Load Balancing in Cellular Network through Cell Breathing

Abstract- In cellular network, teletraffic continues to grow at a proliferating rate. Thus existing networks often encounter undesirable phenomenon like call drop due to overloading of cells. To overcome this problem, this thesis aims to perform load balancing using the technique of cell breathing in a novel way. Unlike previous works on cell breathing, where the radius of the overloaded cell was shrunk, this model proposes cell overlap. Here the coverage area of a lightly loaded neighboring cell who’s AP provides optimum power to the concerned clients, is expanded and overlapped with the heavily loaded cell. This ensures that ongoing calls in the overloaded cell are not dropped and transfers only the extra load to the lightly loaded neighboring cell without handoff. Furthermore, this paper also introduces Genetic Algorithm to optimize relevant parameters like power more efficiently than the conventional optimizing algorithm. Keywords: Load Balancing, Cellular Network, Cell Breathing, Grade of Service, Blocking Probability.

I. Introduction

Wireless networks have been playing an

immensely vital role in developing the telecom

sector and mass connectivity in Bangladesh.

Wireless networks have given a boost to the

telecommunication industry of Bangladesh.

Growth in Bangladesh’s mobile telephone

sector, from a humble beginning in the early-

1990s, has really picked up pace in the past few

years, aided by higher subscriber volumes, lower

tariffs and falling handset prices. BTTB’s

network structure being much dispersed, mobile

phone is creating huge impact especially among

poor people in the village. This advantage is

further enhanced by the tremendous potential for

Wireless Internet in Bangladesh.

However, as the cellular network grew, the

traffic demand began to mount to a level of

being strenuous. Thus a major limitation being

faced is call blockings due to power limitation.

This problem is specially faced in CDMA where

code limitation may occur prior to power

limitation.

Henceforth, we propose an efficient dynamic

load balancing scheme in cellular networks for

managing a tele-traffic hot spot in which channel

demand exceeds a certain threshold.

With the advent and flourishing of wireless and

cellular network, many other novel technologies

followed the trail into our country. Besides,

hyper advanced mobile sets and equipments like

Bluetooth, a remarkable trend started on the

experimentation of new algorithm for traffic

handling. As such, an optimizing tool that we

want to introduce for the first time in cellular

traffic load balancing is Genetic Algorithm.

II. Motivation

On a global perspective, telecommunication is

the most evolving and most prospective arena.

Improvements in telecommunication have given

us new horizons of hopes and dreams. Today,

one can talk to somebody even on the farthest

and most remote corner of the globe through the

virtues of telecommunication. It is possible to

even communicate with astronauts in outer

space and no wonder we are dreaming to soon

come in contact with our extraterrestrial

brothers!

Yet, perhaps the biggest contribution of

telecommunication has perhaps been to bring us

humans closer to one another. In doing so, one

of the greatest contributors has been cellular

phone technology.

Thus, we tried to contribute, to our limitations,

to the development of the cellular network.

Cellular technology not only connects people, it

is also playing a colossal role in boosting the

economy of Bangladesh. With the increasing

popularity of cellular phones and arrival of giant

phone companies, cellular traffic continued to

grow alongside the ever increasing demand for

better and newer customer services. The

proliferation of lightweight hand-held devices

with built in high-speed Wi-Fi network cards

and the significant benefit of any-where any-

time Internet access has spurred the deployment

of wireless “hot-spot” networks.

The mapping between clients and the APs that

service them is a critical determinant of system

performance and resource usage. An AP can get

seriously overloaded even when several nearby

APs are lightly loaded.

To achieve efficient resource usage without

requiring changes to client software, we propose

the use of cell breathing technique in a novel

way called cell overlap.

To accomplish this task, we were motivated to

experiment using an optimization algorithm,

called Genetic Algorithm. This has never been

used in load balancing mechanisms, but the

concept of GA is so inspired from nature,

especially from the concept of evolution and

survival of the fittest, that we found it worth

implementing in cellular load balancing.

III. Objectives

In our proposed cell breathing technique, the

mobile switching centre (MSC) performs the

pre-calculation as outlined:

Before assigning a call to the base

station or access point (AP) of a cell, the

MSC will check if the capacity of the

cell is exceeded, i.e if it is getting

overloaded.

In case of overloading, the received

power of the client (to whom the call is

directed) decreases below the threshold.

As such, the MSC searches which

neighboring AP transmits optimum

power to this client and has free load i.e.

its current load is less than its maximum

capacity.

Once such an AP is found, its coverage

area is expanded to serve the client of

the neighboring AP and MSC assigns

the client to this new AP.

Thus the overloading call is not dropped

and the grade of service is improved.

Unlike previous works on cell breathing,

where the radius of the overloaded cell

was shrunk, our model expands and

overlaps the coverage area of a

neighboring cell whose AP provides

optimum power to the concerned clients.

This ensures that ongoing calls in the

overloaded cell are not dropped and

transfers the extra load without handoff.

We have applied this technique to an

arbitrary cluster of 10 cells.

Then we have provided a comparative

analysis to show how the grade of

service improves after applying cell

breathing.

In optimizing power with respect to

distance, we have implemented Genetic

Algorithm and tried to provide a

comparison that it provides better

optimization of the concerned

parameters.

IV. Problem Formulation

Our target is to find the minimum acceptable

power for better reception that will be received

by the client. Then we determine the nearest cell

which can provide the same received power. We

increase the transmitted power of the target AP.

Thus the area of the targeted AP extends. If any

cell is overloaded, i.e. if the client demand is

greater than the capacity of an AP then this

process will take place.

To formulate the problem, the notations and

assumptions used are as follows:

Notations:

• Pt: Transmitted Power

• Pr: Received Power

• C: Capacity of an each AP

• α: Attenuation factor

• a: Gain factor

• d: distance between AP and client

• D:Clients demand

• n: Number of clients an AP can handle

at a time

• N: Number of clients

Assumptions:

1) The initial transmission powers to

all AP’s are equal.

2) The received power from the first

AP can be measured.

3) We assumed a threshold power level

below which transmission is not

possible.

The overall procedure applied in our cell

breathing technique is outlined below:

First we calculate the distance between the Host

AP and the client. [1]We can find it from the

general Received Power equation.

Pr(i,j)=a*Pt/d(i,j)α……(1)

Figure 1: Flowchart of cell breathing procedure

Here, “a” is a constant that usually denotes the

channel gain or gain factor of the cell. Pt denotes

the transmitted power, whereas Pt(i,j) implies the

transmitted power between ith cell and jth client.

Equivalently Pr(i,j) implies the received power

between them, d(i,j) is the corresponding

distance and, α is the attenuation factor of the

medium. It is obvious that this equation is

applicable for both free space propagation and

two ray ground reflection model. The various

path loss elements are considered within the

attenuation factor of the medium. As our main

goal is to deal with the power, we are omitting

the detail discussion about the different path

losses.

[2]Wireless propagation model does not always

follow the above equation. In case of obstruction

this equation varies. But by introducing virtual

distance, we can estimate the actual wireless

propagation by following the above equation.

AP’s can collect the measurement of transmitted

power and the received power and then

approximate the actual wireless propagation by

finding d′(i, j) (virtual distance), α′ (virtual

attenuation factor), and a′ that fit the model

Pr(i, j) =a′ Pt/d′(i, j)α′ ……(2)

where Pr(i, j) and Pi are from the measurement.

Then we apply our power assignment to the

virtual distances and virtual attenuation factor.

Finding the distance:

We will start our algorithm if and only if the cell

is heavily loaded. To define or to identify the

cell is over loaded or not, we can have a

parameter, called AP’s capacity. If the number

of calls is greater than the AP’s capacity then

those call will be dropped. So to make those

calls successful we have to balance the cells

load. To do this, we first compare the number of

calls connected with the AP’s capacity. For our

help, we assume that an AP’s capacity can be

defined as the number of clients served

successfully at a specific time. When the number

of clients exceeds the capacity call drop occurs.

Then we should initiate our cell breathing

process.

Figure 2: Cell Breathing

We first assume unique transmission power at

every AP. [3] Normally for cellular transmission

the power is between 0dbm to 20dbm. For our

calculation we consider the power of each AP

primarily 20 dbm or 0.1 W. Now, when a call is

generated and directed to the targeted mobile

station, that MS received the call with certain

received power. We can find the distance

between the AP and the client or MS by using

equation 1. Then we go for the calculation or

finding the distance between different AP’s and

different clients. We find which AP is the

nearest AP for that certain client or a group of

clients. After finding the minimum distance

from the neighboring cells now we have to

assign the appropriate transmission power to

serve the client.

As, if the main AP was not overloaded that

power will be the best for reception as it was the

nearest AP, so to allocate the client to the next

sell we can make this receive power as standard

power for reception and convert the transmission

power of the neighboring cell as after covering

the distance the client’s receive power are equal

to that receive power.

Now, it is always better to change the power as

less as we can. So, we calculate the minimum

acceptable power as well using the above

equation. And determine the location of that AP

simultaneously. After that if we increase the

transmitted power to the desired level, than

automatically, the client comes under the

coverage area of that AP. Thus the cell breathing

procedure can occur.

In our proposed technique of overlapping cell

breathing, we do not change the transmission

power of the hosting AP, only the neighboring

AP’s area is changed.

When there are two same powers from two

different AP, one from the host AP, another

from the closest neighboring cell with minimum

load, the call at the host usually drops because of

shortage of capacity, then the call is

automatically directed to the closest lightly

loaded AP, whose transmission area is

overlapped with the overloaded AP. Thus only

the extra load is transferred without interrupting

ongoing calls in the overloaded AP. Also, In this

case handoff is less frequent than the non-

overlapping cell breathing technique. So, this

one could be the better solution.

Problem Formulation using Genetic

Algorithm (GA):

We first apply our previous technique to find the

overloaded cells and their distances among the

uncongested APs. When the distance and

received power have been found, we can then

search for the transmission power for a

particular distance and receive power.

The next step is to define the objective function

which and fitness function that help determining

the minimum distance between the targeted

client and the neighboring APs and provides the

optimal solution for the new transmission power

that increases the cell radius. This

[5]The fitness function quantifies the optimality

of a solution (that is, a chromosome) in a genetic

algorithm so that that particular chromosome

may be ranked against all the other

chromosomes. Optimal chromosomes are

allowed to breed and mix their datasets by any

of several techniques, producing a new

generation that will (hopefully) be even better.

The fitness is decided according to the nature of

the problem. In the case of a minimization

problem, the lowest numerical value of the

associated objective function is declared as the

fittest individual. In many cases, the fitness

value corresponds to the number of offspring

that an individual can expect to produce in the

next generation.

Selection is the next stage of a genetic algorithm

in which individual genomes are chosen from a

population for later breeding (recombination or

crossover).

Generational process of the GA is repeated until

a termination condition has been reached, which

in our case is completion of power calculation of

all the APs in a specific network.

V. Results

Assumptions:

To measure the efficiency of our algorithm we

made the following assumptions.

• The Capacity of each AP is 5 mbps

• Client demand is uniform and that is 1

mbps

• There are 10 APs randomly distributed

in the experiment set

• Attenuation factor is 4 and gain constant

is 0.9

• Radius of each AP is 2 km or 2000m

• The range of power should between 0

dbm -20 dbm or 0.001W-0.1W

Some arbitrary load was applied to 10 APs.

Then the overloaded APs were detected and

their calls rearranged by changing the AP’s

transmission radius.

In this way, we ran the algorithm with different

offered loads and calculated the Grade of service

(GOS) of individual AP and the overall system

before and after applying our proposed cell

breathing technique.GOS is chosen as the

performance indicator since lower GOS implies

lower blocking probability and hence better

network quality.

Offered Load=30

AP Offered Load

AP's Capacity

Dropped Calls

Grade of Service

Offered Load2

Dropped Calls2

Grade of Service2

1 2 5 0 0.00 2 0 0.00

2 3 5 0 0.00 3 0 0.00

3 4 5 0 0.00 4 0 0.00

4 2 5 0 0.00 4 0 0.00

5 7 5 2 28.57 5 0 0.00

6 2 5 0 0.00 2 0 0.00

7 2 5 0 0.00 2 0 0.00

8 2 5 0 0.00 2 0 0.00

9 2 5 0 0.00 2 0 0.00

10 4 5 0 0.00 4 0 0.00

Total 30 50 2 6.67 30 0 0.00

Table 1: After Cell breathing the GOS decreases from 6.67% to 0%

First, we assumed total load on the network is 30

whereas it can handle 50 clients at a time. But all

30 clients were not evenly distributed in every

AP. Hence, the 5th AP became overloaded.

After applying cell breathing the overloaded cell

was load balanced to 5 units of load. Thus the

system’s GOS decreased from 6.67% to 0%.

Similarly, we assumed different offered loads

and ran our proposed algorithm to justify our

model. The results are tabulated below:

Offered Load=37

AP Offered Load

AP's Capacity

Dropped Calls

Grade of Service

Offered Load2

Dropped Calls2

Grade of Service2

1 2 5 0 0.00 4 0 0

2 7 5 2 28.57 5 0 0

3 6 5 1 16.67 5 0 0

4 4 5 0 0.00 5 0 0

5 2 5 0 0.00 2 0 0

6 4 5 0 0.00 4 0 0

7 4 5 0 0.00 4 0 0

8 1 5 0 0.00 1 0 0

9 5 5 0 0.00 5 0 0

10 2 5 0 0.00 2 0 0

Total 37 50 3 8.11 37 0 0

Table 2: After Cell breathing the GOS decreases from 8.11% to 0%

Offered Load=50

AP Offered

Load AP's

Capacity Dropped

Calls

Grade of

Service Offered Load2

Dropped Calls2

Grade of

Service2

1 4 5 0 0.00 5 0 0.00

2 8 5 3 37.50 5 0 0.00

3 5 5 0 0.00 5 0 0.00

4 7 5 2 28.57 5 0 0.00

5 6 5 1 16.67 5 0 0.00

6 5 5 0 0.00 5 0 0.00

7 2 5 0 0.00 5 0 0.00

8 6 5 1 16.67 5 0 0.00

9 1 5 0 0.00 5 0 0.00

10 6 5 1 16.67 5 0 0.00

Total 50 50 8 16.00 50 0 0.00

Table 3: After Cell breathing the GOS decreases from 16.00% to 0%

Offered Load=55

AP Offered

Load AP's

Capacity Dropped

Calls

Grade of

Service Offered Load2

Dropped Calls2

Grade of

Service2

1 5 5 0 0.00 5 0 0.00

2 8 5 3 37.50 5 0 0.00

3 7 5 2 28.57 6 1 16.67

4 8 5 3 37.50 5 0 0.00

5 2 5 0 0.00 5 0 0.00

6 5 5 0 0.00 5 0 0.00

7 1 5 0 0.00 5 0 0.00

8 7 5 2 28.57 7 2 28.57

9 5 5 0 0.00 5 0 0.00

10 7 5 2 28.57 7 2 28.57

Total 55 50 12 21.82 55 5 9.09

Table 4: After Cell breathing the GOS decreases from 21.82% to 9.09%.

In the above table, since offered load>maximum

capacity, GOS is not achieved to be zero, yet it’s

significantly improved by our cell breathing

algorithm.

Summarizing the above results in the following

graph, it is observed that the GOS decreases

significantly by applying the proposed cell

breathing technique, justifying the significance

of our proposed model.

Figure 7: Grade of Service before and

after cell breathing. GA Implementation Results

For minimization of transmission power we used

two different methods: General method and

Genetic algorithm. For different runs of GA, we

find the minimized transmission power as

follows:

For d=2.11 and Pr=0.001961386

Figure 3: Best Transmission power for d=2.11 and Pr=0.001961386

For d = 2.12 and Pr = 0.001696211

Figure 4: Best Transmission power for 2.12 and Pr = 0.001696211

For d=2.22 and Pr = 0.001542238

Figure 5: Best Transmission power for d=2.22 and Pr = 0.001542238 Comparison between Different Runs:

Figure 6: Best Transmission Power for 5 different runs

Comparative Study between General Process

and GA:

Run Distance

Received Power using GA

Transmitted Power Using GA

Received Power using general process

Transmitted Power using general process

1 2.3 0.001045013 0.074734 0.001258479 0.09

2 2.15 0.00174661 0.089155 0.001959071 0.1

3 2.11 0.001961386 0.091145 0.00215194 0.1

4 2.12 0.001696211 0.080708 0.00205963 0.098

5 2.22 0.001542248 0.092401 0.001669082 0.1

Table 5: Transmission Power for GA and conventional method

From the above table we can clearly observe that

the transmission power selected through GA is

lower than the transmission power derived from

general process. One of the main aspects of good

transmission is a minimum power level. So

implementing GA instead of the general process

will be more efficient and cost effective.

Limitations:

One limitation in this model is that it does not

include any tracking system. So we estimated

the distances among the APs and clients

randomly. Without the actual coordinates we

had to run this program for each overloaded

client separately.

Also, we could not geometrically plot the

increased and decreased area of the APs and

therefore, could not show the minimized load

plotting. But we became successful showing

the increase or decrease of distances and the

transmitted power to support the cell breathing

process.

Lastly, we assumed a fixed capacity of each AP

and which in practical scenario varies within a

range.

VI. Conclusion

This paper presents a novel cell breathing

concept in cellular WLAN that performs load

balancing with the aim of improving the Grade

of Service. Unlike previous works on cell

breathing, where the radius of the overloaded

cell was shrunk, our model expands and

overlaps the coverage area of a neighboring cell

whose AP provides optimum power to the

concerned clients. This ensures that ongoing

calls in the overloaded cell are not dropped and

transfers the extra load without handoff.

The transfer procedure from one AP to another

is seamless. This procedure is done by the MSC,

it finds the appropriate AP for better

transmission, and only then the call is assigned

to that AP. Therefore, any probability of a new

call being dropped is pre-calculated by the MSC

and the call transferred to the appropriate AP

(before the call is actually connected) through

this cell breathing technique. This load

balancing technique allows the network to adapt

itself to changing network load conditions in

order to maintain or improve the current

level of service being provided to users.

Results show that the proposed scheme does

indeed improve the level of service received by

users in the form of lower Grade of Service and

improved overall throughput.

Furthermore, we provided rigorous analysis of

the problem and presented two algorithms to

determine the optimal solutions. The first

algorithm minimizes the load of congested

AP(s) in the network by changing the coverage

area of the AP, and the second algorithm also

does the same thing but it uses GA in

minimizing the power. These optimal solutions

are obtained only with the minimal information

which is readily available without any special

assistance from the users or modification of the

standard. We only assume the control on the

transmission power of the AP beacon messages,

which should be possible with simple software

update of APs.

Henceforth, our proposed cell breathing

technique efficiently achieves load balancing by

dynamically reconfiguring transmission power

and cell boundaries. It does not require changes

to the client software or the standard, thereby

making it rapidly deployable. Thus we have

demonstrated the effectiveness of cell breathing,

and showed that it significantly out-performs the

popular fixed power schemes.

VII. Future Possibilities

As discussed earlier in the limitation section we

could not include the tracking system for this

problem. If we can enable a tracking system like

GPS, this procedure can be done with more

accuracy.

Other optimization tools could be experimented

to yield better minimization results and shorter

run time for calculation.

On a broader scale, this proposed model could

be further modified to be implemented in

different types of networks like, Wireless Sensor

Networks, Computer Networks etc. Further

studies and research are needed for this purpose.

This technique has not yet been implemented in

our country. With few modifications, this

proposed load balancing mechanism can serve to

substantially improve the quality of the existing

cellular network, in turn enhancing the overall

communication system.

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