Guntu. Nooka Raju

Work place: JNTU-Kakinada, Andhra Pradesh, India

E-mail: vanka_raju@yahoo.com

Website:

Research Interests: Computational Science and Engineering, Engineering

Biography

Mr. Guntu. Nooka Raju received his B.E degree in Electronics and Communication Engineering in 1995 from Andhra University, M.Tech degree in Instrumentation and Control Systems in faculty of ECE from JNT University, Kakinada in 2005, and pursuing PhD from JNTU-Kakinada. He is interesting to do research work in the field of Wireless communications, Cellular and Mobile Communications. He is published Two journals and one IEEE conference.

Author Articles
Adaptive Multi User Detection for FD-MC-CDMA in Presence of CFO

By Guntu. Nooka Raju B.Prabhakara Rao

DOI: https://doi.org/10.5815/ijwmt.2014.04.04, Pub. Date: 1 Nov. 2014

The main targets of multi-carrier direct sequence code division multiple access (MC-DS-CDMA) mobile communication systems are to overcome the multi-path fading influences as well as the near-far effect and to increase its capacity. Different types of optimal and suboptimal multi-user detection schemes have been proposed and analyzed in literature. Unfortunately, most of them share the drawback of requiring an efficient practical solution. Genetic algorithm provides a more robust and efficient approach for solving complex real world problem such as multi user detection, but genetic algorithms are not computationally efficient. Computational complexity and performance of the genetic algorithms depends on number of generations and/or the population size, schemes involving genetic algorithms would compromise in computational complexity or performance. In this paper we propose adaptive population sizing genetic algorithm based multi user detection algorithm and compare its performance with existing multi user detection algorithms in various channels. Simulation results confirmed that the proposed adaptive genetic algorithm assisted multi user detection algorithm performs better compared to the existing multi user detection algorithms.

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