A Distributed Load Balancing Algorithm for LTELTE-A Heterogeneous Networks 2015

A Distributed Load Balancing Algorithm for

LTE/LTE-A Heterogeneous Networks

Diego Castro-Hernandez

Department of Electronic Systems Engineering

University of Regina

Regina, Canada

[email protected]

Raman Paranjape

Department of Electronic Systems Engineering

University of Regina

Regina, Canada

[email protected]

Abstract—In this paper, we propose a distributed load

balancing algorithm with adaptive bias adjustment for

LTE/LTE-A HetNets. We have formulated the problem as a

local sum utility maximization. The distributed algorithm is

capable of fairly distributing the load among base stations

with a minimum level of coordination. The performance of

the algorithm was evaluated with a network topology subject

to the conditions of a real HetNet deployment, in an urban

environment, and applying a realistic traffic distribution

during a period of peak usage. Our simulation results show

that 350% higher data rate can be achieved for users with the

worst 5% rates, and an overall average data rate gain of 23%

can be obtained. Furthermore, the load balancing algorithm

applied in combination with our adaptive bias adjustment

method, provides an additional average gain of 50% in the

rates for the 25th percentile.

Keywords—Adaptive range extension; HetNets; Load

balancing; LTE; LTE-A;

I. INTRODUCTION

During the last few years there has been an increasing interest in the deployment and optimization of LTE/LTE-A heterogeneous networks (HetNets). Such networks typically consist of a set of low power base stations, commonly known as small cells (e.g. microcells, picocells, femtocells), deployed to complement the coverage of high power macrocells by increasing the available capacity and enhancing user experience. HetNets have become an attractive option for network operators to provide high quality service to local areas with significant traffic volume, e.g. hotspots like food court areas in malls or crowded waiting rooms in large airports.

Although HetNet deployments can bring great advantages, there are important challenges to consider when it comes to optimizing their performance. One key challenge is the increasing complexity of network planning, in particular as the density of small cells per macrocell increases.

Furthermore, another relevant challenge is related to the balancing of the load between base stations. HetNets are an excellent option for increasing capacity and decreasing the congestion levels of macro cells during peak periods. However, careful coordination between base stations is necessary to achieve a fair distribution of the traffic. User

experience can be significantly affected when receiving service from an overloaded base station, even in areas with high SINR (signal to interference plus noise ratio) conditions. Current cell selection mechanisms, e.g. a user is served by the base station that provides the strongest received signal or SINR, tend to ignore a critical aspect: the load of the base stations [1]. These mechanisms provide suboptimal cell associations with unbalanced load distributions, leading to congestion in some cells and under-utilization in others. Sharing the load among base stations (small cells and macro cells), can greatly improve the overall network throughput.

Significant efforts have been made to propose effective load balancing algorithms based on traffic transfer strategies. The use of adaptive cell specific offsets, or range extension bias (REB), to dynamically control the coverage areas of small cells has been extensively studied [2]-[7]. Unfortunately, the optimal values of REB are typically calculated based on network-wide analysis, with bias values specified in a per-tier basis using centralized algorithms with slow adaptation. In recent years, authors in [8] and [9] have proposed approaching the load balancing issue as a convex optimization problem. A utility function is formulated, typically in terms of the user’s achievable data rate. The optimal cell association that maximizes the network-wide sum of the utility is found. Unique user association, power control and load sharing are constraints included in the optimization problem. Approximating the optimal network-wide user association usually involves the implementation of complex iterative algorithms, which require significant coordination between base stations and a substantial exchange of signaling messages between base stations and users (UEs). Furthermore, evaluation of these load balancing algorithms is usually carried out in simple environments and without considering realistic traffic patterns present in real urban scenarios.

In this paper, we propose a distributed load balancing algorithm based on traffic transfer with minimal signaling exchange between eNBs. Given a current suboptimal user association, each base station can solve locally a load-aware utility maximization problem. Such problem is solved based on the information of the current eNB’s load level, resource scheduling and SINR conditions of its associated users. By solving the utility maximization problem locally, an

8th International Wireless Distributed Networks Workshop on Cooperative and Heterogeneous Cellular Networks 2015

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overloaded base station can determine which users are negatively impacting its sum of the utility, those users are then candidates to be transferred to other base stations with spare capacity via load-aware handover procedures. Our approach was evaluated under realistic conditions of an urban environment given a real traffic map. Based on system level simulations, the overall average data rate gain reached 23% with a significant rate gain for users in the 5th percentile, close to 350%. Similar gains were reported in [8], however our approach is significantly less complex. Furthermore, we combine our proposed load balancing algorithm with an adaptive method to adjust the REB values for small cells. As a result, an additional average gain of 50% in the average data rate for low rate users was achieved. With our bias adjustment method each small cell can adapt its own bias without creating coverage holes or reaching congestion; as opposed to REB algorithms proposed in [2]-[7] that calculate a unique value of the bias for all small cells in the same layer regardless of their location or local load conditions.

II. SYSTEM MODEL

In this paper we have considered the downlink of a two-tier LTE HetNet deployed in a real environment. UEs are distributed in the area of interest according to a traffic map derived from statistics collected by a network operator and prior knowledge of the users’ distribution. Small cells are deployed spatially in such a way that they provide coverage to known hotspot areas. In our case of study most of these hotspots correspond to indoor locations, e.g. food court areas, with high density of users during peak hours.

For our analysis only downlink data transmission is considered, a study of the implications of our approach taking into account the uplink is left for future work. Furthermore, we have considered a HetNet deployment with dedicated spectrum for each tier, i.e. macrocells and small cells use different frequency bands. Even though only intra-layer interference is considered, our approach can be extended to the case of co-channel deployments where an inter-cell interference coordination technique (eICIC) is applied, e.g. Almost Blank Subframes (ABS). Additionally, for the macrocell layer the inter-cell interference from neighboring macrocells is assumed to be negligible.

We denote by � the set of all base stations, and � the set of all users. User association is defined as in [8], the

indicator variable �� describes the association of the UE j

to eNB i and it is defined as:

�� = � 1, j� UE is associated to i� eNB 0, otherwise (1)

The set of users associated with the �� eNB is denoted as:

�� = !" |�� = 1, " ∈ �} (2)

The instantaneous downlink data rate offered by the ith eNB to the jth user, during subframe k is defined as:

&��(() = *��+ (() ∙ -��(() (3)

Where *��+ (() is the bandwidth in Hz scheduled

(offered) for downlink transmissions to the jth UE. The value -��(() corresponds to the normalized rate of the UE in

b/s/Hz and is calculated as a function of the .�/0�� as:

-�� = 1(.�/0��) (4)

Where .�/0�� is the ratio of the received power from

the ith eNB and the total power of the received interference from neighbouring cells belonging to the same tier plus noise. The function 1(∙) has been traditionally determined by the Shannon Hartley theorem, as shown in [1]-[3], [8]

and [9]. However, in real networks -�� depends on the value

of the Channel Quality Indicator (CQI) that is periodically

reported by the UE. The higher the measured .�/0�� , the

higher the value of CQI; which means that the UE is capable of decoding received data with a higher modulation order and coding rate. Furthermore, the spectral efficiency can also be improved if the eNB and the UE support MIMO capabilities like spatial multiplexing. The actual mapping between the measured .�/0�� and the reported CQI value

depends on UE capabilities and have been left by the 3GPP as a vendor specific implementation. In this paper, our simulator uses the mapping derived in [10] for a 10% block error rate (BLER).

The long-term rate of user j is calculated as the average of the instantaneous rate during a certain number of subframes 2:

0�� = ∑ 456(7)89:;< (5)

A. Load of eNBs

In a LTE/LTE-A network, all active UEs that connect to an eNB have to share the available bandwidth. Each one of the UEs demands a specific quality of service depending on the running application. Low bandwidth demand may correspond to voice calls or downloading small files, whereas bandwidth intensive applications like HD video streaming or gaming requires higher demand of resources. The task of the scheduler is to distribute the available bandwidth such that the demanded quality of service of each user is satisfied, while keeping a sense of fairness.

The load of an eNB can be quantified in terms of the demanded load and the offered load. A demanded load index and offered load index are defined as [2]:

=�> = ∑ ?@ 56A6∈B5C5 (6)

=�+ = ∑ ?@ 56D6∈B5C5 (7)

Where *@��+ and *@��> are the average offered and

demanded bandwidth of the jth user respectively during K subframes, and E� corresponds to the total bandwidth of the

ith eNB. The offered load index =�+ reaches its maximum value of one when the totality of the bandwidth have been scheduled for downlink transmissions. On the other hand,

the demanded load index =�> can take values larger than 1, e.g. when the total demand of bandwidth is higher than the available bandwidth.

A base station whose offered load index is below unity is assumed to be underloaded since a portion of its


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bandwidth has not been scheduled, hence it has spare capacity. However, when the offered load index approaches unity and the demanded load index is above unity, then the base station is considered to be overloaded, since it does not have enough resources to satisfy its current demand. The sum throughput of a base station can be greatly impacted by the overload condition, resulting in a degraded quality of service. A situation that is undesirable for network operators.

III. PROBLEM FORMULATION

The goal of an effective load balancing algorithm is to balance the overall load in the network between eNBs. Typically, network-wide optimization techniques have been proposed to solve the load balancing problem [8], [9]. Based on the long-term rate of the jth user, a utility function F�G0��H is calculated and a network-wide optimization

problem is formulated to find the optimal user association:

IJ = arg maxI ∑ ∑ �� ∙ F�G0��H�∈N�∈O (8)

s.t. ∑ ��∈O = 1, ∀" ∈ �

�� ∈ !0,1}, ∀" ∈ � , ∀� ∈ �

With I = Q��| " ∈ �, � ∈ � R being the network-wide

user association. The objective is to find the optimal

distribution of UEs IJ among all the eNBs subject to the constraint that any UE has to be associated to only one eNB. The optimal cell association provides the maximum sum of the utilities of all the users in the network.

As it has been mentioned in [8] and [9], the optimization defined in (8) is a combinatorial problem whose computation is intractable for real size networks. The problem becomes more challenging due to the coupled relationship between the load of each eNB and the user association, since the long term rate 0�� depends on how

loaded the ith eNB is. Different authors have proposed relaxations to reduce the complexity of the optimization problem, e.g. in [8] a fractional user association scheme allows a user to be associated with more than one base station. Additionally, significant efforts have been made to solve (8) in a distributed manner [9]. Unfortunately, many of the proposed methods require high coordination between base stations and a significant amount of exchange of messages between users and eNBs. Our load balancing algorithm is presented in the next section.

IV. DISTRIBUTED LOAD BALANCING ALGORITHM

Instead of solving the network-wide optimization problem stated in (8), we simplify the problem by letting each base station determine its own local optimal user association, imposing a constraint based on the desired level of demanded load. The simplified optimization problem can be expressed in terms of a local indicator variable �S� as:

IJ T = arg maxIT ∑ �S� ∙ F�G0��H�∈N5 (9)

s.t. ∑ �S� ∙ *@��>�∈N5 < X E� �S� ∈ !0,1}, ∀" ∈ �� ; X ≥ 1

With IT = Q�S�| " ∈ �� R. The local optimal user

association [JT can be calculated by each base station

considering all the UEs currently associated to it. By solving (9), each base station can select from the set of associated UEs a subset of users that maximizes its own sum of the

utility function. The optimal local user association [JT must also satisfy a constraint related to the desired level of demanded load, i.e. the total demanded load of the selected users must not exceed the value XE�, where the parameter X can take a value higher or equal than 1. A value of X closer to unity will provide a stricter selection of UEs. Such parameter can be setup by the network operator.

Similarly as in [8] and [9], we have selected a logarithmic utility function. As it was stated in [8], the logarithmic function has the advantage that it yields high utility values if more resources are provided to users with low rates (cell-edge users) as opposed to providing the same amount of resources to users already with good rates. This encourages cell-edge user associated with overloaded eNBs to be transferred to underloaded base stations where they can receive more resources, hence improving the balance of the load among eNBs. In our case we define the logarithmic utility function as:

F�G0�� , *@��+ H = log ]0��*@��+

^ (10)

The quantity _56?@ 56D represents the average normalized long-

term rate for the jth UE in b/s/Hz. We approximate the solution of the problem formulated

in (9) with an approach based on greedy heuristics. We first start by calculating the values of the utility function for each user associated to a base station. Then, the users are sorted in descending order based on the corresponding value of the utility function. Starting with the user with highest value of

utility, its indicator function �S� is set to “1” if the resulting

cumulative demanded load is below XE�. The rest of the users are evaluated sequentially, and their indicator function �S� will be set to “1” as long as the total cumulative

demanded load constraint is satisfied. Otherwise, the indicator function will take the value of “0”. The pseudocode of this process is presented in Algorithm 1.

Based on the set IJT, the base station can determine which users are candidates to be transferred to underloaded neighboring base stations. We define the sets `� and a� as: `� = !"|�S� = 1, �S� ∈ IbT} (11)

a� = !"|�S� = 0, �S� ∈ IbT} (12)

The set � contains the subset of users that will continue to be associated with the ith base station after IJT has been obtained from algorithm 1. The set a� contains the subset of users that base station i will attempt to transfer to other underloaded eNBs via handover operation.

After the sets � and a� are known, it is time to actively transfer the excess load to base stations with spare capacity. For each user in a� , the base station should submit a handover request to another base station with spare capacity, and whose RSRP (Reference Signal Receive Power) and SINR measured by the user satisfy the cell selection criteria defined by the operator. The values of RSRP and SINR of neighboring cells are obtained from measurements reports submitted by the users in a� .


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Evidently, the overloaded base station must know the current load conditions of its neighboring cells so that it can select target eNBs that are underloaded. This information can be obtained from a centralized unit in charge of periodically broadcasting load indicators (e.g. the offered and demanded load indexes) of all the base stations. For our proposed algorithm, this is the only piece of information that base stations need to share among each other. The exchange of this information is a functionality expected to be part of Self-Optimizing Networks (SON) [11].

It is expected that an overloaded base station might not be able to transfer all the users in a� . This could happen when the selected target eNBs might not have sufficient spare capacity to handle all the handover requests or when there are no suitable neighboring cells to submit the handover request (low RSRP and SINR values from neighboring cells). Therefore, those UEs whose handover was unsuccessful, should be reassociated to the source base station, i.e. the value of �S� corresponding to those users

should be set to 1 and the sets � and a� should be updated accordingly. As a consequence of this limitation in real networks, the resulting user association of an overloaded base station might not completely satisfy the demanded load constraint in (9).

V. ADAPTIVE BIAS ADJUSTMENT

Overloaded base stations can further offload more users to neighboring base stations by carefully adjusting their cell specific offset or range extension bias (REB). Such offset is used to encourage (or discourage) users to associate to small cells with low transmission power but lower path losses compared to a distant high power macrocell. Typically, UEs select the base station that satisfies (13) [2]:

c = arg max� G0.0e�� + g�H , ∀ � ∈ � (13)

Where g� is the current value of the REB of the ith eNB, with g� = 0 for macrocells and g� ≥ 0 for small cells.

The higher the value of REB, the larger the coverage area of the small cell. For an overloaded small cell, it is desirable to reduce the value of the bias such that its coverage area is reduced and less users will tend to select the small cell during their cell selection/reselection procedure. On the other hand, if a small cell is underloaded, then it is desired to expand its coverage area by increasing

the value of its bias to attract more users. The adjustment of the bias should be done carefully in order to avoid coverage holes (setting a value of the bias too low) or increasing cell edge interference levels (setting a value of the bias too high).

We propose a simple scheme to adjust the value of the bias for overloaded small cells based on the load balancing algorithm presented previously. Our approach consists in the evaluation of the values of RSRP reported by users belonging to the set a� of each overloaded cell. Those values of RSRP were reported by the UEs that were successfully handed over to underloaded base stations.

Our approach is based on the following observation: if a user j in a� is located in the range extension area of an overloaded eNB i, then it is possible to reduce the value of the bias, such that other UEs located nearby will be encouraged to select the underloaded base station to which the user j was handed over.

Consider a user " ∈ a� that was transferred to a target eNB �∗. User j is located in the range extension area of the overloaded base station i if the following condition is satisfied: 0.0e�∗� + g�∗ > 0.0e�� (14)

Equation (14) indicates that user j would select base

station i only if the bias g� is added to the measured 0.0e�� ,

otherwise it would select base station �∗. This means that the bias can be reduced accordingly so that other users located nearby will also select base station �∗ instead of the overloaded eNB i. A tentative value for g� can be calculated such that the edge of the range extension area is moved closer to the position of user j by applying (15). j� = maxG0.0e�∗� + g�∗ − 0.0e�� , 0H (15)

The tentative value of the bias j� can be calculated for

all users " ∈ a� located in the range extension area of base station i. The new bias value for base station i is given by: g�lmn = meanQj�p j� < g� , " ∈ a�} (16)

Additionally, if after decreasing the value of the REB a small cell remains underloaded for a certain number of subframes, then its bias should be increased to a default value previously set by the operator. This will allow the coverage area of underloaded cells to expand to their original size and attract more users.

VI. PERFORMANCE EVALUATION

To evaluate the performance of the proposed load balancing algorithm, we consider a two-tier HetNet deployed in a university campus area. The selected campus corresponds to the University of Regina in Saskatchewan, Canada. A 3D model of the environment, that includes buildings and vegetation, was generated with a resolution of 1m. A traffic map based on network statistics and knowledge of the users’ distribution was elaborated to determine the location of hotspots during peak hours. A total of 100 users where distributed in the area of interest based on the traffic distribution presented in fig. 1.

The environment of the University of Regina campus can be classified as urban with flat terrain and irregular locations, sizes and orientations of buildings. The average building elevation is 17m with a total of 18 buildings. The

Algorithm 1: Get [JT Require: Set q = QF�p" ∈ ��}

Require: Set r> = Q*@��> p" ∈ ��}

Require: Parameter X

Output: [Js 1: tuvwxtut_z{wt ← 0

2: Sort(q) in descending order

2: for x = 1: |��| 3: F~l ← �u�F~(q)

4: if tuvwxtut_z{wt + *@�l> < XE� 5: �Sl = 1

6: tuvwxtut_z{wt = tuvwxtut_z{wt + *@�l>

7: else

8: �Sl = 0 9: end if

10: end for

11: [JT ← Q�S��R


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area covered by this study has a rectangular shape with dimensions 600 m by 700 m.

Fig. 1. Traffic map and location of base stations

One macro cell with three sectors is located on the rooftop of one of the buildings (cells 1, 2 and 3), with a total height of 36m. Six small cells (cells 4 to 9) are deployed outdoors and are equipped with directional antennas mounted on light posts with 10m height. Additional parameters of the system are provided in table I.

A site-specific propagation path loss model based on the Geometrical Theory of Diffraction and geometrical optics, proposed and validated in [12], was applied to model the propagation environment. We assumed that UEs initially associate with the base station that satisfies (13) with a demanded rate that is randomly selected between 0.5 and 10 Mbps. This demanded data rate corresponds to the rate at which the eNB is buffering data for the UE. The value of K was set to 10. This means that every 10 subframes (one frame), each base station calculates the average rate offered to its UEs and executes the proposed load balancing algorithm. The higher the value of K the slower the adaptation of the load balancing.

For this study, the parameter α was arbitrarily set to 1.1. This means that overloaded base stations will attempt to reduce their demanded load to no more than 110% of their available bandwidth.

TABLE I. SIMULATION PARAMETERS

Parameter Value

Bandwidth 15 MHz

Carrier freq. : Macrocells/ Small cells 2.1 GHz / 2.6 GHz

Network topology 1 macrocell / 3 sectors

6 small cells

Transmit power: macrocell / small

cells

47 dBm / 30 dBm

Horizontal / vertical beam Macro: 65°/10°

Small cells: 75°/40°

Antenna model: macrocell

small cell

UNNPX306R3

S31003U_2600

Antenna pattern: 3D (from manufacturer)

Default REB: 9 dB

Traffic model Full buffer

Gaussian noise �� -174 dBm/Hz

Scheduler Proportional fair

Simulation time 100 ms

A. Distribution of users

As it was mentioned before, in our network topology six small cells have been located to provide coverage to hotspots. Therefore, it is expected that during periods of peak usage some small cells will likely become overloaded. The initial distribution of users between underloaded and overloaded cells is presented in fig.2. It can be observed that 90% of the users are associated with base stations that are overloaded (in our case: cells 4, 5, 8 and 9). The remaining 10% is associated with underloaded cells (cells: 1, 2, 3, 6 and 7). After applying the load balancing algorithm, the portion of users associated with the overloaded cells decreased to 68%, which means that 22% of users were transferred to underloaded cells.

Fig. 2. Distribution of users between overloaded and underloaded eNBs

B. Fairness of load balancing

In order to evaluate the performance of the load balancing algorithm, we calculated the well-known Jain’s fairness index �(=). Where = corresponds to the set of offered load indexes (or demanded load indexes) of all

eNBs. The fairness index has range of �1 /m�� , 1� , where

/m�� is the total number of base stations. A fairness index equal to unity, indicates that the base stations share the load equally (“fair” distribution of the load). The index is calculated according to (17).

�(�) = (∑ �55∈� )�� ∑ (�5)�5∈� (17)

The fairness index of the offered load and demanded load are shown in table II. When no attempt to balance the load among eNBs is made, the fairness indexes of the demanded and offered load have poor values of 0.62 and 0.52 respectively. Our load balancing algorithm was able to distribute the load fairly to achieve values of fairness indexes around 0.9 and 0.92 for the offered and demanded load respectively.

TABLE II. FAIRNESS INDEXES OF DEMANDED AND OFFERED LOAD

Fairness index Without LB With LB

Demanded load 0.62 0.9

Offered load 0.52 0.92

C. Data rate gain evaluation

An important aspect of an effective load balancing algorithm is its capability to provide gains in the overall network data rate. The overall average data rate for all users


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connected to the network was 23% higher when the load was balanced.

In order to quantify the gain in average data rate for cell-edge users (5th percentile), the cumulative distribution function (CDF) of the normalized average data rate was calculated.

The CDF for the offloaded cells is presented in fig. 3. The users with the lowest 5% normalized rates experienced an improvement of up to 350% in their average rate, whereas users with already good rates (50th percentile) experienced an improvement of only 7%. This means that users with low rates received greater benefit after their base station was offloaded and users that already had good rates were able to maintain them.

A similar result can be observed from the CDF of the overall normalized rate as shown in fig. 4. Higher gains in rates are provided to users with lower rates. This is due to the fact that users with low rates are transferred to base stations with spare capacity, where they are assigned more resources and consequently achieving higher rates. Once again, users with good rates only experienced marginal gains in their rate.

The performance of the load balancing algorithm combined with the adaptive bias adjustment shows an additional average gain of 50% for low data rate users (25th percentile), compared to the case when only the load balancing algorithm is applied.

Fig. 3. CDF of the normalized data rate of offloaded cells

Fig. 4. CDF overall normalized data rate for all eNBs

VII. CONCLUSIONS

In this paper, we proposed a distributed load balancing algorithm with adaptive bias adjustment for LTE/LTE-A HetNets. We have formulated the problem as a local maximization of the sum utility for each base station. The distributed algorithm is capable of fairly distributing the load among base stations, requires minimum level of coordination and negligible number of signaling messages between users and base station. Our simulation results show that a significant gain, around 23%, in the overall average data rate can be achieved. Furthermore, the average data rate for the low 5% of users is substantially improved with a gain around 350%. The application of our load balancing algorithm combined with the proposed adaptive bias adjustment method was able to provide an additional average gain of 50% for the 25th percentile.

ACKNOWLEDGMENTS

The authors would like to thank SaskTel Inc. for collaborating in the development of this research.

REFERENCES

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[8] Y. Qiaoyang et al., "User Association for Load Balancing in Heterogeneous Cellular Networks," IEEE Trans. Wireless Commun., vol.12, no.6, pp.2706,2716, June 2013

[9] K. Shen, W. Yu, "Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks," IEEE Journal on Selected Areas in Communications, vol.32, no.6, pp.1100,1113, June 2014

[10] M. Kawser et al., “Downlink SNR to CQI Mapping for Different Multiple Antenna Techniques in LTE,” Intl. Journal of Information and Electronics Engineering, Vol. 2, No. 5, Sept. 2012

[11] 3GPP, TS 36.902 “Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Self-Configuring and Self-Optimizing Network (SON) Use Cases and Solutions”, May, 2011

[12] D. Castro-Hernandez and R. Paranjape, “Estimation of coverage areas in microcells,” IEEE Intl. Conf. on Advanced Networks and Telecommunications Systems, pp.1,6, Dec. 2013.


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A Distributed Load Balancing Algorithm for LTELTE-A Heterogeneous Networks 2015