Clustering method of MIMO multi-cell base station cacheable system

Abstract

The invention discloses a clustering method for an MIMO multi-cell base station cacheable system, and belongs to the technical field of wireless communication. The method comprises the steps of modeling according to a more practical cacheable base station system, considering a downlink model of a single-user cell, pre-clustering the cell by using a soft cluster size constrained graph partition clustering method, then carrying out cluster size balancing according to set judgment conditions, and measuring advantages and disadvantages of the clustering according to the effective rate, thereby enabling the final clustering scheme to more conform to feasible conditions of interference alignment. Compared with the prior art, a clustering scheme with more balanced cluster size and more excellentsystem performance can be obtained under the base station cacheable system capable of adapting to higher-rate transmission by using the clustering method provided by the invention.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a clustering method for removing interference in a multi-cell cacheable system. Background technique [0002] With the advent of 5G technology, data transmission is getting higher and higher. The factors that restrict the throughput of multi-cell multiple-input multiple-output (MIMO) systems mainly include backhaul link capacity and wireless link interference. For the limited capacity of the backhaul link, the base station can pre-cache the content by designing an appropriate cache model, which can make the system reach the reachable rate of the wireless link with a certain probability, and effectively improve the system performance; the limitation of the wireless link , mainly caused by the interference between multiple users, the Interference Alignment (IA) technology is widely used as an interference management scheme to eliminate the interference. ...

AI Technical Summary

Problems solved by technology

[0009] 1. This solution does not consider base station caching, and with the increase in data transmission rate requirements, base station caching is a development trend. Under the condition of limited backhaul link capacity, how to align cacheable base station system cluster interference is not clear plan;

[0010] 2. The scheme performs clustering by defining the number of clusters, but whether the size of each cluster (that is, the number of cells contained in the cluster) strictly meets the IA feasibility condition after clustering is uncertain, and the clustering result may not be strict Eligible for interference alignment feasibility

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