TADocs: Teacher–Assistant Distillation for Improved Policy Transfer in 6G RAN Slicing

Xian Mu, Yao Xu, Dagang Li () and Mingzhu Liu
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Xian Mu: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Yao Xu: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Dagang Li: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
Mingzhu Liu: College of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang 330044, China

Mathematics, 2024, vol. 12, issue 18, 1-21

Abstract: Network slicing is an advanced technology that significantly enhances network flexibility and efficiency. Recently, reinforcement learning (RL) has been applied to solve resource management challenges in 6G networks. However, RL-based network slicing solutions have not been widely adopted. One of the primary reasons for this is the slow convergence of agents when the Service Level Agreement (SLA) weight parameters in Radio Access Network (RAN) slices change. Therefore, a solution is needed that can achieve rapid convergence while maintaining high accuracy. To address this, we propose a Teacher and Assistant Distillation method based on cosine similarity (TADocs). This method utilizes cosine similarity to precisely match the most suitable teacher and assistant models, enabling rapid policy transfer through policy distillation to adapt to the changing SLA weight parameters. The cosine similarity matching mechanism ensures that the student model learns from the appropriate teacher and assistant models, thereby maintaining high performance. Thanks to this efficient matching mechanism, the number of models that need to be maintained is greatly reduced, resulting in lower computational resource consumption. TADocs improves convergence speed by 81% while achieving an average accuracy of 98%.

Keywords: RAN slicing; deep reinforcement learning; policy distillation; SLA; Radio Access Network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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