Abstract

The rapid growth of smart-city infrastructures has created an environment in which massive IoT deployments operated across dense, heterogeneous wireless networks. As device density increased, the communication channels have often experienced severe interference, unpredictable fading, and high noise levels that collectively limited estimation accuracy. Traditional estimation techniques relied on linear models that struggled to track the dynamic channel conditions of large-scale IoT environments. This scenario established the core problem: existing estimators have not maintained reliable performance when network density surged or when devices transmitted sporadic traffic. To address this, the study proposed an AI-driven channel estimation framework that has leveraged deep learning to extract latent channel characteristics from limited pilot signals. The method incorporated a hybrid convolutional–recurrent design that captured spatial variations while it tracked temporal fluctuations of each channel. The system also included an adaptive refinement block that has improved estimation accuracy when pilot contamination occurred. The architecture was trained with synthetic and real-world datasets that have represented typical smart-city IoT deployments, including traffic sensors, utility meters, and environmental monitoring nodes that operated under mixed mobility patterns. The evaluation demonstrates that the proposed framework consistently outperforms conventional estimators. The method achieves a 6.2% NMSE at 100 epochs compared with 10.4% for MMSE and 8.2% for CS, and reduces MAE to 4.0% compared with 7.2% for MMSE. Spectral efficiency increases to 6.9 bps/Hz, while pilot overhead is reduced by 25%, outperforming baseline methods. Computational time remains practical at 3.6 ms per batch, confirming that the AI-assisted estimation effectively enhances reliability and efficiency in large IoT smart-city deployments.

Authors

N. Sudhir Reddy¹, D. Thilagavathy²
Malla Reddy College of Engineering, India¹, Adhiyamaan College of Engineering, India²

Keywords

AI-based Channel Estimation, Massive IoT, Smart-City Networking, Deep Learning Model, Pilot Contamination Reduction