Title: Machine learning of irreducible error floor in the space-time trellis code

Authors: Ungku Azmi Iskandar Ungku Chulan; Mardina Abdullah; Nor Fadzillah Abdullah; Abdullah Ramli

Addresses: Faculty of Engineering and Built Science, National University of Malaysia (UKM), Bangi, 43650, Malaysia ' Faculty of Engineering and Built Science, National University of Malaysia (UKM), Bangi, 43650, Malaysia ' Faculty of Engineering and Built Science, National University of Malaysia (UKM), Bangi, 43650, Malaysia ' Faculty of Information Management, Technology University of MARA (UiTM), Bandar Puncak Alam, 42300, Malaysia

Abstract: The phenomenon of irreducible error floor in the space-time trellis code (STTC) is not fully understood. This comes from the fact that the connection between the trellis structure of the generator matrix G and the instigation of an irreducible error floor is uncertain. Given this difficulty, the present study attempts to gain a better insight into the ordeal via a data-driven approach. The classification and regression trees (CART) machine learning model is employed to predict the occurrence of the irreducible error floor from the trellis structure. Further analysis of the combinatorial characterisation of the trellis structure unveils a series of dominant patterns that consistently instigate the irreducible error floor. Furthermore, simulation also reveals that the codewords within the 'initial state' of the trellis structure are primal in the occurrence of the irreducible error floor. CART can achieve approximately 0.92 accuracy in predicting the irreducible error floor, with an average prediction time of 0.3833 μs.

Keywords: STTC; space-time trellis code; irreducible error floor; combinatorial characterisation; machine learning; low density parity check code; coding scheme; trellis structure.

DOI: 10.1504/IJIEI.2022.128875

International Journal of Intelligent Engineering Informatics, 2022 Vol.10 No.4, pp.290 - 312

Received: 27 Nov 2021
Received in revised form: 13 Jun 2022
Accepted: 13 Jun 2022
Published online: 08 Feb 2023 *

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