Deep reinforcement learning-based optimization for end-to-end network slicing with control-and user-plane separation
Control-and user-plane separation (CUPS) and network slicing are two key technologies to
support increasing network traffic and diverse wireless services. However, the benefit of …
support increasing network traffic and diverse wireless services. However, the benefit of …
Deep Reinforcement Learning for Optimization of RAN Slicing Relying on Control-and User-Plane Separation
The rapid development of radio access network (RAN) slicing and control-and user-plane
separation (CUPS) has created a new paradigm for future networks, namely CUPS-based …
separation (CUPS) has created a new paradigm for future networks, namely CUPS-based …
On Performance of SWIPT Empowered NOMA-HetNet with Non-Linear Energy Harvesting
AS Parihar, A Baghel, P Swami… - … National Conference on …, 2024 - ieeexplore.ieee.org
This work studies the performance of energy harvesting (EH) assisted cooperative non-
orthogonal multiple access (NOMA) in a two-tier heterogeneous network (HetNet) with a …
orthogonal multiple access (NOMA) in a two-tier heterogeneous network (HetNet) with a …
Dynamic Game-based Caching Replacement in Edge Networks
H Gu, W Cai, L Zhao, W Luo, G Zhou… - 2022 IEEE 95th …, 2022 - ieeexplore.ieee.org
In this paper, we design a caching replacement algorithm for edge networks to enhance the
cache hit ratio of the edge network and reduce the traffic usage in the backbone network …
cache hit ratio of the edge network and reduce the traffic usage in the backbone network …