Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.

翻译：可靠性对于无线系统物理层至关重要，因为它对端到端性能具有决定性影响。然而，由于神经网络的"黑箱"特性，主流深度学习（DL）物理层算法的不确定性难以量化。这一局限性是阻碍其实际部署的主要障碍。本文试图量化一类重要的基于深度学习的信道估计器的不确定性。我们提出了一种高效的统计方法，仅基于接收导频即可对深度学习估计信道的均方误差进行盲预测，无需已知真实信道、信道先验分布或噪声统计量。该盲性能预测方法的复杂度较低，仅与天线数量呈线性关系。针对混合远场和近场路径的超大规模多输入多输出（UM-MIMO）信道估计的仿真结果验证了所提方法的准确性和效率。