Clustering-Based Pilot Overhead Reduction for Channel Estimation in Dynamic Wireless MIMO Systems

This paper proposes an adaptive framework for channel estimation that minimizes overhead by optimizing pilot design for dynamic wireless environments. Unlike traditional methods that often assume a static environment, this approach adapt to real-world systems where channel conditions vary significantly, leading to changes in the transmission channel model. As these fluctuations can significantly affect the performance of channel estimators, which are typically trained on a single, static channel model. The proposed framework offers a dynamic solution as it operates in two steps: first, it uses a reduced set of strategically placed pilots to identify the actual channel model, and then it applies a specialized, pre-trained network tailored for that specific model to perform channel estimation with fewer pilots. For reliable model identification, a pilot placement strategy using Concrete AutoEncoder (CAE) is employed. Once the channel model is identified, a Transformerbased network, fine-tuned to the detected model, performs the channel estimation by focusing on relevant model features, thus reducing pilot requirements. Simulation results show that this adaptive framework outperforms conventional methods that train a single estimator across various models, achieving more accurate channel estimation with lower pilot overhead.