Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Computationally efficient bandwidth allocation and power control for OFDMA
IEEE Transactions on Wireless Communications
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Multiuser OFDM with adaptive subcarrier, bit, and power allocation
IEEE Journal on Selected Areas in Communications
Transmit power adaptation for multiuser OFDM systems
IEEE Journal on Selected Areas in Communications
Resource allocation in MU-OFDM cognitive radio systems with partial channel state information
EURASIP Journal on Wireless Communications and Networking
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Cognitive Radio (CR) is a promising technique for improving the spectrum efficiency in future wireless communication networks. In this paper, dynamic resource allocation in a Multiuser Orthogonal Frequency Division Multiplexing (MU-OFDM) based CR system is investigated. Dynamic resource allocation in MU-OFDM CR systems is a computationally complex combinatorial optimization problem. Memetic algorithms (MAs), which are hybrid evolutionary algorithms with local searches, have been shown to outperform traditional algorithms for many combinatorial optimization problems. However, the performance of MAs is highly dependent on the choice of the local search and evolutionary operators. This choice should be based on the characteristics of the problem at hand. Fitness landscape is an important technique for analyzing the behavior of combinatorial optimization problems. Based on fitness landscape analysis, appropriate local search and evolutionary operators are selected for the proposed MA. Simulation results show that the proposed memetic algorithm provides better performance than existing algorithms.