Matrix theory: a second course
Matrix theory: a second course
Mobile Communications Engineering
Mobile Communications Engineering
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Microwave Mobile Communications
Microwave Mobile Communications
Digital Filters and Signal Processing
Digital Filters and Signal Processing
Wireless Communications
Introduction to Space-Time Wireless Communications
Introduction to Space-Time Wireless Communications
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Capacity and power allocation for fading MIMO channels with channel estimation error
IEEE Transactions on Information Theory
Power prediction in mobile communication systems using an optimal neural-network structure
IEEE Transactions on Neural Networks
Decision feedback recurrent neural equalization with fast convergence rate
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
Expert Systems with Applications: An International Journal
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A new hybrid PSO-EA-DEPSO 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. This algorithm is shown to outperform RNN predictors trained off-line by PSO, EA, and DEPSO as well as a linear predictor trained by the Levinson-Durbin algorithm. To explore the effects of channel prediction error at the receiver, new expressions for the received SNR, array gain, and average probability of error are derived and analyzed. These expressions differ from previous results which assume the prediction error is Gaussian and/or independent of the true CSI. The array gain decays with increasing signal-to-noise ratio and is slightly larger for spatially correlated systems. As the prediction error increases in the non-saturation region, the coding gain decreases and the diversity gain remains unaffected.