Dynamic Cellular Channel Allocation using...

School of Computer Science Carleton University

Dynamic Cellular Channel Allocation using Intelligent Agents Gayan Abeysundara 100267102 COMP 4905 Honours Project Advisor: Dr. Tony White, School of Computer Science April 2005

Dynamic Cellular Channel Allocation using Intelligent Agents April 2005 COMP 4905 Honours Project

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Abstract

The following paper looks at an approach to dynamic channel allocation in cellular telephone

networks using mobile agents modeled after social insects. Current cellular networks are

extremely large and complex, and usually managed by a centralized system. This centralized

method is prone to problems, and possibly leads to inefficient utilization of network resources.

Using division of labour and task specialization, agents in the network are able to self-organize

and produce intelligent swarm behavior. As a result, a decentralized, robust system emerges –

adapting real-time to network conditions.

This work is being built upon an existing agent-based solution proposed by a previous Honours

student. Simulation results had shown feasibility, but only with further research and development.

The original work was extended to include additional features, higher agent intelligence, and

more realistic network situations.

Numerous simulations of the current project clearly show that an agent-based solution is feasible.

Agents performed very well in all network conditions, increasing the use of limited network

resources. As well, using a dynamic method to control the agent population produces results close

to optimal.


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Table of Contents 1. Introduction ................................................................................................................. 1 1.1 Cellular Telephone Networks............................................................................. 2 2. Swarm Intelligence...................................................................................................... 4 2.1 Swarm Intelligence in Network Resource Allocation ......................................... 5 3. Cellular Network Simulation ........................................................................................ 8 3.1 Java Classes ..................................................................................................... 9 3.2 Problems with Original Simulation ................................................................... 10 3.3 Software Additions and Modifications.............................................................. 12 3.3.1 Random Agent Sweep........................................................................ 13 3.3.2 Call Models ......................................................................................... 14 3.3.3 Simulation Mode ................................................................................. 16 3.3.4 Dynamic Ant Adjustment..................................................................... 17 3.3.5 GUI Modifications ............................................................................... 18 4. Experimentation ........................................................................................................ 20 4.1 Simulation Results ........................................................................................... 20 4.1.1 Downtown Call Model ......................................................................... 21 4.1.2 Centre Hotspot Call Model.................................................................. 24 4.1.3 Random Hotspot Call Model ............................................................... 28 4.2 Observations and Analysis .............................................................................. 31 5. Conclusion ............................................................................................................... 32

5.1 Similar Work Done in Field .............................................................................. 32 5.2 Future Developments ...................................................................................... 33 5.3 Summary ......................................................................................................... 34

References ..................................................................................................................... 35 Appendix A – Simulation Tables ....................................................................................A-1 Appendix B – Contents of Project CD............................................................................B-1


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List of Figures FIGURE 1. Cellular network grid with base station antennas. .........................................................2 FIGURE 2. Network grid with intelligent agents. ..............................................................................5 FIGURE 3. Screenshot of original cellular simulation developed by Lawlor. ...................................8 FIGURE 4. Screenshot of a simulation with a centre hotspot. .......................................................15 FIGURE 5. Screenshot of control panel in modified GUI. ..............................................................18 FIGURE 6. Screenshots of two different views of cellular grid in GUI. ..........................................19 FIGURE 7. Simulation results of Downtown call model using call rate of 3. ..................................21 FIGURE 8. Simulation results of Downtown call model using call rate of 4. ..................................21 FIGURE 9. Simulation results of Downtown call model using call rate of 5. ..................................22 FIGURE 10. Simulation results of Downtown call model using call rate of 6. ................................22 FIGURE 11. Simulation results of Downtown call model using call rate of 7. ................................22 FIGURE 12. Simulation results of Downtown call model using call rate of 8. ................................23 FIGURE 13. Simulation results of Downtown call model using call rate of 9. ................................23 FIGURE 14. Simulation results of Downtown call model using call rate of 10...............................23 FIGURE 15. Simulation results of Centre Hotspot call model using call rate of 3. ........................24 FIGURE 16. Simulation results of Centre Hotspot call model using call rate of 4. ........................25 FIGURE 17. Simulation results of Centre Hotspot call model using call rate of 5. ........................25 FIGURE 18. Simulation results of Centre Hotspot call model using call rate of 6. ........................25 FIGURE 19. Simulation results of Centre Hotspot call model using call rate of 7 .........................26 FIGURE 20. Simulation results of Centre Hotspot call model using call rate of 8. ........................26 FIGURE 21. Simulation results of Centre Hotspot call model using call rate of 9. ........................26 FIGURE 22. Simulation results of Centre Hotspot call model using call rate of 10. ......................27 FIGURE 23. Simulation results of Random Hotspot call model using call rate of 3.......................28 FIGURE 24. Simulation results of Random Hotspot call model using call rate of 4.......................28 FIGURE 25. Simulation results of Random Hotspot call model using call rate of 5.......................28 FIGURE 26. Simulation results of Random Hotspot call model using call rate of 6.......................29


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FIGURE 27. Simulation results of Random Hotspot call model using call rate of 7.......................29 FIGURE 28. Simulation results of Random Hotspot call model using call rate of 8.......................29 FIGURE 29. Simulation results of Random Hotspot call model using call rate of 9.......................30 FIGURE 30. Simulation results of Random Hotspot call model using call rate of 10.....................30

List of Tables TABLE 1. Comparing simulations with corrected channel-stealing algorithm................................12 TABLE 2. Comparing agent sweep algorithms. .............................................................................13


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1. Introduction

The objective of this project is to continue the work done by a previous Honours student, Michael

Lawlor. It proposes a decentralized approach to dynamic channel allocation in cellular telephone

networks. This is accomplished by using mobile agents modeled after social insects such as ants

and bees. Using division of labor and adaptive task allocation, simple interconnected agents self-

organize to exhibit intelligent behavior to produces an overall robust and adaptive system.

The agent-based solution proposed by Lawlor produces better results than a traditional agent-less

system. However, this is only under certain situations, with specific call generation rates and

using a certain number of agents [Lawlor, 2003]. Furthermore, many agents working under a high

network load only produces a less organized system, with excessive channel shuffling (stealing of

frequencies). This project involves building on the work done by Lawlor to produce an improved,

more refined agent-based solution to the problem of dynamically allocating limited cellular

frequencies in a wireless network.

The project will be deemed successful if improvements are made on the pervious agent-based

solution. This may be achieved by showing that agents in the system can affectivity allocate

network resources under all conditions. As well, the addition of agents in to the system should

produce clear and consistent trends in performance. Extensive simulations of the cellular network

will be done to reveal the final results.

A primary goal of the project is the make the system highly adaptive. With varying network

scenarios, the number of agents in the system should be able to adapt to the current condition.

This proposed feature will be implemented in the system to give agents the power of self-

termination and self-duplication. This higher form of agent intelligence will be experimentally

tested to determine feasibility.

The report will begin by discussing the current situation of cellular telephone networks and then

move onto swarm intelligence, and how it could be used in network resource allocation. The main

component of the report will consist of the cellular network simulation software, developed using

Java. The simulation results will then be presented with a comprehensive analysis. And finally

concluding with a look at similar research done in the field and future developments for the

proposed agent-based solution.


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1.1 Cellular Telephone Networks

At the present moment, cellular telephone networks have higher demand and operating

requirements then ever before. They are very large, complex, and still growing at a rapid rate.

They’ve grown so large that no single human can begin to comprehend or manage such a system.

And currently, they are usually only controlled by a single central system, with ongoing human

interaction to keep it stable and resolve problems.

Cellular networks are organized as a grid of cells with a base station located in each one. The

base station consists of an antenna that transmits cellular frequencies and defines the perimeter or

boundary of the cell. The base station is responsible for communicating with each of the cellular-

based user devices within the cell. Because of the limited frequency spectrum allocated to cellular

communication, frequencies have to be used efficiently to utilize the network to its maximum

potential. Cells are allowed to reuse frequencies over the network – as long as the same frequency

is not used in the same cell or two adjacent cells.

FIGURE 1. Cellular network grid with base station antennas.

For a regular cellular telephone call, three frequencies are required: one for outgoing voice,

another for incoming voice, and the last for control communication. The three combined is

referred to as a channel, so therefore one channel is required per call. As such, frequency and

channel will be used interchangeably for the remainder of the report, and will both refer to the

method of communication in a cellular telephone call.

A simplified version of a cellular telephone network consists of new calls, in-progress calls,

blocked calls, and handoffs. New calls refer to a cellular phone requesting an available channel to

use. This is accomplished be contacting the respectful base station who in turn will return an

appropriate frequency from the central system. In-progress calls simply refer to what it states -


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calls that have been successfully connected to the central telephone network and is in the process

of communicating voice or data. Blocked calls refer to a call being blocked and disconnected

from the system for a particular reason. This may occur while a new connection is being

established, and no appropriate frequencies are available. As well, if a call moves out of the

current cell boundaries where the frequency is not strong enough and no new frequencies are

available, the call will be blocked and dropped by the system. If a call does move out of its

current cell boundaries into another cell where it is allocated a new channel, it is referred to as a

handoff. Currently, all the above procedures are handled by one central system. As one could

imagine, the system has to manage a constant heavy workload to execute a continuous flow of

tasks. Such a centralized system is inevitably prone to problems and could lead to a single point

of system failure.

Another important aspect of cellular grids are network hot spots, or simply hotspots. A hotspot

occurs when there is high demand in a particular location of the cellular network grid. This may

be due to many reasons, some predictable and others random and unpredictable. Predictable

events that may trigger a hotspot include such things as a dense population of people during

working hours in a city centre, or people in a large shopping mall during the weekend.

Unpredictable events occur when there is an unforeseen gathering of people, possibly due to a

spectator event, protest, or any other random externality that causes a population spike. As such,

cellular networks should be designed appropriately to handle hotspots, whether predictable or not.


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2. Swarm Intelligence

Swarm intelligence is a field of artificial intelligence where the system is modeled after the

behavior of a particular swarm of animals. This usually consists of modeling a colony of social

insects such as bees or ants, but may also include larger animals such as flocks of birds.

Simulations and models are created to solve particular problems in the technology field. This may

be purely software related, usually taking on the form of optimization algorithms, resource

allocation, or finding optimal routes in a network. But it can also be used in robotics, to have a

group of robots behaving under swarm intelligence. The purpose of using swarm intelligence is to

have a decentralized system that is extremely flexible and robust. This is possible due to the

concept of emergence: interconnected simple elements self organizes and exhibits intelligent

behavior, more robust than a centralized approach [Johnson, 2001].

The principle components are the individual entities or agents in the swarm. Each agent only

follows a simple set of rules that are inherent in the particular species of animal the swarm is

modeled after. They interact with each other and the environment to determine its course of

action. Each agent does not know the entire system or what the entire swarm is doing, leading to

no overall evaluation. However, as a result of the overall swarm behavior from each and every

agent intertwined with each other, the system self-organizes to produce an efficient working

entity [Bonabeau et al., 1999].

There are four components of self-organization: positive feedback, negative feedback,

amplification of fluctuations or randomness, and the reliance on multiple interactions [Bonabeau

et al., 1999]. Positive feedback occurs through recruitment and reinforcement, such as the

recruitment to a food source. Negative feedback is required to counter balance the positive

feedback. Such feedback can occur through competition and exhaustion. Self-organization

requires amplification of fluctuations, or randomness. This important aspect allows for the

discovery of new solutions. The last ingredient of self-organization is the reliance on multiple

interactions. There must be some sort of minimal interaction between individuals, and they

should each be able to make use of the results of their own actions and the actions of others.

Agents may communicate with each other in two ways, either directly or indirectly. Direct

communication between social insects is usually accomplished by releasing a chemical substance

known as pheromones. Pheromones are used by ants for trail-laying and trail-following, to a food


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source for example. The strength of the pheromone concentration will determine how likely

individuals will respond to it. If a strong concentration is established, more ants will respond and

crowd together. To indirectly transfer information, agents must change the surrounding

environment for others to perceive and act accordingly. This is also known as stigmergy, where

the environment serves as a median of communication [Bonabeau et al., 1999].

2.1 Swarm Intelligence in Network Resource Allocation

Agents behaving under swarm intelligence can be released into a network to efficiently allocate

limited resources. As such, in our case, agents distribute the limited number of cellular

frequencies among the cells to accommodate network fluctuations and areas in high demand. This

approach leads to agents distributing channels instead of a central system - each solving the

problem at hand locally.

The specific strategy used in our agent-based solution is referred to as division of labour.

Overtime, agents in the system establish a routine or pattern to efficiently divide the total

workload. This is accomplished by using dynamic response thresholds that change depending on

the network demand. Network cells that are in high demand and low on resources stimulate

agents randomly until one responds. This can be seen as releasing pheromones into the system,

propagating from the exhausted cell. Each stimulated agent determines if it will respond to the

cell depending on its response threshold. If the stimulus is equal to, or above the agent threshold,

it will respond and proceed to take the required steps. Otherwise, the agent will ignore the

stimulus and continue what ever it was doing. Each agent has a unique response threshold for

each cell in the network. There is an indirect transfer of information between agents, or

stigmergy, because when an agent responds to a cell in need it changes the environment and

eliminates that stimulus for others.

FIGURE 2. Network grid with intelligent agents.


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The above strategy of division of labour is used in conjunction with adaptive task allocation.

Adaptive task allocation consists of agents adapting and “learning” to perform certain tasks of the

overall workload. The system accomplishes this by changing response thresholds depending on

cell stimulation. If an agent responds to a cell in demand, it will lower its threshold for that cell

and its neighboring cells by specified amounts, and increase its threshold for all other cells.

Therefore, agents are able to “specialize” in particular areas of the grid where they have done

more work, and have lower response thresholds. Since call generation rates, directions, and

locations are not completely random, where they follow some sort of pattern or trend over time,

agents specializing in areas of the grid produces a flexible system that can adapt to changing

network conditions. It should be noted that with the current implementation, only agents that are

stimulated change there thresholds, idle agents do not lower or change there response thresholds;

this concept will be seen later.

When a cell with high demand iterates through the list of available agents, stimulating each one,

the probability that an agent a located in cell i responds to a stimulus from cell j is:

2,

2,

2

2

jijaj

jaij dS

SP

βαθ ++= (1)

Where Sj is the amount of demand or stimulus from cell j, θa,j is the response threshold of agent a

for cell j, di,j is the distance between cell i and cell j, and α and β are two positive coefficients that

weight the influences of θ and d. If an agent a responds to a stimulus from cell j, it updates its

response thresholds in the following way:

0,, ξθθ −= jaja (2)

1)(,)(, ξθθ −= jnajna , for ∀n(j) (3)

ϕθθ += kaka ,, , for ∀k where k ≠ j and k ∉ {n(j)} (4)

Where {n(j)} is the set of neigboring cells to cell j, ξ0 and ξ1 are two learning coefficients

corresponding to cell j and its neigbors respectively, and ϕ is the forgetting coefficient for all

other cells in the grid. So to summarize, an agent that responds to a cell stimulus lowers its

response threshold for that cell according to Eq. (2), lowers its threshold for all neighboring cells

using Eq. (3), and increases its threshold for all other cells using Eq. (4).


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The four elements of self-organization are accommodated in our agent-based solution. Positive

and negative feedback is created through lowering and increasing response thresholds,

respectively. Idle agents moving randomly through the network is an example of amplification of

fluctuations, or the required randomness. And the need for multiple interactions exists because

agents indirectly communicate with each other through the environment.


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3. Cellular Network Simulation

A simple cellular network simulation was developed by Lawlor, which was programmed using

Java. The network consists of a ten by ten grid of hexagonal cells, representing the cellular

telephone grid. A total of 100 channels are allocated to the system, with each cell initially holding

ten channels. This is possible because as stated before, frequencies may be repeated in the

network so long as they are not in adjacent cells. The simulation is executed based on simulated

seconds, also known as ticks, where each second advances all network operations by one step.

Calls are generated depending on a user specified call generation rate. The call generation rate, or

call rate, does not give the number of calls per second, but only provides an upper bound on the

number of call that can be generated in a second. The actual number of calls per second is a

random number uniformly distributed between zero and the call rate. Since a channel is

composed of the three frequencies required for communication, each call requires one channel. A

newly generated call is randomly positioned on the grid, but is affected by the hotspot algorithm –

explained later. The hotspot algorithm also determines the call direction. The holding time of a

call and its velocity in the network is randomly determined and will be clarified in more detail

later. Handoffs occur when a call moves from one cell to another, and a suitable channel is

allocated in the new cell. The percentage demand in each cell, or simply cell demand, is equal to

the current number of in-progress calls divided by the total number of channels in the cell. So

therefore a cell with demand of 0% is not using any of its available channels, while a cell with

demand of 100% is using all of its available channels.

FIGURE 3. Screenshot of original cellular simulation developed by Lawlor.


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Agents are referred to as ants in the system, and as such, the two terms will be used

interchangeably from this point forward. Any number of ants may be released into the network,

and is specified by the user. Each ant moves randomly within the network from one cell to

another in an “Idle” state until stimulated by a cell in high demand. This random movement of an

idle agent can symbolize an actual insect wandering about its colony, due to lack of stimulation.

Cells in high demand are defined in the system as cells that are using over 65% of there current

resources, or channels. When a cell reaches this status, it begins to stimulate idle ants until one

responds. The response probability that an ant has to a stimulus is determined by the current

response threshold that ant has for the particular cell. As noted above, all ants in the system have

a unique response threshold for each cell in the network grid. Ants that respond to a cell

stimulation begin to move toward the cell - in a “In Transit” state. Once the target cell has been

reached, the ant changes to a “Working” state. While in this state, the ant tries to allocate new

channels to the high demand cell. It accomplishes this by “stealing” channels from neighboring

cells, while preventing any frequency interference from occurring due to new channel locations.

Ants may not create new channels, nor steal channels from a cell that has a specified minimum

number. Each cell must have a minimum of five channels, so it does not get completely starved of

resources.

3.1 Java Classes

All components of the Java simulation will not be listed here, but instead only the important

classes are summarized below. A detailed description of all the implemented classes can be found

in the report done by Lawlor.

Cell Each cell in the network grid is implemented as a unique object. The class has instance variables

for its position in the grid, a list of channels allocated to it, a list of current calls in-progress, its

current demand (channel usage), and whether or not an ant is assigned to it.

Ant Each ant is also implemented has a separate object. The ant holds its current position on the grid,

its current state, and response threshold for each cell in the network grid. The state of an ant can

be “Working”, “In-Transit”, or “Idle”. The response threshold is a number between 0 and 100,

and is initialized to 50 when the ant is created. In addition to the above instance variables, the Ant


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class contains algorithms to determine if it will respond to a stimulus, and algorithms to allocate

new channels while working.

Antwork The Antwork class is the main model of the simulation. It is responsible for most of the operation

and execution of the network simulation. Once started by the user, the Antwork class progresses

time in the system to generate and manage calls, manage the movement and behavior of all ants,

and keeps track of all important simulation statistics.

NetworkFrame The NetworkFrame class is the container frame for the entire graphical user interface (GUI). It

contains the panel (NetworkPanel) of network cells, displays all simulation statistics, and all input

buttons and text fields for the user to change settings and control the overall simulation.

3.2 Problems with Original Simulation

The previous agent-based solution developed by Lawlor worked well, but only under certain

situations. Agents in the network produced noticeably better results only with call rates between

four and seven and only with a specific number of ants [Lawlor, 2002]. No specific trends or

patterns emerged consistently through the different call rates when more ants were added. What

did occur was extensive channel shuffling by ants - unnecessarily moving channels from one cell

to another and back, which was also noticed by Lawlor. An extensive look at the project and

simulation results revealed some interesting observations – enough to justify the mixed results

obtained by the original agent-based solution.

A new call was generated with a random location on the grid, with a random call direction. With

this randomness, simulations that are run for an extended amount of time should produce calls

that are evenly distributed over the grid. With equally distributed calls, the addition of ants should

not produce better results (less calls blocked). In some cases, ants may relieve certain cells with

high demand, but in the long run, ants will only end up shuffling channels back and forth between

cells. This explains why simulation runs with ants did not always produce better results, but did

result in thousands of channels being stolen.


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An error was discovered in how random numbers were generated. A new Random object was

created in every method (where needed) resulting in similar time seeds to be used in successive

method calls. When multiple Java Random objects use the same seed, they will all produce the

same sequence of random numbers. Therefore, this resulted in a higher then expected call

generation rate in certain cells. To prove this, a simulation was done for 500 seconds, averaged

over ten runs, with a call generation rate of ten. The original simulation produced a call blockage

of 9.1%, while the corrected simulation only blocked 2.9% of the calls. The corrected simulation

only creates one instance of Random in the main operation class and every method refers to it.

It was noticed after experimental simulations that ants would specialize in specific areas of the

grid. This was also noticed by Lawlor, “the ant became so specialized in a specific locality of the

grid, that it completely ignored demands from any other area” [Lawlor, 2003], where he was

referring to one ant in the network. But similar results were observed with more ants: “similar to

the case with one ant, these five ants become over-specialized in particular areas and ignored the

demands of other areas” [Lawlor, 2003]. Looking into this phenomenon further, the network

simulation was tested numerous times, finally revealing the problem. It was noticed after some

time that ants would always tend to stay on the left side of the grid. Upon studying the code, the

reason was determined - cells in need of ants would be iterated through in order, from top to

bottom. This gave the first cells in the list higher priority over ants than the last cells. So

overtime, cells in the top left corner would have easier access to ants (lower thresholds) than cells

in the bottom right corner. This effect was only seen and detectable with a low number of ants - in

our case one and five. A higher a number ants caused the network grid to become denser,

resulting in less obvious trends and patters.

Some other minor problems in the simulation code were found. As Lawlor had sated in his report,

ants were stimulated in order instead of randomly. This may not seem to be very serious, but

caused some very undesirable effects in the simulation. Over time, ants in the beginning of the list

that were stimulated first had lower response thresholds than ants in the end of this list. And in

some cases, ants in the end of list were never stimulated – always randomly moving around. The

overall effects of this on simulation results are not completely known. Another code defect was

found in the algorithm that “steals” channels for working ants. Ants working in cells with high

demand, where the channel usage is over 65%, were allowed to steal frequencies from

neighboring cells that were also high in demand. So for example, an ant in a cell with demand of

70% was allowed to a take a channel from a neighbor cell with demand of 90%. This was most


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likely a simple mistake, but it did lead to higher channel shuffling rates. A simulation was done

for 2000 seconds with 20 ants at a call germination rate of ten, averaged over ten runs. The results

are shown in Table 1.

Total Calls Calls Blocked Stolen Channels

Original Simulation 5696 19.5% 9112 Corrected Simulation 5679 19.7% 7385 TABLE 1. Comparing simulations with corrected channel-stealing algorithm.

As one can see, the number of stolen channels dropped by 1727 or 19%, while the total number of

calls and percentage blocked was about the same.

Even though some problems and faults had been found, which were candidly pointed out here,

the project done by Lawlor was an excellent start to the proposed agent-based solution. The work

done was a significant achievement considering the little time given to complete the project. The

completed work had set up a solid base and framework on to which it was built upon.

3.3 Software Additions and Modifications

Extensive modifications were done to the Java simulation. This included minor changes such as

different call holding times and call speeds, but other large components were also added to the

application.

The averaged call holding time was 380 seconds, or about six minutes. Under realistic

circumstances, this may be too long for a cellular telephone call. Therefore it was changed to 180

seconds, or three minutes – representing a more likely call time. The actual call holding time is

randomly distributed between 5 and 1505 seconds.

Call velocity, or call speed, represents how fast a call moves through the network grid, being

handed off from one cell to another. Originally, this was a random number between 0 and 0.005,

which gives the number of seconds a call lasts in a cell before being handed off. Therefore, if call

were moving at a maximum speed of 0.005, it would be handed of every 200 seconds. With an

average call holding time of 180 seconds, most calls would never be handed off. With such a

slow speed, the handoff feature would not be properly tested. The call velocity was changed to a

random number between 0 and 0.02, which gives a maximum speed of 50 seconds per cell. So an


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average call lasting 180 seconds may be handed off three times, given the maximum speed. This

may seem fast, but could represent a realistic situation such as a car on a highway.

3.3.1 Random Agent Sweep

As noted above, the algorithm that sweeps through ants for cells in high demand was not random.

This caused the differing response thresholds between ants in the beginning of the list and ants at

the end of the list. Not only was this sweep algorithm fully randomized, but an extra feature was

added. Before random stimulations took place, ants located in the neighboring cells were

stimulated first. So therefore a cell in high demand initially searched for idle ants that are located

in its neighboring cells, or even in its own cell. The feature was added because the sweep

algorithm may find an idle ant located five or more cells away, while there could be an idle ant

located right next to the demanding cell, producing inefficient results from long “In Transit”

times. This could also represent a more realistic release of pheromones, where the chemical

substance propagates from its originating location. This propagation effect initially reaches

nearby insects before being sensed by the rest of the colony. To test the effect of the proximity

sweep algorithm, a simulation was conducted for 4000 seconds with 20 ants at a call rate of ten,

averaged over ten runs. The Random Hotspot call model was used (explained later) because it

allowed for a greater variety of possible ant locations, resulting in higher ant-to-cell distances.

The results are shown in Table 2.

Total Calls Stolen Channels Calls Blocked Random Sweep 11398 7506 15.2% Proximity Sweep 11413 10444 12.8%

TABLE 2. Comparing agent sweep algorithms.

The algorithm with the proximity sweep blocked fewer calls. However it resulted in more

channels to be stolen. This is because in the regular random sweep, when a cell successfully

stimulates a far off ant, the ant must take some time to travel to the high demand cell. Once the

cell has been reached the ant maybe too late to help or the channel usage may have dropped

below the threshold (65%) and the ant continues to be idle. On the other hand, the sweep

algorithm with the proximity feature allows cells to stimulate nearby ants to steal channels. The

nearby ant can quickly steal channels for the cell in need, resulting in higher channel shuffling

rates.


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3.3.2 Call Models

As mentioned above, the main reason the agent-based solution did not work well was because of

the complete randomness in the network. Calls were generated in a random location on the

network grid and moved in a random direction. The agent-based solution is most effective under

realistic situations, where network activity is somewhat predictable or organized. Simulations of

actual cellular network girds should show clear patterns and trends over time that will cycle

depending on time and day. To depict these realistic situations in the cellular simulation, multiple

call models were added. They are: All Random, Downtown, Centre Hotspot, and Random

Hotspot.

All Random Not really a call model, All Random represents the original network simulation, with random call

location and direction. With the All Random call model selected, the location of the call and

direction is randomly determined, and not dependent on any other condition such as time. This

call model was included just have some sort of benchmark and a method of comparing with the

original simulation.

Downtown The Downtown call model represents a simplified version of a typical workday in a city. The call

model consists of four cycles, each lasting an equal time. The first cycle represents the morning,

where people are moving into the city centre. Calls in this cycle have a direction toward the

centre, and have a random call location that is partially centre oriented. The second cycle

represents the working afternoon, where most people are densely populated in the centre of a city.

In this cycle, calls are generated in the centre and have a random direction. The third cycle

represents the end of day where people are traveling back home. The cycle generates calls with an

outward direction and a location that is partially centre oriented. The last cycle of the Downtown

call model represents the evening, with no clear pattern or trend. Calls are generated with a

random location and direction. Upon completion of the fourth cycle, the model starts to repeat

and begins with the first cycle again.

Centre Hotspot If a centre hotspot is desired, like in the Downtown call model, but all the other cycles are not

required, a user may select the Centre Hotspot call model. It represented by only two cycles. The


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first generates calls with a random location and direction, like the All Random model. And the

second cycle generates calls that are densely populated in the centre, with an inward moving

direction. The two cycles then continue to repeat. The overall effect of the Centre Hotspot is

simply a random situation followed by a centre hotspot.

FIGURE 4. Screenshot of a simulation with a centre hotspot.

Random Hotspot The last call model implemented is Random Hotspot. This call model is very similar to the Centre

Hotspot, but the hotspot location is randomly determined instead of centre-oriented. The first

cycle is completely random, and the second cycle generates calls in a random hotspot location

with calls also directed toward that location. Even though the Random Hotspot call model may

not represent a typical trend or pattern in a cellular network grid, it gives a good representation of

unpredictable events. As described above, unpredictable events are caused by a gathering of

people that normally does not occur during that particular day or time. As well, Random Hotspot

will most likely give the best indicator on how adaptive the agents are. Besides the first All

Random call model, the other call models are centre oriented, allowing agents to “settle” into a


16

predictable routine over time. The Random Hotspot model should test their full capabilities to

changing network conditions.

The user may select the desired call model through the graphical interface. All cycles in each call

model run for the same amount of time. It is initialized to 500 seconds, but can be changed by the

user through the GUI.

3.3.3 Simulation Mode

Too properly test the network simulation under all conditions, such as differing call rates and

number of ants, many experimental simulations must be done. Initially this was accomplished by

modifying the Java code to reflect the desired conditions, resulting into a tedious and inefficient

process. Therefore a “Simulation Mode” was added to the software.

By selecting the Simulation Mode, a user can simulate by entering the desired number of ants,

call rate, seconds to simulate, and number of runs to average over. A log file is created for each

simulation, showing all the statistics averaged over the number of runs. The name of the log file

can be specified by the user, or left as “auto”. The “auto” feature automatically creates a unique

file name depending on the number of ants, call rate, seconds, and runs. For example, a

simulation using 30 ants for a call rate of 10 for 2000 seconds averaged over 5 runs will create a

log file with the name: sim_30_10_2000_5.txt. Note the file name currently does not include the

call model.

A unique feature of the Simulation Mode is the ability to input multiple values for the number of

ants and call rate. For example, an experimenter may decide to test ten different call rates using

five different ant populations under the same network conditions. This combination can be

entered into the program and left alone to simulate on its own. The feature is very useful when

running long simulations, allowing the experimenter to execute the program and “leave it”,

possibly over night. Each value is delimitated (separated) by a comma in the text field. A log file

is created for each simulated ant and call rate value, forcing the “auto” feature to be used (for the

log file name).


17

3.3.4 Dynamic Ant Adjustment

A primary goal of the project is the make the system adaptive. The number of ants should

somehow adjust to the different network conditions. Hence, the “Dynamic Ant Adjustment”

feature was added. If selected, this feature will automatically adjust the number of ants in the

network to an optimal level.

The Dynamic Ant Adjustment feature was implemented using the ant response thresholds. Every

second, each ant will calculate its average response threshold, equal to the average threshold of

the 100 network cells. If the average threshold of an idle ant is below a certain point, indicating

the ant is not being used, it will terminate itself (from the system). If an ant, in any state, has an

average threshold above a certain point, it will spawn a new ant, or duplicate itself. The new ant

will be located in the same cell as the original. The current state of an ant – “Idle”, “In Transit”,

or “Working” – is taken into consideration before termination. Only an “Idle” ant may be

terminated, because an ant that is “Working” or “In Transit” is needed and should not terminate.

The spawning feature is executed in any state.

The average response threshold for ant termination must be less than one percent. This assures the

ant has not being stimulated and therefore has not worked. The average threshold for ant

duplication (spawning a new ant) must be greater or equal to 80%. Eighty percent was chosen

because a higher response threshold was not likely to occur, unless the entire network was under

extremely heavy demand. As well, 80% gives a good indicator of when an ant is excessively

worked, allowing another ant to enter the system and “help out”. Note the above parameters, 1%

and 80%, have been found to be good values based on experimentation, but may not represent

optimal values.

It must be noted here that the agent algorithms for adaptive task allocation had to be modified to

accommodate this feature. As noted above, idle ants do not lower or change there response

thresholds. So an ant that is never stimulated nor works continues to have the same response

threshold that it was initialized to, currently 50%. As one can deduce, the above ant termination

process can never occur under this situation. Therefore, to accommodate unused ants and the

termination process, ant thresholds decrease by 0.05% every second for each cell when the ant is

idle. So overtime, an idle ant in a low-demand network will reduce its threshold until it terminates


18

itself. The threshold reduction only occurs when the Dynamic Ant Adjustment feature is selected.

The following code represents the actual termination and duplication algorithms used.

if(autoAnts) // ant termination algorithm... { // decrease all cell thresholds for an idle ant thisAnt.decreaseAllCellThresholds();

// if average threshold below 1.0, terminate! if(thisAnt.getAverageThreshold() < 1.0)

{ if(Ants.size() > 1) { // must have at least one ant so it Ants.remove(i); // is able to re-generate population i--; } } }

if(autoAnts) // ant duplication algorithm... { // if average threshold greater or equal to 80, spawn! if(thisAnt.getAverageThreshold() >= 80.0)

{ thisAnt.resetThresholds(); // must reset thresholds of original ant Ant newAnt = new Ant(thisAnt.getPosition()); newAnt.setHandle(this); Ants.add(newAnt); // add new ant in same location as original } } 3.3.5 GUI Modifications

Significant changes were made to the graphical user interface (GUI). Some are self-explanatory

such the required text fields for the Simulation Mode, the radio buttons for the call model

selection, and additional fields for simulation statistics. Other modifications were made for

cosmetic reasons.

FIGURE 5. Screenshot of control panel in modified GUI.

Originally, each cell displayed the channel usage and call demand percentage. The call demand

text would change colour depending on demand, from white to yellow to red, and the cell itself

was black. In the new simulation, the user has the option of showing the cell statistics, and the


19

colour of the entire cell changes depending on call demand. The cell progressively changes colour

from green to red, depending if the cell has low or high demand. This displayed a much clearer

picture of the network grid and progression in the system. Furthermore, the update procedure in

the GUI was changed to dramatically reduce flickering.

FIGURE 6. Screenshots of two different views of cellular grid in GUI.


20

4. Experimentation

To test the new and improved network simulation, many simulations were conducted - utilizing

all the capabilities of the application. Simulations were done for the three call models:

Downtown, Centre Hotspot, and Random Hotspot. The All Random call model was not

considered because agents in a random network will not necessarily produce better results, as

seen in previous experimentation done be Lawlor.

For each call model, simulations were done for call generation rates from three to ten. Lawlar had

started from a call rate of two, but that resulted in too few calls generated and no calls being

blocked, hence the call rate now starts at three per second. For each call rate, the following

sequence of ants were used: 0, 1, 5, 10, 15, 20, 25, 30, 35, and 40. Simulations using zero ants is

to set the benchmark: the network with no agents. Simulations using only one ant is merely as a

boundary case, and should not necessarily produce better results than zero ants, since one ant is

not considered a swarm. For each combination of call rate and number of ants, the simulation was

run for 4000 seconds (about 66.7 minutes) and averaged over five runs. Four thousand seconds

was chosen because it gave sufficient time for the number of in-progress calls to level off, while

thoroughly testing the capabilities of agents. A log file was created for each simulation with

average results for the five runs.

In addition to a fixed number of ants, simulations were done using the automatic ant adjustment

feature. It was tested for each call model, using all above call rates. All simulations start off with

20 ants, and automatically adjust depending on network conditions. Each simulation was run for

12,000 seconds, averaged over five runs. Such a long simulation time was chosen because the

network needed enough time to adjust the number of ants to the optimal level and stabilize. Also,

there needed to be enough time to accurately measure the benefits of the stabilized ant population.

4.1 Simulation Results

The simulation results are shown in the following graphs, grouped by the call model used. Each

graph shows the relationship between the number of ants used (horizontal axis) and the

percentage of calls blocked (vertical axis). As well, each graph contains a star that represents the

results of the Dynamic Ant Adjustment feature. The point of intersection depicted by the star


21

represents the final number of ants at the end of the simulation and the percentage of calls

blocked. Tables with detailed information for each simulation are presented in Appendix A.

4.1.1 Downtown Call Model

Dowtown Model: Call Rate of 3

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FIGURE 7. Simulation results of Downtown call model using call rate of 3.


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The agent-based solution using the Downtown call model produced excellent results. Each graph

shows a very clear trend and correlation between the percentage of calls blocked and number of

ants. As more ants were added to the system, the percentage of calls started to level off into a

plateau. The only fluctuation seen was when one ant supported the network. And as the call rate

increased, the fluctuation grew larger. This is nothing to be concerned about since one ant is not

considered a swarm and should not necessarily produce better results. In the situations were the

lone ant produced worse results, the cause is not completely known. It could be possible that with

such high call rates, the ant was shuffling channels in a way that caused more calls to be blocked.

Considering the above call rates of three to ten, the agent-based solution reduced the number of

calls blocked by 63% - 73%.

Using the Dynamic Ant Adjustment feature also showed very good results. It surprisingly chose

the optimal number of ants that produced the lowest call blockage. And in the lower call rates, the

dynamic number of ants produced very good results using fewer ants than the fixed method. The

number of calls blocked was reduced by an average of 67% using dynamic ants.

4.1.2 Centre Hotspot Call Model

Centre Hotspot Model: Call Rate of 3

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FIGURE 15. Simulation results of Centre Hotspot call model using call rate of 3.


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Like the Downtown model, the Centre Hotspot call model produced good results when agents

were added. Again the graph would level off and plateau after a certain number of ants. With this

call model, all situations with one ant showed a lower number of call blocks than the agent-less

system. One other interesting discovery of this call model is how evenly uniform the graphs are

when compared with the Downtown model. The trend lines are very level, with little fluctuations.

This is most likely because there is less cycles than the Downtown model, and gave the agents

more time to adapt and settle into the predictable pattern. Simulations with the Centre Hotspot

model produced a remarkable 71% - 82% reduction in calls blocked using agents (in all call

rates).

Again the results using the Dynamic Ant Adjustment were excellent. The number of ants adapted

very well to the different call rates. The feature even produced better results with lower call rates

than any of the fixed ants. But this could be due the fact that the simulation were run for three

times longer than with fixed parameters (12,000 compared with 4000 seconds). Using the feature

caused an average reduction of 76% in calls blocked.


28

4.1.3 Random Hotspot Call Model

Random Hotspot Model: Call Rate of 3

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FIGURE 23. Simulation results of Random Hotspot call model using call rate of 3.


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The simulation results using the Random Hotspot call model were different than previous results,

but still very good. Adding ants in the network did lower the call blockage until it leveled off, but

there were more fluctuations. The graphs are not as even and uniform like the graphs in the other

call models. This is mainly because the hotspot was always in a random location every cycle. The

ants were unable to adapt to network conditions as easily as the other two centre-oriented call

models. Due to this, more fluctuations in the results should be expected. If the simulations were

averaged over more runs, such as ten instead of five, the graphs should become more uniform.

Agents in the Random Hotspot call model reduced the number of calls blocked by 55% - 77%.

When the number of ants were allowed to adjust automatically, it sometimes resulted in more ants

in the system than required. This was especially apparent with higher call rates. However, the

adapted number of ants did result in a call blockage rate that was close to the minimum that was


31

obtained using fixed ants. We could conclude that the random hotspots produced a high demand

for ants that resulted in higher ant populations, bu did not result in better call rates simply because

the network is too stressed and could not be relieved by extra channel shuffling. When the ant

population was able dynamically adjust, the call blockage was reduced by an average of 62%. 4.3 Observations and Analysis

Upon examining all the above simulation results, we can confidently conclude that adding agents

to the cellular network produced better call rates under all conditions. All simulations leveled off

to a plateau when more agents were added. Agents proved to work very well and self-organize to

dramatically reduce the percentage of calls blocked. Considering all call models, agents were able

to reduce the number of calls blocked by 55% - 82%. Agents in the Centre Hotspot call model

reduced the call blockage by an average of 77%. While agents in the Downtown and Random

Hotspot model reduced the calls blocked by an average of 70% and 68%, respectively. The higher

performance results in the Centre Hotspot call model can be attributed to the less random network

conditions, allowing agents to adapt more easily to the centre-oriented network demand. Overall,

the agent-based solution decreased the number of calls blocked by a remarkable 72%, considering

all simulated network scenarios. In contrary to previous simulations done by Lawlor, the call

generation rate had no effect on the results. With high call rate such as nine or ten, when one

assumes the network may be too overloaded to have any affect, the addition of agents always

improved network performance. Another interesting fact was when the simulation results attained

the minimum call blockage rate, the addition of more agents did not overload the system but

continued to produce the same performance results. Although the Random Hotspot model

generated more fluctuations in the graphs, the results were excellent. Agents were able to adapt

efficiently to the continuous flow of random network hotspots.

The results of the Dynamic Ant Adjustment feature were outstanding. The number of ants

adapted very well to network conditions, producing call rates that were good as any of the fixed

ant methods. The system managed to adapt to an optimal minimal number of ants under every

situation, with the exception of the Random Hotspot. With higher call rates in the Random

Hotspot model, more ants were spawned then required. But even with the higher ant populations,

call rate results were not affected. An average 68% reduction in call blockage was achieved using

the feature, considering all call models and call rates.


32

5. Conclusion 5.1 Similar Work Done in Field

There has been a great deal of work in the field of intelligent agents used for network control and

management. Considerable interest in this field arises from the decentralized nature of agent

technology and the ability to adapt real-time to network conditions. One researcher by the name

Khaldoun Al agha had done similar work to this project by using intelligent agents associated

with wireless networks to achieve adaptive resource allocation [Al agha, 2000].

Al agha proposes a multi-agent solution for intelligent base stations in wireless networks. Agents

are able to combine knowledge and experience with neighboring agents to make the best

decisions. This is achieved by agents cooperating, communicating, reasoning, and perceiving.

Agents corresponding with a base station are capable of communicating its state to neighbors and

learning from past events in the environment to optimize the utilization of resources.

The agent solution is used in conjunction with a resource allocation scheme known as Channel

Segregation (CS). Channel Segregation differs from traditional dynamic allocation schemes

because it has a simple form of self-learning. It involves segregating physical channels from a

common pool by each base station to form a preferred list of channels. Base stations attempt to

allocate channels at the top of their priority list. The learning aspect of CS is achieved though the

method priority lists are formed, resulting in differing lists across the network cells – and

stabilizing over time. Simulation results have shown that the integration of intelligent agents with

channel segregation had improved call rates by decreasing the number of calls blocked.

An overview of research done in the field of both Communication Networks (CN) and

Distributed Artificial Intelligence (DAI) can be found in [Willmott and Calisti, 2000]. Willmott

and Calisti identify current trends in agent-based network control and management. They discuss

areas that would most benefit from agent technologies and deployment strategies for agent based

solutions. For a thorough summary of mobile agents in networks, the reader may refer to

[Sriraman, 2002].


33

5.2 Future Developments

Under the current implementation, agents only interact with the environment, the network cells.

An aspect of self-organization is the reliance on multiple interactions, and the ability of agents to

make use of the results of their own actions and the actions of others. The latter is not very

apparent in the current system – agents make very little use of the actions of others. The next

development process should include some sort of agent-to-agent communication. For example,

agents can directly communicate with each other to share information or request for help in high

demand areas. This may be accomplished by agents simulating other agents, or cooperative

negotiation between agents. The latter was a proposed solution by Bigham, where agents

associated with the cellular base station would negotiate with each other agents to optimize local

cell coverage [Bigham and Du, 2003].

Currently, agents can only steal a channel from a neighboring cell. This may not be too effective

when an agent is in the centre of a large hotspot, where there are no free channels available. A

proposed solution to this is to give agents the ability to steal-and-carry channels while moving

through the network. While idle, if an agent comes across a cell with a high number of unused

channels, it may steal some channels and carry them around with it. While working, the agent can

then deposit the stolen channels into a high demand cell. Note this feature would be used in

addition to the current ability of agents stealing channels from neighbors. With more options

given to agents in the system, a higher form of intelligence should emerge, producing a more self-

organized swarm.

An aspect of swarm intelligence is the concept of worker polymorphism, which basically states

that different entities in a swarm are responsible for different tasks [Bonabeau et al., 1999]. These

tasks depend on the class hierarchy of the swarm, and were the insect (or other animal) sits in the

hierarchy. The idea of worker polymorphism may also be used for our agent-based network.

Agents may begin there existence in the system by performing a specialized task, such as

shuffling channels, stealing and carrying channels, or distributing network information. As time

progresses, agents may begin to carry out different tasks or possibly multiple tasks - depending on

thresholds. Thereby, agents adapt to the current requirements of the network and the swarm as a

whole.


34

Improvements on the cellular simulation can also be made. As suggested by Lawlor, the call

generation rate can change depending on time or call model cycle. A dynamic call rate would

represent scenarios in actual cellular grids more accurately. This would also thoroughly test how

agents adapt to changing conditions. Many have proposed the concept of dynamically adjusting

the cell size to improve performance in cellular networks [Togo et al., 1998] [Du et al., 2002].

This may be implemented into the current system in conjunction with intelligent agents, possibly

giving the agents control of cell size.

5.3 Summary

The previous agent-based solution had some apparent problems. Most importantly, the fact that

everything was random produced a network that was also too random for agents to have any

consistent affects. The introduction of the different call models produced an environment for

agents to interact and exhibit a form of swarm intelligence.

Examination of simulation results has shown us that significant improvements have been made

over the previous agent-based solution. Agents in the network consistently produced better call

rates in every condition. Agents were able to successfully self-organize and adapt to network

conditions, such as differing call generation rates and call models. The dynamic approach for ant

population also produced excellent results. The number of ants were able to adapt to a minimum

number in most cases. Compared with the fixed channel network, agents decreased the number of

calls blocked by an average of 72%, and 68% when given the ability to change the population.

We can confidently conclude that the agent-based solution for dynamic channel allocation in

cellular telephone networks is feasible. Using division of labor and adaptive task allocation,

mobile agents modeled after social insects produced a decentralized, robust, and adaptive system.

The simple interconnected agents were able to self-organize and exhibit intelligent behavior to

dramatically decrease blocked call rates.

However, the project is still open to further research and development. A primary goal of the next

phase should be direct agent-to-agent communication, leading to higher intelligence and self-

organization abilities. The cellular simulation could also be modified to represent more realistic

situations, such as dynamic call generation rates and additional call models.


35

References [Al agha, 2000]

Al agha, K. (2000) Resource management in wireless networks using intelligent agents, International Journal of Network Management, 10:29-39.

[Bigham and Du, 2003]

Bigham, J., Du, L. (2003) Cooperative Negotiation in a Multi-Agent System for Real-Time Load Balancing of a Mobile Cellular Network, Association for Computing Machinery.

[Bonabeau et al., 1999]

Bonabeau, E., Dorigo, M., Theraulaz, G. (1999) Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press.

[Du et al., 2002]

Du, L., Bigham. J., Cuthbert, L., Parini, C., Nahi, P. (2002) Cell Size and Shape Adjustment Depending on Call Traffic Distribution, IEEE.

[Johnson, 2001]

Johnson, S. (2001) Emergence: The Connected Lives of Ants, Brains, Cities, and Software, Scribner.

[Lawlor, 2002]

Lawlor, M. (2002) A Self Organizing Social Insect Model for Frequency Allocation in Cellular Telephone Networks, Carleton University Honours Project.

[Sriraman, 2002]

Sriraman, S. (2002) An Annotated Bibliography of Mobile Agents in Networks, University of North Texas.

[Togo et al., 1998]

Togo, T., Yoshii, I., Kohno, R. (1998) Dynamic Cell-size Control According to Geographical Mobile Distribution in a DS/CDMA Cellular System, IEEE.

[Willmott and Calisti, 2000]

Willmott, S., Calisti. M. (2000) An Agent Future for Network Control?, Informatik/Informatique, Number 1, February 2000.


A-1

Appendix A – Simulation Tables The following tables show all the statistical data obtained for each of the simulations done in section 4 - Experimentation. The last line of each table represents the results of using the Dynamic Ant Adjustment feature. To keep the table columns a reasonable size, headings are abbreviated. Refer to the following legend for a full description of each heading: A Number of Ants TC Total Calls TH Total Handoffs IP Total In Progress Calls at End of Run BC Total Blocked Calls BH Total Blocked Handoffs BC% Blocked Call Percentage BH% Blocked Handoff Percentage NL% Network Load Percentage at End of Run SC Total Stolen Channels SC/A Total Stolen Channels per Ant Dowtown Call Model

A TC TH IP BC BH BC% BH% NL% SC SC/A 0 4880 6024 216 126 49 2.58 0.83 21 0 0 1 4863 6030 217 84 36 1.73 0.61 21 549 549 5 4891 6037 214 45 22 0.94 0.37 21 759 152

10 4899 6102 228 50 24 1.02 0.41 22 868 87 15 4868 6174 220 44 22 0.92 0.37 22 844 56 20 4881 6160 219 42 20 0.87 0.32 22 869 43 25 4846 6141 202 49 24 1.02 0.40 20 847 34 30 4883 6048 218 42 24 0.87 0.40 21 885 30 35 4880 6197 219 49 23 1.00 0.37 22 927 26 40 4884 6030 214 34 18 0.70 0.31 21 862 22 1 14624 19756 213 124 65 0.85 0.33 21 1763 88

TABLE A-1: Simulation data of Downtown call model using call rate of 3.


10 6011 7409 273 102 47 1.70 0.64 27 1560 156 15 6039 7345 262 98 48 1.63 0.65 26 1524 102 20 6005 7474 256 106 51 1.77 0.69 26 1680 84 25 6074 7525 268 106 59 1.75 0.79 27 1633 65 30 6073 7498 265 104 51 1.72 0.69 26 1654 55 35 6053 7545 268 107 54 1.77 0.73 26 1694 48 40 6050 7458 263 100 50 1.65 0.68 26 1682 42 3 18114 23659 272 281 149 1.55 0.63 27 4030 202



A-2


10 7089 8579 320 175 89 2.47 1.05 32 2515 252 15 7076 8612 315 177 92 2.51 1.07 31 2587 172 20 7105 8799 314 154 82 2.17 0.94 31 2784 139 25 7035 8767 320 158 81 2.26 0.93 32 2648 106 30 7059 8603 314 165 86 2.34 1.00 31 2538 85 35 7065 8565 317 142 70 2.02 0.82 31 2582 74 40 7091 8626 310 135 66 1.91 0.77 31 2783 70 4 21356 27735 308 419 226 1.97 0.81 31 6897 345



10 8023 9840 343 247 126 3.08 1.29 34 3863 386 15 8059 9744 361 197 106 2.45 1.10 36 3975 265 20 8135 9866 352 218 113 2.68 1.15 35 4055 203 25 8118 9917 362 228 114 2.82 1.15 36 4253 170 30 8082 9558 354 226 116 2.81 1.22 35 3895 130 35 8099 9836 353 227 115 2.80 1.17 35 4005 114 40 8074 9789 349 194 100 2.41 1.02 35 4135 103 7 24197 31105 350 666 366 2.75 1.18 35 11156 558



10 8925 10555 372 300 159 3.37 1.51 37 5100 510 15 9028 10710 398 299 165 3.31 1.54 40 5519 368 20 8999 10794 385 280 143 3.11 1.33 38 5690 285 25 8994 10883 381 284 151 3.17 1.39 38 5635 225 30 8991 10885 390 279 150 3.11 1.39 39 5742 191 35 9027 10908 394 287 143 3.18 1.31 39 5845 167 40 9031 10902 400 263 135 2.91 1.25 40 5805 145 11 26954 34226 393 825 446 3.06 1.31 39 16337 817



A-3


10 9886 11805 424 403 218 4.08 1.84 42 6535 654 15 9758 11490 427 397 202 4.07 1.76 42 6677 445 20 9805 11814 438 328 179 3.35 1.52 44 7226 361 25 9852 11793 419 313 169 3.19 1.44 42 7870 315 30 9863 11672 425 339 182 3.44 1.57 42 7606 254 35 9846 11736 439 333 169 3.39 1.44 44 7902 226 40 9847 11927 414 333 174 3.38 1.47 41 8137 203 16 29415 37443 424 932 512 3.17 1.37 42 23086 1154



10 10655 12262 455 564 291 5.30 2.38 45 7142 714 15 10624 12376 458 465 247 4.39 2.00 45 8695 580 20 10614 12387 460 404 217 3.81 1.76 46 9657 483 25 10589 12452 447 432 235 4.08 1.89 44 9863 395 30 10660 12667 465 424 222 3.98 1.76 46 10701 357 35 10751 12944 455 421 228 3.92 1.76 45 10871 311 40 10662 12634 493 390 211 3.67 1.68 49 10194 255 20 31910 40685 468 1204 686 3.77 1.69 47 30657 1533



10 11390 12779 447 754 397 6.62 3.11 44 7525 753 15 11384 13164 469 569 313 5.00 2.38 47 10269 685 20 11346 13129 487 522 278 4.61 2.12 49 11510 576 25 11347 13265 502 457 252 4.02 1.90 50 12656 506 30 11427 13767 487 444 237 3.88 1.73 48 13200 440 35 11413 13632 491 412 226 3.61 1.66 49 14057 402 40 11424 13557 496 444 240 3.89 1.77 49 13643 341 20 34199 42411 478 1492 814 4.36 1.92 48 36980 1849



A-4

Centre Hotspot Call Model


10 4891 6143 215 86 46 1.77 0.76 21 901 90 15 4873 5975 221 79 44 1.62 0.74 22 875 58 20 4860 6090 219 85 41 1.75 0.67 22 977 49 25 4868 6090 218 87 45 1.79 0.75 22 892 36 30 4879 6031 217 85 42 1.74 0.71 21 931 31 35 4847 5889 215 74 38 1.54 0.65 21 893 26 40 4828 6006 218 77 39 1.61 0.66 22 904 23 1 14597 19329 205 162 88 1.11 0.46 20 1780 89

TABLE A-9: Simulation data of Centre Hotspot call model using call rate of 3.


10 6086 7520 281 150 81 2.48 1.08 28 1682 168 15 6124 7548 255 128 65 2.09 0.88 25 1674 112 20 6059 7449 266 133 73 2.20 0.99 26 1632 82 25 6069 7478 271 131 66 2.17 0.88 27 1672 67 30 6075 7318 270 117 64 1.93 0.88 27 1699 57 35 6028 7255 275 135 73 2.25 1.01 27 1537 44 40 6036 7287 271 138 75 2.30 1.04 27 1573 39 2 18068 23545 264 272 141 1.51 0.60 26 3927 196



10 7017 8644 312 191 108 2.73 1.26 31 2420 242 15 7080 8598 293 169 90 2.39 1.05 29 2553 170 20 7072 8699 311 184 94 2.62 1.09 31 2791 140 25 7085 8867 305 168 91 2.38 1.03 30 2848 114 30 7130 8681 305 168 85 2.36 0.99 30 2695 90 35 7069 8656 307 169 88 2.40 1.01 30 2699 77 40 7094 8580 310 190 96 2.69 1.12 31 2837 71 4 21251 27648 321 504 278 2.37 1.01 32 6995 350



A-5


10 8065 9590 336 302 163 3.74 1.71 33 3732 373 15 8042 9804 353 298 156 3.71 1.59 35 3881 259 20 8013 9540 337 283 154 3.53 1.62 33 4045 202 25 8020 9728 353 261 148 3.26 1.53 35 4114 165 30 8042 9665 355 268 141 3.34 1.46 35 4060 135 35 8050 9589 349 270 154 3.36 1.60 34 4124 118 40 8104 9725 348 256 134 3.17 1.39 34 4348 109 8 24229 30973 344 889 493 3.67 1.59 34 11307 565



10 8926 10435 363 434 231 4.86 2.21 36 4740 474 15 9044 10584 381 424 228 4.69 2.16 38 5655 377 20 8968 10629 377 387 203 4.31 1.91 37 5564 278 25 8955 10575 364 378 203 4.22 1.93 36 5870 235 30 9046 10749 380 379 205 4.19 1.92 38 6222 207 35 8890 10461 367 351 189 3.95 1.81 36 5945 170 40 8972 10568 378 369 196 4.12 1.86 38 6019 150 11 26936 33629 377 1290 696 4.79 2.07 37 16434 822



10 9770 11128 386 570 307 5.84 2.77 38 5972 597 15 9790 11278 390 534 293 5.46 2.60 39 7039 469 20 9823 11342 391 515 277 5.25 2.45 39 7479 374 25 9860 11316 407 519 275 5.27 2.43 40 8036 321 30 9889 11644 389 471 253 4.76 2.17 39 8338 278 35 9795 11458 405 503 277 5.13 2.42 40 7933 227 40 9852 11505 420 479 252 4.86 2.19 42 7958 199 19 29424 36378 411 1551 863 5.27 2.37 41 24309 1215



A-6


10 10716 11763 425 826 430 7.71 3.66 42 6702 670 15 10603 11957 415 698 375 6.59 3.14 41 8658 577 20 10626 12341 432 674 358 6.35 2.91 43 10061 503 25 10660 12439 443 636 349 5.97 2.80 44 10785 431 30 10650 12377 420 629 340 5.91 2.75 42 11339 378 35 10574 12193 423 607 332 5.74 2.73 42 10698 306 40 10694 12354 427 605 337 5.66 2.74 42 11052 276 20 31836 38846 434 1908 1060 6.00 2.73 43 32032 1602



10 11418 12253 442 1067 534 9.34 4.36 44 6975 698 15 11358 12290 452 842 435 7.42 3.54 45 9746 650 20 11392 12878 453 807 427 7.09 3.32 45 11736 587 25 11389 12803 450 769 411 6.76 3.21 44 12803 512 30 11418 13070 449 740 394 6.49 3.02 45 13431 448 35 11390 12954 462 704 379 6.18 2.93 46 13853 396 40 11390 12883 449 689 371 6.06 2.88 45 13463 337 20 34309 40685 452 2471 1354 7.20 3.33 45 39488 1974

TABLE A-16: Simulation data of Centre Hotspot call model using call rate of 10. Random Hotspot Call Model


10 4875 5742 196 218 110 4.48 1.93 19 2150 215 15 4878 5850 213 249 130 5.11 2.23 21 2159 144 20 4890 5937 206 146 84 3.00 1.44 20 2453 123 25 4874 5957 216 165 88 3.39 1.50 22 2477 99 30 4872 5879 219 185 102 3.81 1.74 22 2133 71 35 4869 5781 203 195 95 4.01 1.67 20 2373 68 40 4969 5985 209 175 87 3.53 1.46 20 2528 63 8 14695 17930 196 779 380 5.31 2.12 19 5886 294

TABLE A-17: Simulation data of Random Hotspot call model using call rate of 3.


A-7


10 6005 7012 261 297 157 4.96 2.27 26 3532 353 15 6034 6913 231 388 200 6.44 2.95 23 3008 201 20 6027 7020 251 332 168 5.52 2.43 25 3214 161 25 6112 7263 266 273 138 4.48 1.91 26 3859 154 30 5968 6973 253 245 126 4.11 1.82 25 3195 107 35 6103 7281 245 311 153 5.11 2.11 24 3422 98 40 6056 7303 254 248 119 4.11 1.64 25 3744 94 12 18080 22046 262 1075 499 5.94 2.26 26 10811 541



10 7082 7916 254 591 281 8.36 3.55 25 4009 401 15 7123 8123 287 489 238 6.88 2.94 28 4738 316 20 7081 8071 278 443 203 6.26 2.56 28 5053 253 25 7115 8290 313 356 185 5.01 2.25 31 5503 220 30 7041 8123 293 414 213 5.89 2.65 29 4450 148 35 7100 8152 286 399 189 5.62 2.34 28 5524 158 40 7084 8280 296 349 176 4.92 2.17 29 6299 157 28 21266 25844 300 1361 661 6.40 2.57 30 16950 848



10 8050 8887 280 776 359 9.64 4.06 28 4962 496 15 8017 9000 306 704 314 8.79 3.51 30 5922 395 20 8085 9132 315 566 275 7.01 3.04 31 6717 336 25 8058 9037 311 665 322 8.26 3.59 30 6580 263 30 8122 9227 314 538 266 6.63 2.91 31 7374 246 35 8032 9122 323 571 278 7.11 3.11 32 7558 216 40 8104 9288 322 566 252 6.98 2.73 32 7176 179 33 24202 28646 328 1888 884 7.80 3.10 32 22390 1120



A-8


10 9027 9491 310 1071 469 11.87 4.96 31 5344 534 15 8965 9725 349 769 347 8.58 3.60 35 7120 475 20 8912 9947 344 629 295 7.05 2.98 34 7737 387 25 8945 10030 359 704 339 7.86 3.42 36 7476 299 30 9004 10336 352 654 312 7.27 3.02 35 8751 292 35 9012 10054 328 792 371 8.79 3.76 33 8191 234 40 8929 10138 337 627 301 7.02 2.98 33 9143 229 37 27048 31878 357 2298 1088 8.50 3.42 35 28533 1427



10 9815 10078 319 1361 539 13.87 5.36 31 5715 572 15 9863 10325 335 1224 533 12.41 5.20 33 7542 503 20 9752 10341 362 1078 478 11.03 4.67 36 8272 414 25 9827 10478 361 1049 466 10.67 4.46 36 9062 362 30 9824 10534 355 1065 474 10.85 4.56 35 8709 290 35 9891 10737 354 1043 470 10.54 4.39 35 9712 277 40 9816 10922 394 839 411 8.55 3.80 39 10356 259 46 29527 34429 399 2560 1277 8.67 3.71 40 33597 1680



10 10584 10568 344 1643 656 15.52 6.22 34 6034 603 15 10725 10921 355 1444 620 13.45 5.69 35 8154 544 20 10729 11508 370 1303 557 12.15 4.87 37 9902 495 25 10594 11233 378 1291 579 12.20 5.17 38 10139 406 30 10550 11513 371 1049 496 9.94 4.34 37 10861 362 35 10598 11075 376 1095 507 10.35 4.62 37 10810 309 40 10667 11467 375 1076 494 10.10 4.36 37 11451 286 48 31818 35980 372 3420 1583 10.75 4.41 37 38245 1912



10 11421 11482 358 1791 720 15.67 6.30 36 6592 659 15 11437 11267 379 1757 680 15.35 6.06 37 8353 557 20 11347 11726 385 1438 638 12.68 5.45 38 10403 520 25 11448 11829 402 1475 689 12.88 5.87 40 10885 435 30 11476 12115 387 1412 605 12.30 5.03 38 12715 424 35 11448 11770 366 1527 696 13.32 5.93 36 11190 320 40 11490 11721 388 1596 704 13.90 6.05 39 11441 286 50 34160 38117 385 4018 1834 11.76 4.82 38 44406 2220



B-1

Appendix B – Contents of Project CD The following is a list of files and folders contained in the CD that accompanies this report. Final Report.doc Final honours project report. Honours Proposal.doc Honours project proposal. Progress Report.doc Project progress report. Antwork\ Folder containing all source code for Java cellular simulation. To

execute program, compile and run NetworkFrame.java. Antwork Backups\ Folder containing old versions of Antwork (Java simulation) that

were saved during the development process. Simulation Files\ Folder containing log files for all simulations shown in the

report. Articles and Papers\ Various articles and papers collected during lifecycle of project.

Includes relevant topics on cellular networks, intelligent agents, dynamic channel and frequency allocation, agents used in network management, etc.

Michael Lawlor\ Folder containing all original work done by Michael Lawlor,

including original Java simulation source code.

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