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

TL;DR

This paper explores deep learning models, specifically FCNNs, to improve indoor wireless channel estimation in low-power IoT devices, significantly reducing estimation errors compared to traditional methods.

Contribution

Introduces two novel FCNN-based channel estimation models for IoT, achieving substantial MSE reduction over existing techniques.

Findings

Model A reduces MSE by 99.02%

Model B reduces MSE by 90.03%

Models outperform traditional estimation methods

Abstract

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.