Genetic algorithms for optimal channel assignment in mobile communications
ABSTRACT Since the usable frequency spectrum is limited, optimal assignment of channels is becoming more and more important. It can greatly enhance the traffic capacity of a cellular system and decrease interference between calls, thereby improving service quality and customer satisfaction. In this paper, we use genetic algorithms (GA) to solve the problem of assigning calls in a cellular mobile network to frequency channels in such a way that interference between calls is minimized, while demands for channels are satisfied. This channel assignment problem is known to be a difficult optimization problem. Simulation results showed that the GA approach is able to further improve on the results obtained by other techniques.
- [Show abstract] [Hide abstract]
ABSTRACT: Channel allocation in wireless communication systems is one of the fundamental issues. The corresponding allocation schemes can not be static due to the dynamically changing traffic conditions and network performance. Thus, more sophisticated strategies adapted to current network conditions must be investigated and applied. Recently, various approaches have been proposed for channel allocation based on intelligent techniques such as multi-agent technology and genetic algorithms. These approaches constitute heuristic solutions to resource management problem. On the other hand, the ant colony optimization approach has been proposed for solving optimization problems but this approach has not been proposed so far for solving the channel allocation problem in wireless communication systems. In this paper, a comprehensive heuristic approach for solving the channel allocation problem based on intelligent techniques such as multi-agents and ant colony optimization is proposed. Moreover, important implementation issues such as thread execution sequence are also presented. Finally, the simulation results show the performance improvement of the proposed ant colony optimization algorithm as well as the multi-agent modeling approach.Intelligent Systems, 2008. IS '08. 4th International IEEE Conference; 10/2008
- [Show abstract] [Hide abstract]
ABSTRACT: Finding suitable channels to allocate in order to serve increasing user demands in a cellular network, which is a dynamical system, constitute the most important issue in terms of network performance since they define the bandwidth management methodology. In modern cellular networks these strategies become challenging issues especially when advanced services are applied. The effectiveness of decision making for channel allocation in a cellular network is strongly connected to current traffic and wireless environment conditions. Moreover, in large scale environments, network states change dynamically and the network performance prediction is a hard task. In the recent literature, the network adaptation to current real user needs seems it could be achieved through computational intelligence based channel allocation schemes mainly involving genetic algorithms. In this paper, a quite new approach for communication channels decision making, based on ant colony optimization, which is a special form of swarm intelligence, modelled through multi agent methodology is presented. The main novelty of this research lies on modelling this optimization scheme through multi agent systems. The simulation model architecture which includes network and ant agents are also presented as well as the performance results based on the above techniques. Finally, the current study, also, shows that there is a great field of research concerning intelligent techniques modelled through multi-agent methodologies focused on channels decision making and bandwidth management in wireless communication systems.Advances in Data Mining. Applications and Theoretical Aspects, 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20-22, 2009. Proceedings; 01/2009
- [Show abstract] [Hide abstract]
ABSTRACT: The mobile manipulator is a multivariable and non-linear system, so the research about the kinematics decoupling of mobile manipulator is important, especially the control methods based on neural network. To solve the deficiency of neural network such as decision of structure and adjustment of parameters in hidden-unit, genetic algorithm based on RBF neural network is presented to deal with kinematics decoupling of mobile manipulator. The centers and widths of hidden layer and the weights of the output layer are coded into one chromosome. It strengthens the cooperation between the hidden layer and the output layer, and avoids the risk of getting stuck into a local minimum. RBF neural network using genetic algorithm is established for kinematics decoupling which brought by coordinated motion between the manipulator and mobile platform of mobile robot system. The experimental results show the method reasonable and effective.Information Technology and Computer Science, International Conference on. 07/2009; 2:380-383.
Genetic Algorithms for Optimal Channel
Assignments in Mobile Communications
Lipo Wang*, Sa Li, Sokwei Cindy Lay, Wen Hsin Yu, and Chunru Wan
School of Electrical and Electronic Engineering
Nanyang Technological University
Block S2, Nanyang Avenue, Singapore 639798
* Corresponding author
Phone: +65 6790 6372
Fax: +65 6792 0415
The demand for mobile communication has been steadily increasing in recent years. With
the limited frequency spectrum, the problem of channel assignment becomes increasingly
important, i.e., how do we assign the calls to the available channels so that the
interference is minimized while the demand is met? This problem is known to belong to a
class of very difficult combinatorial optimization problems. In this paper, we apply the
formulation of Ngo and Li with genetic algorithms to ten benchmarking problems.
Interference-free solutions cannot be found for some of these problems; however, the
approach is able to minimize the interference significantly. The results demonstrate the
effectiveness of genetic algorithms in searching for optimal solutions in this complex
Keywords: genetic algorithms, channel assignment, mobile communications, wireless
As cellular phones become ubiquitous, there is a continuously growing demand for
mobile communication. The rate of increase in the popularity of mobile usage has far
outpaced the availability of the usable frequencies which are necessary for the
communication between mobile users and the base stations of cellular radio networks.
This restriction constitutes an important bottleneck for the capacity of mobile cellular
systems. Careful design of a network is necessary to ensure efficient use of the limited
One of the most important issues on the design of a cellular radio network is to determine
a spectrum-efficient and conflict-free allocation of channels among the cells while
satisfying both the traffic demand and the electromagnetic compatibility (EMC)
constraints. This is usually referred to as channel assignment or frequency assignment.
There are three types of constraints corresponding to 3 types of interference , namely:
1) Co-channel constraint (CCC)
• where the same channel cannot be assigned to certain pairs of radio cells
2) Adjacent channel constraint (ACC)
• where channels adjacent in the frequency spectrum cannot be assigned to
adjacent radio cells simultaneously
3) Co-site constraint (CSC)
• where channels assigned in the same radio cell must have a minimal
separation in frequency between each other.
One of the earlier aims of the channel assignment problem (CAP) is to assign the
required number of channels to each region in such a way that interference is precluded
and the frequency spectrum is used efficiently. This problem (called CAP1 in ) can be
shown to be equivalent to a graph coloring problem and is thus NP-hard.
As demand for mobile communications grows further, interference-free channel
assignments often do not exist for a given set of available frequencies. Minimizing
interference while satisfying demand within a given frequency spectrum is another type
of channel assignment problem (called CAP2 in ).
Over the recent years, several heuristic approaches have been used to solve various
channel assignment problems, including simulated annealing , neural networks
, and genetic algorithms -. In particular, ,- used GA for CAP1.
, , and  formulated CAP2; however, they were interested only in interference-
free situations.  gives a unique formulation of CAP2 in terms of GA; however, no
simulation results were presented. Ngo and Li  developed an effective GA-based
approach that obtains interference-free channel assignment by minimizing interference in
a mobile network. They demonstrated that their approach efficiently converges to
conflict-free solutions in a number of benchmarking problems.
In this paper, we apply Ngo and Li's approach to several benchmarking channel
assignment problems where interference-free solutions do not exist. The organization of
this paper is as follows. Section 2 states the channel assignment problem (CAP). Section
3 summarizes Ngo and Li's approach to solving CAP with genetic algorithms. Section 4
describes the tests carried out and results obtained, with many choices of parameters.
Finally, we conclude the paper in section 5.
2 CHANNEL ASSIGNMENT PROBLEM
The channel assignment problem arises in cellular telephone networks where discrete
frequency ranges within the available radio frequency spectrum, called channels, need to
be allocated to different geographical regions in order to minimize the total frequency
span, subject to demand and interference-free constraints (CAP1), or to minimize the
overall interference, subject to demand constraints (CAP2). In this paper, we are
interested in CAP2, since it is more relevant in practice compared to CAP1.
There are essentially two kinds of channel allocation schemes - Fixed Channel Allocation
(FCA) and Dynamic Channel Allocation (DCA). In FCA the channels are permanently
allocated to each cell, while in DCA the channels are allocated dynamically upon request.
DCA is desirable, but under heavy traffic load conditions FCA outperforms most known
DCA schemes. Since heavy traffic conditions are expected in future generations of
cellular networks, efficient FCA schemes become more important . The fixed
channel assignment problem, or in other words, assigning channels to regions in order to
minimize the interference generated has been shown to be a graph coloring problem and
is therefore NP-hard.
A cellular network is assumed to consist of N arbitrary cells and the number of channels
available is given by M. The channel requirements (expected traffic) for cell j are given
by Dj. Assume that the radio frequency (RF) propagation and the spatial density of the
expected traffic have already been calculated. The 3 types of constraints can be
determined. The electromagnetic compatibility (EMC) constraints, specified by the
minimum distance by which two channels must be separated in order that an acceptably
strong S/I ratio can be guaranteed within the regions to which the channels have been
assigned, can be represented by an N × N matrix called the compatibility matrix C.
In this matrix C:
• Each diagonal element Cii represents the co-site constraint (CSC), which is the
minimum separation distance between any two channels at cell i.