In the rapidly growing development of the Internet of Things (IoT)
infrastructure, achieving reliable wireless communication is a challenge. IoT
devices operate in diverse environments with common signal interference and
fluctuating channel conditions. Accurate channel estimation helps adapt the
transmission strategies to current conditions, ensuring reliable communication.
Traditional methods, such as Least Squares (LS) and Minimum Mean Squared Error
(MMSE) estimation techniques, often struggle to adapt to the diverse and
complex environments typical of IoT networks. This research article delves into
the potential of Deep Learning (DL) to enhance channel estimation, focusing on
the Received Signal Strength Indicator (RSSI) metric - a critical yet
challenging aspect due to its susceptibility to noise and environmental
factors. This paper presents two Fully Connected Neural Networks (FCNNs)-based
Low Power (LP-IoT) channel estimation models, leveraging RSSI for accurate
channel estimation in LP-IoT communication. Our Model A exhibits a remarkable
99.02% reduction in Mean Squared Error (MSE), and Model B demonstrates a
notable 90.03% MSE reduction compared to the benchmarks set by current studies.
Additionally, the comparative studies of our model A with other DL-based
techniques show significant efficiency in our estimation models.

该研究论文探讨了深度学习在低功耗物联网通信中利用接收信号强度指示器（RSSI）进行准确信道估计的潜力，通过基于全连接神经网络（FCNN）的两个模型（A 和 B），它们分别实现了 99.02% 和 90.03% 的均方误差（MSE）减少。与其他深度学习技术相比，我们的估计模型表现出显著的效率优势。

室内无线信道估计的深度学习方法与低功耗通信

Deep learning approaches to indoor wireless channel estimation for  low-power communication

With the emerge of the Internet of Things (IoT), localization within indoor
environments has become inevitable and has attracted a great deal of attention
in recent years. Several efforts have been made to cope with the challenges of
accurate positioning systems in the presence of signal interference. In this
paper, we propose a novel deep learning approach through Gradient Boosting
Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network
(AugBoost-ANN) for indoor localization applications as it trains over labeled
data. For this purpose, we propose an IoT architecture using a star network
topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth
Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in
an indoor environment. The dataset for the experiments is gathered in the real
world in different periods to match the real environments. Next, we address the
challenges of the AugBoost-ANN training which augments features in each
iteration of making a decision tree using a deep neural network and the
transfer learning technique. Experimental results show more than 8\%
improvement in terms of accuracy in comparison with the existing gradient
boosting and deep learning methods recently proposed in the literature, and our
proposed model acquires a mean location accuracy of 0.77 m.

本研究基于物联网采用星形网络连通拓扑模型结合深度学习方法提出了一种新的基于梯度提升算法增强的 Step-Wise 特征增强的人工神经网络 (indoor localization) 方法，通过采集蓝牙低功耗模块的接收信号强度指示器 (RSSI) 来建立一套数据集，然后使用深度神经网络和转移学习技术进行决策树的特征增强。实验结果表明，相对于现有的梯度提升和深度学习方法，本文方法的定位精度提高了 8% 以上，平均定位误差为 0.77 米。

使用深度学习增强特征增益的梯度提升室内定位

Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning

Multiple antenna techniques that allow energy beamforming have been looked
upon as a possible candidate for increasing the transfer efficiency between the
energy transmitter (ET) and the energy receiver (ER) in wireless power
transfer. This paper introduces a novel scheme that facilitates energy
beamforming by utilizing Received Signal Strength Indicator (RSSI) values to
estimate the channel. Firstly, in the training stage, the ET will transmit
using each beamforming vector in a codebook, which is pre-defined using a
Cramer-Rao lower bound analysis. RSSI value corresponding to each beamforming
vector is fed back to the ET, and these values are used to estimate the channel
through a maximum likelihood analysis. The results that are obtained are
remarkably simple, requires minimal processing, and can be easily implemented.
The paper also validates the analytical results numerically, as well as
experimentally, and it is shown that the proposed method achieves impressive
results.

本文提出了一种利用 RSSI 值进行通道估计的新方案，该方案使用预定义的码本和 Cramer-Rao 下限分析，其训练阶段和推断阶段非常简单，需要的处理较少，易于实现，并且通过数值验证表明其可以实现良好结果的能源波束成形技术。