Digital twin-assisted multi-mode communication resource management methods for smart buildings

Abstract

The multi-mode communication network provides communication support for the collection, transmission, and processing of energy regulation data and the training of energy regulation models for smart buildings.Digital twin can provide state estimation of computing resources and channel characteristics, assist in the multi-mode communication resource optimization management, and improve the training precision of energy regulation models.However, the digital twin-assisted multi-mode communication resource management of smart buildings still face challenges such as large training error of energy regulation model, coupling of multi-timescale resource allocation, and contradictions between training precision improvement of energy regulation model and energy consumption optimization.Aiming at the above challenges, a multi-timescale communication resource management optimization algorithm based on digital twin and empirical matching learning was proposed.The weighted sum of global model loss function and energy consumption was minimized by jointly optimizing the large-timescale gateway selection and small-timescale channel allocation and power control.Simulation results show that the proposed algorithm can improve the performance of weighted sum of global model loss function and energy consumption, ensure the precise energy regulation requirement and promote the low-carbon operation of smart buildings.

Keywords

• smart building;digital twin;energy regulation;federated learning;matching theory;upper confidence bound