Adversarial Reinforcement Learning-Based Converged Communication Efficiency Improvement Method for Power Distribution Network

In order to satisfy the diversified communication requirements of terminal source nodes in power distribution network, it is necessary to optimize the communication orchestration in power distribution unified communication network. Firstly, we construct the joint optimization problem of data transmi...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Linyu PENG, Xu LIU, Wei TANG, Qing LIU, Hao FANG, Guanghui ZHANG
Aineistotyyppi: Artikkeli
Kieli:zho
Julkaistu: State Grid Energy Research Institute 2023-09-01
Sarja:Zhongguo dianli
Aiheet:
Linkit:https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202210068
Kuvaus
Yhteenveto:In order to satisfy the diversified communication requirements of terminal source nodes in power distribution network, it is necessary to optimize the communication orchestration in power distribution unified communication network. Firstly, we construct the joint optimization problem of data transmission delay and energy consumption. Then, the joint optimization problem is modeled as a multi-armed bandit problem, and an adversarial reinforcement learning-based communication orchestration algorithm for power distribution unified communication network is proposed, which uses the historical orchestration information and the perceived adversary between source nodes to dynamically learn the communication orchestration strategy. Finally, the superior performance of the proposed algorithm is verified through simulation.
ISSN:1004-9649