A realistic performance assessment of any wireless technology requires the
use of a channel model that reflects its main characteristics. The independent
and identically distributed Rayleigh fading channel model has been (and still
is) the basis of most theoretical research on multiple antenna technologies in
scattering environments. This letter shows that such a model is not physically
appearing when using a reconfigurable intelligent surface (RIS) with
rectangular geometry and provides an alternative physically feasible Rayleigh
fading model that can be used as a baseline when evaluating RIS-aided
communications. The model is used to revisit the basic RIS properties, e.g.,
the rank of spatial correlation matrices and channel hardening.

本文评估了使用可重构智能表面（RIS）时，独立同分布的瑞利衰落信道模型不再是基于散射环境中的多天线技术的理论研究的基础，并提出了一种可用于评估 RIS 辅助通信的可行的瑞利衰落模型。该模型用于重新审视 RIS 的基本属性，例如，空间相关矩阵的秩和信道硬化。

可重构智能表面的瑞利衰落建模和信道硬化

Rayleigh Fading Modeling and Channel Hardening for Reconfigurable  Intelligent Surfaces

We study efficient deep learning training algorithms that process received
wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available.
We focus on two tasks that facilitate source identification: 1- Identifying the
modulation type, 2- Identifying the wireless technology and channel in the 2.4
GHz ISM band. For benchmarking, we rely on recent literature on testing deep
learning algorithms against two well-known datasets. We first demonstrate that
using training data corresponding only to the test SNR value leads to dramatic
reductions in training time while incurring a small loss in average test
accuracy, as it improves the accuracy for low SNR values. Further, we show that
an erroneous test SNR estimate with a small positive offset is better for
training than another having the same error magnitude with a negative offset.
Secondly, we introduce a greedy training SNR Boosting algorithm that leads to
uniform improvement in accuracy across all tested SNR values, while using a
small subset of training SNR values at each test SNR. Finally, we demonstrate
the potential of bootstrap aggregating (Bagging) based on training SNR values
to improve generalization at low test SNR values with scarcity of training
data.

本研究旨在探讨利用深度学习处理接收到的无线信号，并通过测试信噪比估计实现源识别，主要集中在识别调制类型和无线技术以及信道。研究表明通过利用测试信噪比对应的训练数据进行训练能够显著减少训练时间而保留一定的平均测试准确度，同时，以小正偏移为误差的测试信噪比估计比以小负偏移为误差的估计更有利于训练。另外，我们提出了一种贪婪的训练信噪比提升算法，可在使用每个测试信噪比的小部分训练信噪比的情况下，在所有测试信噪比的准确度上均匀提高。最后，通过基于训练信噪比值的自举聚集算法来改善具有训练数据紧缺的低测试信噪比值的泛化能力的潜力得到了验证。