@conference{491,
  keywords = {Channel State Information, Deep learning, Multi-Layer Perceptron, Long-Term Evolution},
  author = {Andrew Oosthuizen and Marelie Davel and Albert Helberg},
  title = {Multi-Layer Perceptron for Channel State Information Estimation: Design Considerations},
  abstract = {The accurate estimation of channel state information (CSI) is an important aspect of wireless communications. In this paper, a multi-layer perceptron (MLP) is developed as a CSI estimator in long-term evolution (LTE) transmission conditions. The representation of the CSI data is investigated in conjunction with batch normalisation and the representational ability of MLPs. It is found that discontinuities in the representational feature space can cripple an MLP’s ability to accurately predict CSI when noise is present. Different ways in which to mitigate this effect are analysed and a solution developed, initially in the context of channels that are only affected by additive white
Guassian noise. The developed architecture is then applied to more complex channels with various delay profiles and Doppler spread. The performance of the proposed MLP is shown to be comparable with LTE minimum mean squared error (MMSE), and to outperform least square (LS) estimation over a range of channel conditions.},
  year = {2022},
  journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC)},
  chapter = {94 - 99},
  month = {08/2022},
  address = {Fancourt, George},
  language = {English},
}