simulation for traffice light

1. The use of Genetic Algorithms in optimising Mobile
Network resource allocation
A.N.Other
Modelling, Simulation & Optimisation
Postgraduate Diploma in Data
Analytics
National College of Ireland
Abstract—This document introduces heuristic algorithms
used to find solutions to problems with a large solution space,
metaheuristics to find near optimal solutions and genetic
algorithms as an example of a metaheuristic are a class of
evolutionary algorithms mimicking certain aspects of large scale
biological evolution phenomena in order to produce optimized
solutions to multi-objective problems such as resource allocation
in a mobile cellular network. Four papers describing this aspect
of large space solution optimization are reviewed starting with
John Holland, a pioneer of genetic algorithms from the 1980s
through to the application of these algorithms in the domain of
wireless and mobile network resource allocation problems
throughout the 1990s and 2000s right up to the present day to
state of the art 5G mobile networks introduced in the latter
stages of last decade.
I. INTRODUCTION
A heuristic technique involves producing a candidate
solution to a problem with a large solution space where the
accuracy of the solution chosen is deliberately compromised
for the sake of speed in producing an adequate solution where
adequate could be defined as being of minimum requirements
for being fit for purpose. Metaheuristics apply heuristic
techniques to provide optimal solutions to the problem. This
is largely achieved by using Stochastic processes involving a
certain amount of random selection and iteratively evaluating
an objective function representing the problem to be solved.
The optimisation comes from identifying either potentially
global maximum or minimum values of the objective function
for the parameters supplied which are randomly or
systematically chosen or a combination of both in an iterative
fashion until a “best optimised” solution is identified.
A Genetic Algorithm is a class of Evolutionary Algorithm
whereby the biological processes of crossover, mutation and
natural selection are emulated in producing optimised
solutions to large space problems. In this realm the objective
function evaluating the candidate solution to the problem is
referred to as the fitness function if a maximum value is
desired or a cost function reflecting a minimum. Evolutionary
Algorithms are a general class which apart from Genetic
Algorithms also encompasses Genetic Programming whereby
the solutions are represented by computer programs,
Evolutionary Programming whereby the programs are fixed
but the input parameters are allowed to evolve and other
derivative algorithms.
A central tenet to Evolutionary Algorithms is the existence
of a population of candidate solutions, each candidate solution
is represented as an individual within that population. Over
time or more appropriately over iterations the population of
candidate solutions changes or evolves comprising better
solutions. Each iteration represents a generation.
Producing a near optimal solution is typified by trying to
optimise multiple objectives where constraints exist and also
where there are many instances of local maxima or minima
i.e. local optimal points where small variation in the inputs
produces a worsening of the objective function value but
which does not reflect the global optimum available.
Evolutionary algorithms try to incorporate certain
analogies from biological evolution such as adaptation
whereby the inputs from promising (as evaluated by the fitness
function) candidate solutions are chosen and combined to
produce offspring candidate solutions whose fitness is
assessed. This is also known as selection and crossover. In
order to deal with local maxima and minima in a multi-modal
problem there is a trade-off between exploration and
exploitation which can be defined as how much effort is
expended in randomly searching across the problem space for
better solutions compared to searching within a locality for a
better optimisation. This introduced randomness is analogous
to mutation in biological evolution.
Genetic algorithms specifically encode the input
parameters as bitstrings or integer values (depending on
whether it represents discrete or continuous variable values
typically). These are intended to represent chromosomes.
Each iteration of the algorithm produces a population of
offspring candidate solutions whereby the input parameters
are combined from the parents chromosomes according to
various methods e.g. exchanging subsets of bit sequences
from the selected “fittest” parents. Randomness can be
introduced by mutation of the bit sequence of the offspring by
choosing a random proportion of the next generation offspring
and bit-flipping one or more bits representing the input
parameter encoding. The population of candidate solutions
can be fully or partially replaced in a manner of survival of the
fittest. Many iterations of solution candidate evaluation are
performed until a certain point based on number of iterations,
lack of improvement beyond a stated threshold or an elapsed
time. The solution producing the best value from the objective
function is identified as the optimal solution.
The author has a particular interest in how these
algorithms can be applied to optimisation problems within the
domain of mobile data and telecommunication networks. The
stated requirement of this paper to consider related studies in
Modelling, Simulation and Optimisation from the 1980s,
1990s, 2000s and 2010s quite nicely correlates with the
evolution of mobile telephony.

2. II. REVIEW
J. H. Holland, ‘Genetic algorithms and classifier systems:
Foundations and future directions’, 1987
This paper [1] comes some time after Holland’s pioneering
and authoritative book “Adaptation in Natural and Artificial
Systems” was published in 1975 in which he initiated the field
of study of Genetic Algorithms.
This paper theorizes questions about the characteristics of
Classifier Systems specifically and Adapative Non-Linear
Networks (ANN) generally and proposes extensions to the
functional properties of Classifier systems and the
requirement for a mathematical framework to allow the
further examination of ANNs, their capabilities and
effectiveness.
In this paper he suggests a Classifier system is an example
of an Adaptive Nonlinear Network (ANN). It is interesting to
note that ANNs subsequently came to be known as Artificial
Neural Networks. He defined Classifier Systems as being
processing units that interact in a competitive and non-linear
fashion indicating a level of adaptation to their environment.
These processing units can be modified by various operations
to suit the progressive adaptation needs of the environment.
As mentioned above, selection, crossover and mutation are
basic examples of these adaptation operators. In Genetic
Algorithms we have seen the encoding of parameters as bit
string or integer based “chromosomes” to facilitate the
exchange of information between candidate solutions to
derive new candidate solutions for future generations or
iterations. Within ANNs he calls these “chromosomes” or
generic message processing rules “classifiers” and units of
classifier systems.
Genetic Algorithms by their definition of a population are
inherently a parallel processing system and Classifier Systems
extend that paradigm by allowing interaction between
individuals in the population via standardized messaging.
Holland references the ‘bucket brigade’ algorithm which
passes the outcome from each classifier in an ANN to the
previous classifier in the chain. He combined this approach
which was fraught with merely reinforcing existing solutions
with Genetic Algorithms to introduce diversity into the
solutions produced through selection, crossover and mutation.
Holland theorizes additional techniques for operators such
as parasitism, symbiosis, competitive exclusion as analogies
to biological evolution and whether they can be applied as
characteristics of Classifier Systems specifically or ANNs
generally. He suggests the development of a mathematical
framework based on combinatorics and competition between
parallel processes as being essential to the study of ANNs.
This mathematical model would allow for many analogues of
biological processes such as niche exploitation, phylogenetic
hierarchies, polymorphism and enforced diversity and many
others such as predator-prey and biased mating scenarios.
In summary this is a highly theoretical paper musing future
directions for his pioneering work in Genetic Algorithms and
Artificial Neural Networks and their application within
Machine Learning.
J. M. Johnson and Y. Rahmat-Samii, ‘Genetic algorithm
optimization of wireless communication networks’, 1995
This paper [2] considers the optimal placement of
transceiver nodes within a wireless network using Genetic
Algorithm optimisation. The wireless networks in question
have the topology of a data communications network
backbone for communication between fixed terminals in a
manner similar to that of the wireline Ethernet. The goal of the
optimization is to maximize the overall Quality of Service by
maximising the signal power as represented by the signal to
noise ratio (SNR) whilst minimizing the transmitter power
levels for selected locations of transceivers.
The authors identified the objective function as being
dependent on path length between transmitter and receiver and
although there is some analogy with the Travelling Salesman
Problem in terms of finding the best route between nodes in
the network there are additional constraints which make this
an NP hard problem i.e. not solvable in a time bounded by the
same size of polynomial in brute force fashion. These
additional constraints being the maximisation of the SNR and
minimisation of interference from other transceiver nodes.
This short paper illustrates how Genetic Algorithms can be
applied to optimising a wireless network topology in principle
including defining aspects of the objective function and
suggestion of how the variables can be encoded into
chromosomes. It does fall short however of providing specific
examples of the results of a typical optimisation, its level of
efficacy or a derived network topology using this method.
C. Maple, Liang Guo, and Jie Zhang, ‘Parallel Genetic
Algorithms for Third Generation Mobile Network
Planning’, 2004
In this paper [3] the authors note that in 3G networks the
evolution of the technology means that the signalling
employed in the Radio Access Network (RAN) has moved on
from TDMA based time division multiplexing of individual
frequency bands utilised by 2G networks to code symbol
spread spectrum usage in Code Division Multiple Access
(CDMA) signalling. This means that the frequency planning
involved in 1G and 2G networks is not required as the entire
frequency band is utilised for all users in the cell of signal
coverage. However, the critical elements of planning again
become the number of users per cell and the signal power
transmitted and inter-cell interference received.
With regard to resource optimisation the authors define an
objective function based on maximum number of users per
cell, which is proportional to information encoding rate,
Signal to Noise ratio and inversely proportional to intra-cell
interference along with more advanced radio signalling
characteristics. The radius of a radio network cell from the
antennae is defined as another objective function defining path
loss based on the signal propagation or indeed attenuation
depending on mast height and distance from the antenna.
The authors also note that another objective function is the
cost of infrastructure deployed to maximise the number of
users per call and the coverage, so it becomes a typical multi-
objective optimisation problem.
Genetic Algorithm selection processes are described
including roulette wheel, rank based selection and tournament
selection. The authors describe the Parallel Genetic Algorithm
that they used in their study which defined their fitness
function as a vector representing a combination of capacity,

3. coverage and the inverse of cost and encoding the
chromosomes as a 3 x n matrix where each column represents
a potential radio network base station location and each row
represents whether the base station is present, mast height and
transmission power. The authors noted that the significant
portion of the processing time was consumed by producing the
next generation and evaluating their fitness.
In conclusion this paper again presents a theoretical
approach to the problem without presenting any results in
terms of a derived optimal network topology or quantifying
the effort and efficiency gained through the use of the
optimisation process, but the approach is clear and lucid
regarding its methodology and very well considered.
R. Sachan, T. J. Choi, and C. W. Ahn, ‘A Genetic Algorithm
with Location Intelligence Method for Energy Optimization
in 5G Wireless Networks’, 2016
As the demand for higher and higher bitrates, well into the
Gigabit per Second range, for mobile data services grows due
the popularity of video streaming and interactive gaming, the
frequency bandwidth required for transmission grows into the
high GHz range and so does signal attenuation over distance
which translates to smaller and smaller cells of coverage. In
2G mobile network technology, the cells spanned kilometres,
in 5G this now measures in tens of metres and whereas 2G
radio signal frequencies could penetrate buildings, walls etc.
the very high frequency 5G signals cannot thus requiring more
antennae to achieve coverage to the expected Quality of
Service especially in urban areas. Radio signal coverage is a
complex function of distance from mobile device to radio
network base station incorporating the antennae, the number
of users per cell, the power output of the mobile device and
the antennae and the signal to noise and interference ratio
(SINR) and many other factors. This additional infrastructure
introduces additional cost of infrastructure and power
consumption along with constraints on the availability of sites
where antennae can be located. This represents a classic NP
hard optimisation problem which has garnered attention from
researchers into the applicability of genetic algorithms into
providing optimal solutions.
This 2016 paper [4] brings us right up to date with regard
to mobile network technology. The authors noted a modern
topical concern regarding energy conservation as being a
prime motivation factor in any resource allocation
optimisation study as the base stations in the Radio Access
Network are responsible for 50% of the energy expended in
the overall cellular network.
Their approach otherwise follows the previous papers
quite closely albeit at a more sophisticated level. They use
Real Coded Genetic Algorithms (RGA) using integer
encoding for the power output of the transmitter and the x and
y co-ordinates of the available physical locations for the
antennae placement. Their system model consisted of a
population of 100 individuals – each individual’s
chromosome represents up to 100 base station’s decision
variables represented by the three variables mentioned above
i.e. power, x and y location. The fitness function is quite
progressive compared to the thinking of previous generations
in that fitness is proportional to the number of users squared
and inversely proportional to the transmitter power and the
square of the number of base stations thus emphasising that
power consumption and infrastructure costs (due to the high
number of antennae required for 5G networks as previous
described) must be minimised.
Their implementation achieved better results than
unmodified RGA due to customisations to the crossover
mechanism. They developed a technique called Base Station
Crossover Rate (BCR) using a Box Crossover technique
which limited the production of offspring to a lower and upper
bounded region within the solution space. The authors found
from experimentation that this mechanism outperformed
standard RGA which could not converge to an optimum
solution due to the wholesale shuffle of chromosomes
between parents and offspring. When this was limited by BCR
much better results were produced.
The optimisations took the form of 50 independent runs
with a population size of 100 and allowing up to 200
generations. The authors disclosed all their parameters and
their value ranges and pseudocode for their GA
implementations and graphs of their results illustrating how
their implementation outperformed standard RGA and
Differential Evolution (DE) algorithms. All in all, it represents
a very high quality study of mobile network resource
allocation optimisation in the current state of the art mobile
network technology with its distinct topological requirements
regarding cell size and multiplicity of antennae locations.
III. CONCLUSIONS
In this paper, the author has described foundational
information on what represents a heuristic algorithm and how
metaheuristics applies that class of algorithm to optimisation
problems through to the development of Genetic Algorithms
taking the principles of recombination from biology to
produce iterative generations of prospective solutions that can
be applied to modern problems in telecommunications like
efficient resource allocation of radio network transceivers in
cellular data networks supporting mobile device data services
with high data demands like 5G. 5G is currently in the early
stages of global rollout and in the current world situation key
requirements of minimising costs and energy consumption yet
providing the best Quality of Service for the most users per
cell presents an ideal opportunity for the application of
optimisation techniques such as Genetic Algorithms.
IV. BIBLIOGRAPHY
[1] J. H. Holland, ‘Genetic algorithms and classifier systems: Foundations
and future directions’, Michigan Univ., Ann Arbor (USA), LA-UR-87-
1863; CONF-870775-1, Jan. 1987. Accessed: May 06, 2020. [Online].
Available: https://www.osti.gov/biblio/6277983.
[2] J. M. Johnson and Y. Rahmat-Samii, ‘Genetic algorithm optimization
of wireless communication networks’, in IEEE Antennas and
Propagation Society International Symposium. 1995 Digest, Jun. 1995,
vol. 4, pp. 1964–1967 vol.4, doi: 10.1109/APS.1995.530977.
[3] C. Maple, Liang Guo, and Jie Zhang, ‘Parallel Genetic Algorithms for
Third Generation Mobile Network Planning’, in International
Conference on Parallel Computing in Electrical Engineering, 2004,
Sep. 2004, pp. 229–236, doi: 10.1109/PCEE.2004.51.
[4] R. Sachan, T. J. Choi, and C. W. Ahn, ‘A Genetic Algorithm with
Location Intelligence Method for Energy Optimization in 5G Wireless
Networks’, 2016, doi: 10.1155/2016/5348203.