A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received signal-to-noise ratio is derived for un-coded and beam-forming MIMO systems to see how the RNN error affects the performance. This error is shown to deteriorate the accuracy of the weak singular modes, making beam-forming more desirable. Bit error rate simulations are performed to validate these results.
C. G. Potter et al., "MIMO Beam-Forming with Neural Network Channel Prediction Trained by a Novel PSO-EA-DEPSO Algorithm," Proceedings of the 2008 IEEE International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence (2008, Hong Kong, China), pp. 3338 - 3344, Institute of Electrical and Electronics Engineers (IEEE), Jun 2008.
The definitive version is available at https://doi.org/10.1109/IJCNN.2008.4634272
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IJCNN 2008 (2008: Jun. 1-8, Hong Kong, China)
Electrical and Computer Engineering
Keywords and Phrases
Differential Evolution; Evolutionary Algorithm; Particle Swarm Optimization; Recurrent Neural Network
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jun 2008