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

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

Blog Article

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 here 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 equi-jec 6 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.