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Article Information:
Hybrid Genetic Crossover Based Swarm Intelligence Optimization for Efficient Resource Allocation in MIMO OFDM Systems
B. Sathish Kumar and K.R. Shankar Kumar
Corresponding Author: B. Sathish Kumar
Submitted: April 22, 2014
Accepted: May 25, 2014
Published: July 10, 2015 |
Abstract:
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Rapid development of wireless services, leads to ubiquitous personal connectivity in the world. The demand for multimedia interactivity is higher in the world which leads to the requirement of high data transmission rate. Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a future wireless service which is used to overcome the existing service problems such as development of subscriber pool and higher throughput per user. Although it overcomes the problems in existing services, resource allocation becomes one of the major issues in the MIMO-OFDM systems. Resource allocation in MIMO-OFDM is the optimization of subcarrier and power allocation for the user. The overall performance of the system can be improved only with the efficient resource allocation approach. The user data rate is increased by efficient allocation of the subcarrier and power allocation for each user at the base station, which is subject to constraints on total power and bit error rate. In this study, the problem of resource allocation in MIMO-OFDM system is tackled using hybrid artificial bee colony optimization algorithm based on a crossover operation along with Poisson-Jensen in equation. The experimental results show that the proposed methodology is better than the existing techniques.
Key words: Genetic crossover operator, hybrid artificial bee colony optimization, multiple-input multiple-output, orthogonal frequency division multiplexing, swarm intelligence, ,
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Cite this Reference:
B. Sathish Kumar and K.R. Shankar Kumar, . Hybrid Genetic Crossover Based Swarm Intelligence Optimization for Efficient Resource Allocation in MIMO OFDM Systems . Research Journal of Applied Sciences, Engineering and Technology, (7): 742-749.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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