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

arXiv: https://arxiv.org/abs/2001.00342 · HTML: https://mlsanity.com/paper/2001.00342/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection](https://mlsanity.com/paper/2601.16586/) (2026)

## Abstract (arXiv)

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.