Deep Learning Empowered High Accuracy and Low Complexity Indoor Channel Prediction for Wireless Communication Systems

The rapid expansion of wireless communication mandates the development of efficient and computationally light solutions for base station deployment. Despite the manifold advantages of the latest 5G networks, challenges such as limited coverage and signal attenuation persist, underscoring the need for swift and precise estimation of base station coverage. To address this imperative, this paper introduces a deep learning-based indoor channel prediction model. Through comparative analysis with a traditional high-precision ray-tracing model, the paper showcases the computational prowess of the proposed deep-learning model. Once trained, it achieves an impressive 99.7% reduction in computation time while maintaining comparable accuracy to the ray-tracing model. The proposed method promises expedited and accurate evaluation of optimal base station placements, particularly beneficial for indoor networks such as 5G small cells or wireless LAN. By integrating into handheld platforms, users can swiftly input indoor layouts and base station locations to obtain signal coverage. Such integration not only reduces power consumption but also drives advancements in energy-efficient practices within telecommunication infrastructure.