Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

Abstract

Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost.

Network resource allocation method based on blockchain and federated learning in IoT

“…In [27], RL is used in VNE, where policy gradients are used to train policy networks. In [28], RL and policy networks are used in the node mapping phase, and bandwidth resource sequencing is used in link mapping phase. With the objective of maximizing the number of embedded VNs, deep reinforcement learning (DRL) is applied to VNE in [29], which combines deep learning (DL) and RL to enhance the success rate.…”