Edge cooperation caching method under hierarchical wireless network supporting D2D communication

Abstract

The invention discloses an edge cooperation caching method under a hierarchical wireless network supporting D2D communication. The method comprises the steps that whether a local cache of a device receiving a content request contains request content or not is checked according to the content request; if yes, the request content is hit; if not, routing selection is performed based on a deep reinforcement learning algorithm to hit the request content; and after the request content is hit, the model of the deep reinforcement learning algorithm is updated. According to the method, a deep reinforcement learning algorithm is applied to collaborative cache configuration of a user equipment layer in a mobile wireless network; the unsupervised learning of the cooperative cache of the user equipment is realized, the hit rate of the maximum cache is taken as an optimization target, and the flow is unloaded to the periphery of the user as much as possible, so that the flow transmission in the cellular network is greatly reduced, and the transmission delay of the user request content is shortened.

Description

technical field [0001] The present invention relates to the field of wireless communication and mobile computing, in particular to an edge cooperative caching method under a layered wireless network supporting D2D communication. Background technique [0002] In recent years, with the rapid increase in the number of smart phones and other smart mobile devices, people's demand for multimedia services is soaring when using mobile networks. The severe traffic pressure urgently requires network operators to find practical and effective solutions. Mobile network operators (MNOs) and network equipment suppliers have also adopted a series of complex technologies applied to the physical layer and the lower half of the data link layer - the media access control layer, to increase the bandwidth of wireless links, such as Large-scale multiple-input multiple-output (MIMO), carrier aggregation, and coordinated multi-point transmission (CoMP), etc., but these have reached their theoretical...

AI Technical Summary

Problems solved by technology

As one of the representatives of deep reinforcement learning algorithms, DQN is still based on Q-Learning. Therefore, it is still unable to overcome the shortcoming of Q-Learning itself - overestimation

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