RSSI Estimation for Constrained Indoor Wireless Networks using ANN
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.
- Internet of Things (IoT) Overview, pages 1–35. Springer International Publishing, 2019.
- J-M. Kang. Deep learning-based robust channel estimation for MIMO IoT systems. IEEE Internet of Things Journal, pages 1–1, 2023.
- Artificial neural network based path loss prediction for wireless communication network. IEEE Access, 8:199523–199538, 2020.
- Efficient machine learning-enhanced channel estimation for OFDM systems. IEEE Access, 9, 07 2021.
- Deep learning based channel estimation for 5G and beyond. Journal of Duhok University, 26(2):502–514, 2023.
- MWP. Maduranga and R. Abeysekara. Supervised machine learning for RSSI-based indoor localisation in IoT applications. International Journal of Computer Applications, 183(3):26–32, 2021.
- Deep learning-based signal strength prediction using geographical images and expert knowledge. In GLOBECOM 2020-2020 IEEE Global Communications Conference, pages 1–6. IEEE, 2020.
- Highly accurate prediction of radio propagation using model classifier. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), pages 1–5. IEEE, 2019.
- Wireless channel estimation for low-power IoT devices using real-time data. IEEE Access, 12:17895–17914, 2024.
- N. Raj. Indoor RSSI prediction using machine learning for wireless networks. In 2021 International Conference on COMmunication Systems and NETworkS (COMSNETS), pages 372–374, 2021.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.