Distributed State Estimation for Complex Networks Under Decode-and-Forward Relays: Handling Transmission Power Constraints

This article delves into the distributed state estimation issue for a type of complex networks subjected to packet dropouts, particularly in relay channels employing a decode-and-forward strategy. The communication between the sensor and the remote estimator is conducted via a wireless network, which may encounter probabilistic packet dropouts, necessitating the implementation of a relay-based decode-and-forward strategy. The incidence of packet dropouts is determined by a Bernoulli-distributed random variable, with its probability contingent upon the available transmission power. A comprehensive transmission power constraint model is developed to evaluate the impact of transmission power on the estimation performance. A transmission-power-based estimator is devised, and a sufficient condition is formulated to ensure the mean-square boundedness of the estimation error dynamics. In addition, the codesign of transmission power allocation scheme and estimator gains is framed as an optimization problem, resolved by particle swarm optimization and linear matrix inequality techniques. Finally, simulation examples are displayed to clarify the effective implementation of the theoretical findings.