Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Self-organizing maps
Nonlinear prediction of mobile radio channels: measurements and MARS model designs
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Computationally efficient bandwidth allocation and power control for OFDMA
IEEE Transactions on Wireless Communications
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Power prediction in mobile communication systems using an optimal neural-network structure
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
An evolutionary approach to velocity and traffic sensitive call admission control
Intelligent Decision Technologies
Automatic call management in a cellular mobile network by fuzzy threshold logic
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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Channel prediction is the key requirement in adaptive transmission techniques such as adaptive modulation, adaptive coding and adaptive power control. This paper presents a novel self organizing map (SOM) based channel predictor for the downlink of an orthogonal frequency-division multiple access (OFDMA) system. The proposed predictor uses a Kalman trained-SOM backed mixtures of experts (ME) modular neural network. The performance of the predictor is evaluated on an OFDMA system with a system delay where a channel prediction is needed.