Resource allocation method based on hybrid clustering in ultra-dense network

Abstract

The invention belongs to the technical field of ultra-dense network resource allocation, and relates to a hybrid clustering-based resource allocation method in an ultra-dense network, which comprises the following steps of: acquiring data of an original base station and a user position, and preprocessing the data; clustering the micro base stations by adopting a Canopy algorithm and a K-means algorithm; constructing an interference graph for the users in each cluster after clustering, and grouping the users by adopting a dyeing algorithm according to the interference graph; adopting a greedy algorithm to distribute sub-channels for users in each group; performing power distribution on the users in each group by adopting a water injection algorithm; through the clustering algorithm combining two algorithms, clustering, user grouping, sub-channel distribution and power distribution can be accurately and rapidly carried out on the micro base stations, same-layer interference between the base stations can be effectively reduced, and the throughput and performance of the system are improved.

Description

technical field [0001] The invention belongs to the technical field of ultra-dense network resource allocation, and relates to a hybrid clustering-based resource allocation method in an ultra-dense network. Background technique [0002] Due to the explosive growth of data traffic, wireless networks face enormous challenges. In addition to meeting the requirements of high-speed data transmission, the fifth-generation mobile communication system (5G) also needs to meet the needs of different application scenarios such as large capacity, ultra-reliability, low latency, and ubiquitous access. Ultra-Dense Networks (UDNs) are a promising approach to meet these requirements. [0003] In an ultra-dense network, the number of micro base stations is much larger than that of a traditional network. At the same time, densely distributed micro base stations will shorten the transmission distance between users and base stations, which can significantly improve the throughput of the entir...

AI Technical Summary

Problems solved by technology

However, the k value and the initial clustering center of the traditional k-means algorithm are given in advance, which will lead to inaccurate final clustering results and cannot reflect the real interference relationship, which will lead to inability to allocate resources in the next step. accurate

[0005] After the micro base stations are clustered, the base stations in the same cluster may also cause interference to users connected to each other, which will make it difficult for users to obtain a rate that meets the quality of service

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