RSSI Estimation for Constrained Indoor Wireless Networks using ANN

• URL: http://arxiv.org/abs/2404.15337v1
• Date: Wed, 10 Apr 2024 02:48:13 GMT
• Title: RSSI Estimation for Constrained Indoor Wireless Networks using ANN
• Authors: Samrah Arif, M. Arif Khan, Sabih Ur Rehman,
• Abstract summary: This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN) Both models have been constructed to enhance LP-IoT communication by lowering the estimation error in the LP-IoT wireless channel. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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