论文标题
在大型MIMO系统中，学习辅助的深度路径预测
Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems
论文作者
论文摘要
在本文中，我们提出了一种针对大型多输入 - 数字输出系统的新型学习辅助球体解码（SD）方案，即基于深度路径预测的球体解码（DPP-SD）。在此方案中，我们采用神经网络（NN）来预测子树中``深''路径的最小指标，然后开始在SD中进行树搜索。为了降低NN的复杂性，我们采用了缩小尺寸的输入向量，而不是使用原始接收的信号和完整的通道矩阵。利用NN的输出，即预测的最小路径指标，以确定子树之间的搜索顺序，并优化初始搜索半径，这可能会降低SD的计算复杂性。为了进一步降低复杂性，还提出了基于预测的最小路径指标的早期终止方案。我们的仿真结果表明，与常规SD算法相比，提出的DPP-SD方案可显着降低计算复杂性，尽管实现了几乎最佳的性能。
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.
