Recursive state estimation for multisensor systems over cloud radio access networks: Handling the bit rate allocation issue

Abstract

The dramatic evolution of modern communication technologies has led to the emergence of the Cloud Radio Access Network (C-RAN) as a cutting-edge architecture for communication systems deployed in industry. In this paper, the problem of networked state estimation is addressed for a class of multisensor systems with C-RAN serving as the infrastructure for data transmission and processing. A comprehensive mathematical model is established for the underlying C-RAN with a focus on imperfections in data transmission. A state estimator, based on the Kalman filter, is developed using data transmitted through C-RAN. Furthermore, the resource allocation issue, an essential task in C-RAN, is modeled as a bit rate allocation problem, and the relationship between bit rate constraints and estimation performance is thoroughly analyzed. A sufficient condition for the convergence of the recursive state estimation algorithm is proposed. The particle swarm optimization algorithm is employed to allocate the bit rate to enhance the estimation accuracy. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed state estimator and the bit rate allocation method.