In the expanding field of the Internet of Things (IoT), wireless channel
estimation is a significant challenge. This is specifically true for low-power
IoT (LP-IoT) communication, where efficiency and accuracy are extremely
important. This research establishes two distinct LP-IoT wireless channel
estimation models using Artificial Neural Networks (ANN): a Feature-based ANN
model and a Sequence-based ANN model. Both models have been constructed to
enhance LP-IoT communication by lowering the estimation error in the LP-IoT
wireless channel. The Feature-based model aims to capture complex patterns of
measured Received Signal Strength Indicator (RSSI) data using environmental
characteristics. The Sequence-based approach utilises predetermined
categorisation techniques to estimate the RSSI sequence of specifically
selected environment characteristics. The findings demonstrate that our
suggested approaches attain remarkable precision in channel estimation, with an
improvement in MSE of $88.29\%$ of the Feature-based model and $97.46\%$ of the
Sequence-based model over existing research. Additionally, the comparative
analysis of these techniques with traditional and other Deep Learning
(DL)-based techniques also highlights the superior performance of our developed
models and their potential in real-world IoT applications.

在物联网（IoT）这个不断扩大的领域中，无线信道估计是一个重要的挑战，尤其是对于低功耗物联网（LP-IoT）通信来说，效率和准确性非常重要。本研究使用人工神经网络（ANN）建立了两个不同的 LP-IoT 无线信道估计模型：基于特征的 ANN 模型和基于序列的 ANN 模型。这两个模型都旨在通过降低 LP-IoT 无线信道的估计误差来提高 LP-IoT 通信。基于特征的模型旨在利用环境特征捕捉测得的接收信号强度指示器（RSSI）数据的复杂模式。基于序列的方法利用预先确定的分类技术来估计特定选择的环境特征的 RSSI 序列。研究结果表明，我们提出的方法在信道估计方面具有显著的精度，基于特征的模型的均方误差（MSE）改进了 88.29％，基于序列的模型改进了 97.46％，相较于现有研究。此外，与传统和其他基于深度学习（DL）的技术的比较分析还突显了我们开发的模型的卓越性能和在实际物联网应用中的潜力。