User clustering method and system, storage medium, computer equipment and application

Abstract

The invention belongs to the technical field of wireless communication, and discloses a user clustering method and system, a storage medium, a computer device and an application. The method comprisesthe steps: carrying out the initial user clustering according to the channel gains of all users in the system and the channel similarity between the users; calculating initial model parameters of theGaussian mixture model according to an initial user clustering result; calculating the posterior probability of each sample generated by each mixed component and iteratively updating the parameters ofthe Gaussian mixture model; and judging whether the number of iterations reaches a set termination condition or not: if so, performing user clustering according to a cluster mark, and otherwise, returning to calculate the posteriori probability of each sample generated by each mixed component and iteratively updating the parameters of the Gaussian mixture model. According to the method, the initial model parameters of the Gaussian mixture model are optimized, the system throughput is improved compared with a conventional user clustering method, and the method can be used for a multi-antenna non-orthogonal multiple access system.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and in particular relates to a user clustering method, system, storage medium, computer equipment and application. Background technique [0002] At present: the user clustering method is a key technology in the multi-antenna non-orthogonal multiple access system, which has a great influence on the system throughput. The traversal search clustering method is a method to pursue the maximization of system performance. BENJEBBOVU A et al. proposed in "Concept and Practical Considerations of Non-Orthogonal Multiple Access (NOMA) for Future Radio Access" (International Symposium on Intelligent Signal Processing and Communications Systems.IEEE, 2014: 770-774) that the traversal search clustering method will All possible user clustering combinations are traversed, so the optimal solution or local optimal solution of the objective function can always be obtained. However, the complexity o...

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Problems solved by technology

[0007] (1) The current random user clustering method does not consider the interference between users during the entire implementation process, and the system performance is poor;

[0008] (2) In the implementation process of the pre-grouping and clustering method, there may be problems such as uneven distribution of strong and weak user sets or relatively concentrated users at the interval value, which leads to the failure of the selected users in the user cluster to reach a certain channel quality interval, which affects the system to obtain higher system throughput;

[0009] (3) The fixed grouping algorithm is relatively easy to implement, but this algorithm studies the power pairing situation of only two users in the cluster, and does not make full use of the advantages of the multi-antenna non-orthogonal multiple access system, so the system throughput is improved not obvious enough

The traditional user clustering algorithm only considers the difference in user channel gain or the size of the channel similarity, and does not make full use of the channel state information, resulting in poor overall system performance.

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