A Critical overview on the recent advances in channel allocation strategies for voice and multimedia services in wireless communication systems and the Applicability of Computational Intelligence Techniques (original) (raw)

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.

Application of an Optimization Algorithm for Channel Assignment in Mobile Communication

The channel assignment problem is a complex problem where a minimum number of channels have to be assigned, under several constraints, to the calls requested in the cellular system. Several approaches have been proposed to solve the dynamic channel assignment (DCA). In this paper, DCA has been modeled as a combinatorial optimization problem. Genetic Algorithm (GA) is a simple tool that can be used to solve such optimization problems in a fast and effective manner. It selects the best option from all the possible solutions, thus making it very different from all the other existing approaches. Several constraints like cochannel and adjacent channel interferences have been considered while solving the channel assignment problem. The performance of the proposed GA-DCA model has been evaluated by a computer simulation tool under the effective of varying cellular capacity.

APPLICATION OF GENETIC ALGORITHM IN CHANNEL ASSIGNMENT FOR WIRELESS COMMUNICATION

The application of Genetic Algorithm (GA) in channel allocation, for wireless mobile communication, is described in this paper. The investigations reported in this paper suggest that for a particular number of users in a cell, which scheme is better so that we get lesser number of Blocking Probability with minimum number of channel assignment in Fixed Channel Assignment (FCA) and Dynamic Channel Assignment (DCA) schemes.

A three-stage heuristic combined genetic algorithm strategy to the channel-assignment problem

2003

The Channel Assignment Problem (CAP) is to assign a minimum number of channels to requested calls in a cellular radio system while satisfying certain constraints. It is proven to be NP-complete. Considering Sivarajan's benchmark 21-cell system assignment problem, or the Philadelphia problem, the constraints and traffic demands are given, a lower bound of channels needed for this system is fixed, and many strategies have been provided to solve the problem. This paper presents a dynamic channel assignment algorithm consisting of three stages: 1) the determine-lower-bound cell regular interval assignment stage; 2) the greedy region assignment stage; and 3) the genetic algorithm assignment stage. Its performance is verified through the Philadelphia problem and achieves lower bound solutions on 11 of the 13 instances, which is comparable with existing algorithms . The algorithm also has the advantage that it is able to find optimum solutions faster than approaches using neural-networks and simulated annealing.

Genetic Algorithm for Resource Allocation in WiMAX Network

International Journal of Computer Applications, 2011

WiMAX offer a very demanding multiuser communication problem. To make resource allocation more practical, in mobile WiMAX subchannelization is used. The Resource Allocation is usually formulated as a constrained optimization problem for either fixed-rate applications such as voice or for variable rate applications such as data. In this paper we address Genetic Algorithm for Resource Allocation in downlink Mobile WiMAX networks. Objective of proposed algorithm is to optimize total throughput. Simulation results show that Genetic Algorithm performs better than methods proposed in in terms of higher capacities.

Using a genetic algorithm approach to solve the dynamic channel-assignment problem

International Journal of Mobile Communications, 2006

The Channel Assignment Problem is an NP-complete problem to assign a minimum number of channels under certain constraints to requested calls in a cellular radio system. Examples of the many approaches to solve this problem include using neural-networks, simulated annealing, graph colouring, genetic algorithms, and heuristic searches. We present a new heuristic algorithm that consists of three stages: 1) determine-lower-bound cell regular interval assignment; 2) greedy region assignment; and 3) genetic algorithm assignment. Through simulation, we show that our heuristic algorithm achieves lower bound solutions for 11 of the 13 instances of the well known Philadelphia benchmark problem. Our algorithm also has the advantage of being able to find optimum solutions faster than existing approaches that use neural networks.

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.

Issues, challenges and problems in channel allocation in cellular system

Cellular communication has changed the dimensions of communication by offering the ease of ubiquity to the users. The bandwidth available for a cellular system is always limited. With the increase in multimedia applications and ever increasing demand of wireless devices supporting higher data rate flow, cellular systems, in general, always face scarcity of channels. In cellular systems, utilization of channels has been one of the very important areas of research along with power management, location tracking etc. Efficient and effective allocation of channels for cellular networks has become an extremely important topic of recent research. The different types of emerging services require real time support and uninterrupted communication, having more strict QoS requirements. In this paper we have explored different issues and challenges involved in channel allocations in cellular systems and also highlighted some of the channel allocation schemes proposed in the literature.

A Modified Genetic Algorithm for Resource Allocation in Cognitive Radio Networks

International Conference on Science and Technology (ICST), 2018

Cognitive radio network (CRN) has a capability to sense the conditions of their operating environment. However, the resources allocation scheme is still inefficient because it is generated randomly and can lead to interference among the users. In this paper, we propose a modified genetic algorithm as a method for resource allocation in the cognitive radio network. In this work, the chromosome represents the channel interface index. The objective is to find the optimal resource allocation scheme of the nodes in the network in order to minimize the interference and maximize the network throughput. We have modified an encoding scheme and the fitness function of GA to assign the best channel combination of the cognitive radio network. The simulation results showed that the allocation channel using modified GA is capable of improving the network throughput.

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.