Cooperative edge caching algorithm for delay optimization in ultra-dense network

Abstract

The invention discloses a collaborative edge caching algorithm for delay optimization in an ultra-dense network. The collaborative edge caching algorithm comprises the following specific steps: 1, setting each parameter of a system model; 2, making an optimal cache decision for each SBS by adopting a multi-agent reinforcement learning algorithm based on the game theory so as to maximize the content cache hit rate of each SBS; 3, making an optimal bandwidth resource allocation decision for each SBS by adopting an improved branch and bound method so as to minimize the total content downloading delay of all user equipment. According to the method, the content downloading delay of all users in the ultra-dense network can be effectively reduced, the content cache hit rate and the spectrum resource utilization rate are improved, and the algorithm has good robustness and expandability and is suitable for the large-scale user-intensive ultra-dense network.

Description

technical field [0001] The invention relates to a delay-optimized collaborative edge cache algorithm in an ultra-dense network, belonging to the field of edge caches in an ultra-dense network. Background technique [0002] In the 5G era, with the popularity of smart mobile devices and mobile applications, mobile data traffic is experiencing explosive growth. In order to meet the requirements of high capacity, high throughput, high user experience rate, high reliability, and wide coverage of 5G networks, ultra-dense networks (Ultra-Dense Networks, UDN) came into being. UDN densely deploys low-power small base stations (Small Base Stations, SBS) in indoor and outdoor hotspot areas (such as office buildings, shopping malls, subways, airports, tunnels, etc.) within the coverage of MBS (Macro Base Station, MBS) to improve Network capacity and spatial multiplexing, while making up for the blind areas that MBS cannot cover. [0003] However, the SBS in UDN is connected to the cor...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing collaborative content caching research requires prior knowledge such as the probability distribution of content popularity (such as Zipf distribution) and user preference models, but in fact, content popularity has complex spatio-temporal dynamic characteristics, usually a non-stationary stochastic process, making accurate prediction and modeling of content popularity difficult

In addition, most of the existing research is based on the single-agent reinforcement learning algorithm, which is a centralized algorithm that requires a centralized control center to collect all user content request information and all SBS memory information. However, the robustness of the algorithm (that is, the failure of the centralized control center will lead to system failure) and poor scalability (that is, the convergence time will increase rapidly with the increase of the number of SBSs), especially for the scenario of multiple SBSs, the centralized control center Location determination will be more complicated and therefore not suitable for UDN

Images