Estimation is a critical component of synchronization in wireless and signal processing systems. There is a rich body of work on estimator derivation, optimization, and statistical characterization from analytic system models which are used pervasively today. We explore an alternative approach to building estimators which relies principally on approximate regression using large datasets and large computationally efficient artificial neural network models capable of learning non-linear function mappings which provide compact and accurate estimates. For single carrier PSK modulation, we explore the accuracy and computational complexity of such estimators compared with the current gold-standard analytically derived alternatives. We compare performance in various wireless operating conditions and consider the trade offs between the two different classes of systems. Our results show the learned estimators can provide improvements in areas such as short-time estimation and estimation under non-trivial real world channel conditions such as fading or other non-linear hardware or propagation effects.

通过利用大数据集和大型计算高效的人工神经网络模型进行近似回归的方法，提出了一种替代方案来构建估计器，并考察了相应精度和计算复杂度，结果表明该方法对于在各种无线操作条件下对于短时间估计和非线性硬件或传播效应估计等问题有着明显提升。

学习逼近神经网络估计无线信道状态信息