Reinforcement learning for communication load balancing: approaches and challenges
The amount of cellular communication network traffic has increased dramatically in recent
years, and this increase has led to a demand for enhanced network performance …
years, and this increase has led to a demand for enhanced network performance …
Joint Computation Offloading and Resource Allocation for LEO Satellite Networks Using Hierarchical Multi-Agent Reinforcement Learning
J Lai, H Liu, G Xu, W Jiang, X Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The integration of edge computing with LEO satellite broadband networks (LSBNs) offers a
transformative potential, yet remains underexplored in the optimization of joint computation …
transformative potential, yet remains underexplored in the optimization of joint computation …
Mixed-variable PSO with fairness on multi-objective field data replication in wireless networks
Digital twins have shown a great potential in supporting the development of wireless
networks. They are virtual representations of 5G/6G systems enabling the design of machine …
networks. They are virtual representations of 5G/6G systems enabling the design of machine …
Communication Load Balancing via Efficient Inverse Reinforcement Learning
Communication load balancing aims to balance the load between different available
resources, and thus improve the quality of service for network systems. After formulating the …
resources, and thus improve the quality of service for network systems. After formulating the …
Linking HO Delay towards Load Balancing in SDN-HetNets Environ
A Pagare, A Gupta - 2022 Second International Conference on …, 2022 - ieeexplore.ieee.org
Within the context of heterogeneous networks (HetNets), which are networks made up of
BSs with asymmetry transmitted power, we propose a new handover (HO) algorithm that …
BSs with asymmetry transmitted power, we propose a new handover (HO) algorithm that …