论文标题
深度高网络的MIMO检测
Deep HyperNetwork-Based MIMO Detection
论文作者
论文摘要
多输入多输出（MIMO）系统的最佳符号检测已知是NP硬性问题。常规的启发式算法要么太复杂，无法实用，要么表现不佳。最近，几种方法试图通过将检测器作为深层神经网络实施来解决这些挑战。但是，他们要么在实用的空间相关通道上仍能实现不令人满意的性能，要么在计算上需要进行计算要求，因为它们需要为每个频道实现进行重新培训。在这项工作中，我们通过训练一个被称为HyperNetwork的其他神经网络（NN）来解决这两个问题，该网络将其视为输入通道矩阵并生成基于神经NN的检测器的权重。结果表明，所提出的方法在无需重新训练的情况下实现了几乎最先进的表现。
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Conventional heuristic algorithms are either too complex to be practical or suffer from poor performance. Recently, several approaches tried to address those challenges by implementing the detector as a deep neural network. However, they either still achieve unsatisfying performance on practical spatially correlated channels, or are computationally demanding since they require retraining for each channel realization. In this work, we address both issues by training an additional neural network (NN), referred to as the hypernetwork, which takes as input the channel matrix and generates the weights of the neural NN-based detector. Results show that the proposed approach achieves near state-of-the-art performance without the need for re-training.
