RNN Based MIMO Channel Prediction

Abstract

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.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Beam-Forming; Evolutionary Algorithm (EA); Multiple-Input Multiple-Output (MIMO); Particle Swarm Optimization (PSO); Recurrent Neural Networks (RNN); MIMO systems

International Standard Serial Number (ISSN)

0165-1684

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2010 Elsevier, All rights reserved.

Publication Date

01 Feb 2010

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