Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems
This work addresses complexity reduction for large MIMO systems, offering an incremental improvement over conventional sphere decoding methods.
The paper tackles the high computational complexity of sphere decoding in large MIMO systems by proposing a deep path prediction-based scheme that uses a neural network to predict path metrics, which reduces complexity while maintaining near-optimal performance, with simulation results showing significant computational reduction.
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.