User clustering method for downlink of multi-user mimo-noma system

Abstract

The invention discloses a multi-user multiple-input multiple-output-non-orthogonal multiple access access MIMO-NOMA system downlink user clustering method, which mainly solves the problem that the existing clustering algorithm does not comprehensively consider channel gain difference and spatial correlation It leads to the problem of low user fairness. The implementation scheme is: 1) base station calculates channel gain difference and spatial correlation based on channel information to obtain priority matrix; 2) completes user clustering based on two-way priority selection according to channel priority matrix; 3) completes user clustering based on two-way priority selection; The cluster performs the minimum data rate threshold comparison operation. If the minimum data rate threshold constraint is met, then continue to implement user clustering based on two-way priority selection, otherwise, complete an exhaustive search for all remaining users. The invention has the advantages of both user fairness and system throughput, and can be used for the user clustering operation of the downlink of the multi-user MIMO-NOMA system.

Description

technical field [0001] The invention belongs to the technical field of communication, and particularly relates to a downlink user clustering method, which can be used in a multi-user MIMO-non-orthogonal multiple access MIMO-NOMA system. Background technique [0002] With the rapid growth of mobile data volume, new multiple access technology needs to meet the requirements of 5G mobile communication for high spectral efficiency and massive access. In recent years, non-orthogonal multiple access (NOMA) technology has attracted much attention due to its high performance, and has become one of the most promising candidates in 5G non-orthogonal multiple access technology. Different from Orthogonal Multiple Access OMA technology, NOMA allows multiple users to share time-frequency domain resources by using different power. NOMA technology uses power multiplexing technology at the sending end to actively introduce interference. According to the near-far effect, users with poor chann...

AI Technical Summary

Problems solved by technology

Its advantage is that it comprehensively considers the spatial correlation degree and the channel gain difference, and reduces the intra-cluster interference and inter-cluster interference. Users with low degree but large gain difference cannot participate in clustering, which affects the throughput of the system and the fairness of users

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