Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals
This addresses the problem of high transmission overhead in channel estimation for IRS-assisted wireless systems, offering a more efficient solution, though it is incremental as it builds on existing DL approaches with a federated twist.
The paper tackles channel estimation in IRS-assisted wireless systems by proposing a federated learning framework that reduces pilot signals and transmission overhead. It achieves approximately 60% fewer pilot signals and 12 times lower overhead compared to centralized learning, while maintaining performance close to it and lower error than state-of-the-art DL-based schemes.
Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration. To deal with these uncertainties, deep learning (DL) approaches have been proposed. Previous works consider centralized learning (CL) approach for model training, which entails the collection of the whole training dataset from the users at the base station (BS), hence introducing huge transmission overhead for data collection. To address this challenge, this paper proposes a federated learning (FL) framework to jointly estimate both direct and cascaded channels in IRS-assisted wireless systems. We design a single convolutional neural network trained on the local datasets of the users without sending them to the BS. We show that the proposed FL-based channel estimation approach requires approximately 60% fewer pilot signals and it exhibits 12 times lower transmission overhead than CL, while maintaining satisfactory performance close to CL. In addition, it provides lower estimation error than the state-of-the-art DL-based schemes.