Work

Region-specific reliable channel estimation in RIS-enabled wireless communications via clustered federated learning

Year: 2025

Type: article

Abstract: Machine learning (ML)-based downlink channel estimation for reconfigurable intelligent surface (RIS)-assisted communication faces challenges such as handling channel variations, high communication overhead of centralized learning (CL), and vulnerability to malicious users. We propose a novel approach integrating blockchain to enhance security by verifying registered users, autoencoder (AE)-based clustering to identify regions within the cell, and clustered federated learning (CFL) to ensure good channel estimation performance while minimizing communication and energy overhead. Simulations show that the proposed clustering-based scheme achieves estimation performance comparable to CL while significantly reducing communication and energy overhead.

Source: Physical Communication

DOI: 10.1016/j.phycom.2025.102698

Authors: Muhammad Asaad Cheema, Apoorva Chawla, Vinay Chakravarthi Gogineni, Pierluigi Salvo Rossi

Institutions: Norwegian University of Science and Technology, University of Southern Denmark

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,450