Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Multi-population Particle Swarm Optimizer and its Application to Blind Multichannel Estimation
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Particle swarm optimisation and high dimensional problem spaces
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
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Evolutionary algorithms, in particular particle swarm optimization (PSO), have recently received much attention. PSO has successfully been applied to a wide range of technical optimization problems, including channel estimation. However, most publications in the area of digital communications ignore improvements developed by the PSO community. In this paper, an overview of the original PSO is given as well as improvements that are generally applicable. An extension of PSO termed cooperative PSO (CPSO) is applied for MIMO channel estimation, providing faster convergence and, thus, lower overall complexity. Instead of determining the average iterations needed empirically, a method to calculate the maximum number of iterations is developed, which enables the evaluation of the complexity for a wide range of parameters. Knowledge of the required number of iterations is essential for a practical receiver design. A detailed discussion about the complexity of the PSO algorithm and a comparison to a conventional minimum mean squared error (MMSE) estimator are given. Furthermore, Monte Carlo simulations are provided to illustrate the MSE performance compared to an MMSE estimator.