Cache-enabled D2D communication joint recommendation and caching method

Abstract

The invention discloses a cache-enabled D2D communication joint recommendation and caching method, and the method comprises the following steps: constructing an optimization function by taking the minimum file acquisition cost of a user as an optimization target under the conditions that the caching capacity of the user is limited and the recommendation quantity is limited, and obtaining a recommendation scheme for each user by adopting an annealing algorithm; obtaining a caching scheme of each user by using a knapsack algorithm, and performing iterative optimization on the caching scheme andthe recommendation scheme to obtain an optimal solution of the recommendation scheme and the caching scheme, thereby realizing minimization of the average cost of the users. According to the method, recommendation and caching are combined, the influence of different preferences of users on caching is reduced, the proposed recommendation and caching scheme can reduce the average cost of the users,the users are stimulated to use D2D communication, and the network pressure is reduced.

Description

technical field [0001] The present invention relates to a recommendation and caching method, in particular to a cache-enabled D2D communication joint recommendation and caching method. Background technique [0002] The explosive growth of smart mobile devices and smart applications has led to a sharp increase in mobile data traffic, and traditional cellular networks have been greatly challenged. Device-to-Device (D2D) technology, as one of the key technologies of the fifth-generation mobile communication, allows direct communication between adjacent devices in the communication network without the help of infrastructure such as core devices or central devices, thereby greatly reducing Data pressure on the core network of the communication system. The caching technology is to store the file content items (including videos, web pages, etc.) required by the user in the cache entity in advance, so that the user's demand for content can be effectively realized, greatly reducing ...

AI Technical Summary

Problems solved by technology

However, even if the request probability is reshaped through recommendation, the content requested by users will still vary to a certain extent. Only caching some files at the base station cannot satisfy all users' content requests, and when the user is far away from the base station, more files are still required. fee to get what you need

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