Semi-dynamic overlapping clustering algorithm for densely distributed wireless communication system

Abstract

The invention discloses a semi-dynamic overlapping clustering algorithm for a densely distributed wireless communication system, which comprises the following steps that: firstly, a central processingunit performs semi-dynamic non-overlapping clustering on a remote access unit (RAU) by utilizing periodically acquired long-time information state information under the condition of limiting the cluster size; secondly, cluster edge users are found out by setting a threshold value; thirdly, average large-scale fading from each cooperative cluster to edge users of the cluster and other clusters iscalculated, and overlapped users are found out through a ratio; and finally, each cooperative cluster performs joint transmission on users in the cluster and avoids interference on overlapped users atthe same time, so that a virtual overlapped cluster is formed. The algorithm takes the user as the center, utilizes the long-time channel information to perform overlapping clustering, can adapt to the movement change of the user position, improves the service quality of cluster edge users, improves the system throughput, and has the advantages of being low in algorithm complexity and small in signaling overhead.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and in particular relates to a semi-dynamic overlapping clustering algorithm of a dense distributed wireless communication system RAU. Background technique [0002] The dense distributed wireless communication system can further shorten the distance between users and RAUs, however, it will also bring more interference between RAUs, especially users at the edge of RAUs will receive serious interference from adjacent RAUs . Interference can be effectively reduced by clustering and coordinating the RAUs. [0003] The existing research on clustering algorithms is mainly divided into static clustering and dynamic clustering. Static clustering is mainly based on geographical location. Although the complexity is low and the signaling overhead is small, it cannot be changed according to the change of user location, and the system performance gain is limited. Dynamic clustering uses short-...

AI Technical Summary

Problems solved by technology

Static clustering is mainly based on geographical location. Although the complexity is low and the signaling overhead is small, it cannot be changed according to the change of user location, and the system performance gain is limited.

Dynamic clustering uses short-term channel state information to dynamically cluster at the end of each time slot, which can adapt to changes in the system, but at the same time has the p

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