Summary of Deep Learning Approaches to Indoor Wireless Channel Estimation For Low-power Communication, by Samrah Arif et al.
Deep learning approaches to indoor wireless channel estimation for low-power communication
by Samrah Arif, Muhammad Arif Khan, Sabih Ur Rehman
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the application of Deep Learning (DL) for enhancing channel estimation in Internet of Things (IoT) infrastructure. Traditional methods like Least Squares (LS) and Minimum Mean Squared Error (MMSE) struggle to adapt to diverse environments common in IoT networks. The authors focus on using Received Signal Strength Indicator (RSSI) as a metric, which is susceptible to noise and environmental factors. Two Fully Connected Neural Networks (FCNNs)-based Low Power (LP-IoT) channel estimation models are proposed, leveraging RSSI for accurate channel estimation in LP-IoT communication. The models demonstrate significant improvements over benchmarks, with Model A achieving a 99.02% reduction in Mean Squared Error (MSE) and Model B showing a 90.03% MSE reduction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make wireless communication better for devices that connect to the Internet of Things (IoT). These devices can be affected by signal interference and changing conditions, which makes it hard to get accurate information about the channel. The authors use a new approach called Deep Learning to improve channel estimation. They create two models that are good at estimating channels using data from Received Signal Strength Indicator (RSSI) metrics. This helps ensure reliable communication in IoT networks. |
Keywords
» Artificial intelligence » Deep learning » Mse
