Ultra-dense heterogeneous network small station coding cooperative caching method based on value function approximation

Abstract

The invention discloses an ultra-dense heterogeneous network small station coding cooperative caching method based on value function approximation. The method includes the following steps: adopting areinforcement learning method based on value function approximation, expressing a value function as a function of state and action, taking the operation of maximizing the number of file requests directly served by the average cumulative small stations as an optimization objective, continuously interacting with the environment to adapt to the dynamic changes of the environment, mining a potential file request transfer mode, obtaining an approximate expression of the value function, and further obtaining a cooperative caching decision that matches the file request transfer mode; and encoding thecooperative caching decision by a macro base station, and transmitting encoded cooperative caching results to each small station. The scheme of the invention formulates the caching decision by meansof the transfer mode of the file requests in a real network mined by reinforcement learning, does not need any assumptions about the prior distribution of data, and is more suitable for an actual system; and moreover, through the real-time interaction with the environment, the time-varying file popularity can be tracked, the corresponding caching strategy is made, the process is simple and feasible, and the operation of solving NP-hard problems is not required.

Description

technical field [0001] The invention belongs to the technical field of wireless network deployment in mobile communication, and in particular relates to an ultra-dense heterogeneous network small station coding cooperative caching method. Background technique [0002] With the popularity of smart terminals and the development of Internet services, ultra-dense heterogeneous networks will become one of the key technologies of the fifth generation mobile communication system (5G) in order to meet users' requirements for high data rates and high service quality. Deploying dense small cells within the coverage area of ​​macro base stations can effectively improve the communication quality of users at the edge of the network, thereby improving spectrum efficiency and system throughput. However, since the small cells are connected to the macro base station through the wireless backhaul link, the densely deployed small cells put a huge pressure on the wireless backhaul link. At this...

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Problems solved by technology

First of all, it is often considered that the popularity of files does not change with time, and the popularity of files in the actual network changes from time to time. This method of solving optimization problems based on constant file popularity cannot track the continuous changes of file popularity, thus The resulting caching decision cannot be well applied to the actual network; secondly, even if the constant file popularity is replaced with the instantaneous file popularity, once the file popularity is changed, the optimization problem will be rerun, which brings Huge network overhead, and the optimization problem of modeling is often NP-hard (Non-Polynomial hard) problem, which is very difficult to solve; finally, because the cache problem itself is based on the file request behavior that has occurred in the network, the cache decision is made. To prepare for the upcoming file request behavior, the method of making cache decisions based on the traditional solution optimization problem cannot mine the transfer mode of file requests in the network, so that the cache decision made is not optimal for the upcoming file requests

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