Enhancing Signal Coverage and Performance for Router Placement Using Deep Learning Model

Predicting nodes localization position is Still challenging due to different abstacles and dynamic nature of nodes (Customer devices), This research focuses on optimizing router placement in Wi-Fi 11ax networks to enhance signal coverage and performance in indoor settings. Traditional approaches to router placement often rely on heuristic methods that may not fully exploit the dynamic nature of Wi-Fi environments. To address this challenge, we Discussed and Compare a novel approach that integrates deep learning with traditional optimization techniques. Specifically, we develop an LSTM Based improved Recurrent Neural Network (RNN) model tailored for spatial analysis of Received Signal Strength Indicator (RSSI) values obtained from multiple reference nodes. Unlike conventional RNN architectures, our model incorporates advanced techniques such as Long Short-Term Memory (LSTM) layers for capturing long-term dependencies and spatial correlations in Wi-Fi signal propagation. The improved RNN analyzes sequential RSSI data to understand spatial dynamics and predict optimal router placements. By leveraging LSTM layers, the model can effectively learn and adapt to changing network conditions and environmental factors in real-time. This adaptive capability enables the RNN to suggest dynamic adjustments in router locations, thereby optimizing signal coverage and network performance continuously. To validate our approach, extensive real-world testing is conducted using WiFi RSS Fingerprint Datasets of varying sizes and reference points. The results demonstrate significant improvements in signal coverage and performance metrics compared to traditional methods. Our method helps network managers and homeowners deploy routers optimally in a variety of interior contexts while also advancing the theoretical knowledge of spatial evaluation and optimize networks.