Evolutionary Neural Network Model For Dynamic Channel Allocation In Mobile Communication Network (original) (raw)
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Channel allocation scheme for cellular networks using evolutionary computing
International Journal of Artificial Intelligence and Soft Computing, 2014
The usage of mobile communications systems has grown exponentially. But, the bandwidth available for mobile communications is finite. Hence, there is a desperate attempt to optimise the channel assignment schemes. In this work, some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls, priority, time of calls and mean opinion score are considered. Genetic algorithm and artificial neural networks is used to determine the optimal channel assignment considering the quality of service parameters. The simulation results show that genetic algorithm performs better than frequency assignment at random, a heuristic method. But application of artificial neural networks outperforms genetic algorithm and frequency assignment at random method by a considerable margin. Channel allocation can be optimised using these soft computing techniques resulting in better throughput.
An Evolutionary Approach to Allocate Frequency in Cellular Telephone System
International Journal of Computer Applications, 2010
This paper presents an evolutionary approach (genetic algorithm) to allocate frequencies in the cells of cellular network. In cellular telephone system, each cellular area is divided into small regions called cells. Each cell uses a unique set of frequencies. There is limited frequency so the frequency needs to be reuse. The Frequency allocation problem states that given any area separated into cells are allocated frequencies in such a way that no neighbor cells could have the same frequency... Since the problem looks very simple but as the number of cells is increased it becomes very complex and becomes NP-Complete problem. To find the solution of this problem, we have explored the use of genetic algorithm where possible solutions are improved generation by generation and there is more probability to find the exact solution.. Fitness function is developed which is the backbone of the concept of genetic algorithm and directly affects the performance; since this is NP problem and traditional heuristics have had only limited success in solving small to mid size problems. In this paper we have tried to show that genetic algorithm is an alternative solution for this NP problem where conventional deterministic methods are not able to provide the optimal solution.
Two methods of neural network controlled dynamic channel allocation for mobile radio systems
2002
Two methods of dynamic chmiel allocation using neural networks are investigated. Both methods continuously optimize the mobile network based on changes in calling traffic. The first method uses backpropagation model predictions to aid the channel allocator. Each cell contains a backpropagation model which provides the channel allocator a call traffic prediction allowing the channel allocator to effectively optimize the network. The second method uses the same backpropagation models along with actor-critic models to perform the channel allocation. The actor-critics learn to model traffic activity between adjacent cells real-time, and thereby learn to allocate channels dynamically between cells. The learning criterion is to minimize the number of subscribers lost from each cell. A comparison shows that both methods significantly outperform fixed channel allocation, even when the call traffic activity deviates from the praviously learned models of the call traffic activity. The implementation and continual adaptation characteristics are illustrated and discussed.
Evolutionary Neural Networks algorithm for the Dynamic Frequency Assignment Problem
International Journal of Computer Science and Information Technology, 2011
Wireless communication is used in many different situations such as mobile telephony, radio and TV broadcasting, satellite communication, wireless LANs, and military operations. In each of these situations a frequency assignment problem arises with application-specific characteristics. Researchers have developed different modelling ideas for each of the features of the problem, such as the handling of interference among radio signals, the availability of frequencies, and the optimization criterion. This paper presents a new approach for solving the problem of frequency allocation based on using initially a partial solution respecting all constraints according to a greedy algorithm. This partial solution is then used for the construction of our stimulation in the form of a neural network. In a second step, the approach will use searching techniques used in conjunction with iterative algorithms for the optimization of the parameters and topology of the network. The iterative algorithms used are named hierarchical genetic algorithms (HGA). Our approach has been tested on standard benchmark problems called Philadelphia problems of frequency assignment. The results obtained are equivalent to those of current methods. Moreover, our approach shows more efficiency in terms of flexibility and autonomy.
Evolutionary Optimization for the Channel Assignment Problem in Wireless Mobile Network
3rd CUTSE International Conference (CUTSE)
The channel assignment problem in wireless mobile network is consist of the assignment of appropriate frequency spectrum channels to requested calls while satisfying the electromagnetic compatibility (EMC) constraint. However with the limited capacity of wireless mobile frequency spectrum, an effective channel assignment technique is important for resource management and to reduce the effect of the interference. Most of the existing channel assignment techniques are based on deterministic methods. In this paper, an adaptive channel assignment technique based on genetic algorithm (GA) is introduced. The most significant advantage of GA based optimization in channel assignment problem is its capability to handle both the reassignment of existing calls as well as the allocation of channel to a new call in an adaptive process to maximize the utility of the limited resources. The population size is adapted to the number of eligible channels for a particular cell upon new call arrivals in order to achieve reasonable convergence speed. The MATLAB simulation on a 49-cells network model for both uniform and nonuniform traffic demands showed that the average new incoming call blocking probability for the proposed channel optimization method is lower than the deterministic channel assignment methods.
2008
Channel allocation is one of the fundamental issues in wireless communications due to the fact that it determines how the available bandwidth will be managed. The limited channel availability and the increasing demands for advanced services such as real time video give to channel allocation strategies a special role. Plethora of channel allocation strategies have been proposed in the literature for supporting voice services as well as multimedia services. In large scale networks, where the traffic conditions can not be predicted, intelligent techniques have been applied such as genetic algorithms. This paper, presents a critical overview on the recent advances in the most important channel allocation strategies found in the literature with regards to voice and multimedia services. Moreover, a discussion for the existing channel allocation strategies and the applicability of intelligent techniques, based on the computational intelligence framework, is also presented.
An Evolutionary Algorithm for Channel Assignment Problem in Wireless Mobile Networks
2012
Abstract The channel assignment problem in wireless mobile network is the assignment of appropriate frequency spectrum to incoming calls while maintaining a satisfactory level of electromagnetic compatibility (EMC) constraints. An effective channel assignment strategy is important due to the limited capacity of frequency spectrum in wireless mobile network. Most of the existing channel assignment strategies are based on deterministic methods.
Merging gradual neural networks and Genetic algorithm for Dynamic Channel Assignment Problem
2012
Under this article, we offer a novel neural-network approach called gradual neural network (GNN) hybridized with a genetic algorithm for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The hard problem of frequency assignment problem in the mobile communication system is efficiently solved by GNN as the typical problem of this class. The goal of this problem is to minimize the electromagnetic compatibility constraints between transceivers by first, rearranging the frequency assignment so that they can accommodate the increasing demands and second, using a minimum number of frequencies. An optimal solution is sought to facilitate the subsequent addition of new links. The binary neural network achieves the constraint satisfaction with the help of genetic algorithm, in order to seek the cost optimization and the network topology. The capability of the GNN algorithm is demonstrated through solving...
Adaptive Hybrid Channel Assignment in Wireless Mobile Network via Genetic Algorithm
2011 11th International Conference on Hybrid Intelligent Systems (HIS)
In wireless mobile network, the challenge is to assign appropriate frequency spectrum channels to requested calls while maintaining a desirable level of electromagnetic compatibility (EMC) constraint. An effective channel assignment technique is important to improve the system capacity and to reduce the effect of the interference. Most of the existing channel assignment approaches are based on deterministic methods. In this paper, an adaptive hybrid channel assignment (HCA) technique based on genetic algorithm (GA) is proposed. The proposed GA based optimization in HCA scheme is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals in order to achieve faster convergence speed. Besides, the proposed approach can handle both the reassignment of existing calls as well as the allocation of channel to a new call in an adaptive process to maximize the utility of the limited frequency spectrum. The simulation for both uniform and nonuniform traffic distributions on a 49-cells network model show that the average new incoming call blocking or call dropping probability for the proposed hybrid channel optimization method is lower than the deterministic HCA methods.
Wireless Personal Communications, 2007
The third generation of mobile communication aims to transmit not only voice and text but also videos and multimedia data. Furthermore, in the future it is expected to involve web browsing, file transfer, and database access. This requires wireless cellular networks to efficiently support packet data traffic. Therefore, challenge in the design of wireless networks is to support both voice and packet data service of traffic with different QoS-parameters. On the other hand one aspect of this challenge is to develop an efficient scheme for assigning resources to new arriving calls or handoff of different traffic types. Since the blocking probability is one of the most important QoSparameters, the QoS of wireless cellular networks are often measured in terms of two probabilities, the first is the new call blocking probability that a new call cannot be satisfied because of the unavailability of a proper free channel, and the second is the handoff blocking probability that a proper free channel is not available when a mobile station (MS) wants to move into a neighboring cell. To meet this aspect of the challenge, this proposal proposes a new assignment scheme based on intelligent methodologies to utilize frequency spectrum efficiently and to reduce call blocking probabilities.