数字孪生辅助的智能楼宇多模态通信资源管理方法

多模态通信网络为智能楼宇能源调控数据的采集、传输、处理以及能源调控模型训练提供了通信支撑。数字孪生可以提供计算资源、信道特性等状态估计,辅助多模态通信资源管理优化,提高能源调控模型训练精度。然而,数字孪生辅助的智能楼宇多模态通信资源管理面临能源调控模型训练误差大、多时间尺度资源分配耦合、模型训练精度提高与能耗优化相互矛盾等挑战。针对上述挑战,提出基于数字孪生和经验匹配学习的多时间尺度通信资源管理优化算法,通过联合优化大时间尺度网关选择和小时间尺度信道分配与功率控制,最小化全局模型损失函数和能耗加权和。仿真结果表明,所提算法可以提高全局模型损失函数和能耗加权和性能,保障智能楼宇能源精准调控需求,促进智能楼宇能源调控低碳运行。

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

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.