标题： 基于学习的深度路径预测用于大规模MIMO系统的球面解码 显示英文标题

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

摘要： 本文中，我们提出了一种新颖的学习辅助球面解码（SD）方案，用于大规模多输入多输出系统，即基于深度路径预测的球面解码（DPP-SD）。 在该方案中，我们在SD的树搜索开始之前，使用神经网络（NN）来预测子树中的“深度”路径的最小度量。 为了降低NN的复杂度，我们采用了一个降维的输入向量，而不是使用原始接收信号和完整的信道矩阵。 NN的输出，即预测的最小路径度量，被用来确定子树之间的搜索顺序，以及优化初始搜索半径，这可能会减少SD的计算复杂度。 为了进一步降低复杂度，还提出了一种基于预测最小路径度量的早期终止方案。 我们的仿真结果表明，尽管实现了接近最优的性能，所提出的DPP-SD方案相比传统的SD算法显著降低了计算复杂度。 显示英文摘要

摘要： 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.