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. Potter et al., "MIMO Beam-Forming with Neural Network Channel Prediction Trained by a Novel PSO-EA-DEPSO Algorithm," Proceedings of the IEEE International Joint conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence), Institute of Electrical and Electronics Engineers (IEEE), Jun 2008.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2008.4634272
IEEE International Joint conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence)
Electrical and Computer Engineering
Keywords and Phrases
Differential Evolution; Evolutionary Algorithm; Particle Swarm Optimization; Recurrent Neural Network
Article - Conference proceedings
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