Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

doi:10.1109/ACCESS.2020.2987375

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Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

2020-01-01

Doyeon Weon

Kyungchun Lee *

DOI：10.1109/ACCESS.2020.2987375

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Abstract

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In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the deep paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.

Keywords:

Measurement

Artificial neural networks

MIMO communication

Maximum likelihood decoding

Computational complexity

MIMO

sphere decoding

tree search

machine learning

deep learning

neural network

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