UEs 40 per Macro cell; 10 per Pico cell

Alpha (downlink) 0.6

Maximum throughput 4.4bps/Hz when SINR is larger than 22dB Channel model TU (including fast fading)

Thermal noise -174dBm/Hz REB value 0, 3, 6, 9, 12, 15dB

Four PBS are evenly placed in one MBS, and we regard one MBS and four PBS as a unit of the region for measurement (RFM). Simulation results include network throughput, network fairness and UE access ratio. UEs are placed evenly and stochastically in the Macro and Pico cell, complying with the number in Table I. It is found that UE number in one Macro cell is 30 according to basic scenario. With the regard that the load balancing is a dynamic

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behavior, measuring results during the dynamic behavior is not accurate and it cannot be compared with the results of comparative scheme. As a result, a redundancy time (like 10 minutes) is set to guarantee that the system is stable, which is also a proper strategy to avoid the cold-start problem. The system-level simulation is measured by static snapshot, which is easy to implement. Besides, ten independent simulations with random distribution of UEs are performed to work out the average simulation results.

Figure 3. Ratio of accessing MBS under different REB value

The ratio of accessing MBS in three scheme is shown in Fig. 3. As the value of REB increases, the MBS accessing ratio of Max-RSRP scheme remains unchanged, the one of RSRP-REB decreases monotonously, and the result of DLBA remains relatively stable. It can be concluded that Max-RSRP is not suitable to the HetNet, and simply adding REB value will result the imbalance load when REB value is large with the waste of MBS resources because of the low MBS accessing ratio. And the RSRP-REB is hard to dynamically adapt to the sophisticated and changeable network environment. The DLBA is an adaptive algorithm, and the REB value is not highly decisive factors in the scheme. The result that ratio of accessing MBS and ratio of accessing PBS stays relatively stable validates the load balancing performance of our proposed algorithm.

Figure 4. Fairness index under different REB value

We compare Max-RSRP scheme, RSRP-REB scheme and our proposed scheme DLBA on the factor of fairness index as shown in Fig. 4. And revised fairness index is used here according to Equation (8), which is based on two-tiered

HetNet and therefore can be a proper index to describe the load in HetNet.

ξ

of Max-RSRP remains unchanged and

ξ

of RSRP-REB is equal to the former one when REB is 0, which validate the design that these two scheme is same when REB is 0.

ξ

of RSRP-REB and

ξ

of DLBA get maximum value around REB = 9dB while

ξ

of DLBA is larger and more stable than

ξ

of RSRP-REB. In order to facilitate the following analysis of other factors, we set REB value to 9 dB in the next experiment.

Figure 5. Fairness index under different UE number

Fig. 5 indicates the relationship between the fairness index and UE number that is one of most important factor influencing load. It is obvious that DLBA has a larger fairness index than Max-RSRP scheme and RSRP-REB scheme (REB=9dB) of Max-RSRP is relatively small and decreases slightly when UE number is too large for UE to obtain sufficient resource of BS. The fairness index gap between RSRP-REB and DLBA is enlarged and change of RSRP-REB is more significant. This is because MBS reaches the capacity limit, PBS is of relatively light load at the same time and the PBS can’t transfer traffic from MBS because of scheme as the UE number increases. While the same case will rarely occur in DLBA scheme.

Figure 6. Throughput under different REB value

Fig. 6 shows system throughput in three scheme and local throughput of MBS and PBS in RSRP-REB scheme and DLBA scheme. The reason why the MBS and PBS

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throughput data of Max-RSRP scheme isn’t plotted in the figure is that most throughput is centralized in MBS, which is always relatively unchanged as the REB changes. Throughput of MBS decreases monotonously and the one of PBS increases with REB, because UEs will more inclined to access the PBS under the same condition as REB value increases.

After synthesizing throughput of MBS and throughput of PBS, we get the statistical data of RFM unit. It is found that its throughput increases as the REB increases at first and then decreases correspondingly. Reasons to explain this result can be divided into two parts: UEs will start accessing PBS as the increase of REB at first, leading to an improvement of total throughput; however, as the REB value is relatively large, UEs still access the heavily-loaded PBS which owns a relatively small capacity, while the lightly-loaded but large-capacity PBS is ignored, and thus the throughput decreases in this scene. What’s more, the throughput gap between DLBA and Max-RSRP or RSRP-REB is enlarged, which is due to the reason that MBS and PBS in DLBA scheme are more fully and efficiently used in overall heavy-load situation.

V. CONCLUSION

In this paper, a load balancing problem in 3GPP LTE heterogeneous network is investigated in detail. This paper firstly researches on how to define a proper and reasonable scheme for UEs to fairly access to HetNet in all tiers, so as to enhance the user access fairness of the overall network, and improve network throughput. A HetNet access function with comprehensive consideration for current channel quality and the existing load of neighbor base stations is then proposed. And this paper finally makes a tradeoff between complexity and realizability of the optimum load balancing problem and thus propose a sub-optimal and dynamic load balancing algorithm. Simulation results validate the proposed algorithm that it can improve the balanced load condition, fairness and system throughput among two tiers of HetNet, compared with the Max-RSRP scheme and RSRP-REB scheme.

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