A genetic algorithm-based cell-free massive MIMO clustering method

By using a cellular-free large-scale MIMO clustering method based on genetic algorithms, access point clustering is optimized, solving the problem of insufficient transmission rate under URLLC and improving network transmission quality and computational efficiency.

CN117279007BActive Publication Date: 2026-06-12SOUTHEAST UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SOUTHEAST UNIV
Filing Date
2023-09-19
Publication Date
2026-06-12

AI Technical Summary

⚠Technical Problem

Existing non-cellular massive MIMO clustering solutions, under URLLC requirements, cannot guarantee that devices with relatively poor channels can meet the transmission rate requirements, and the computational pressure on the fronthaul link and the central CPU is relatively high.

⚗Method used

A non-cellular large-scale MIMO clustering method based on genetic algorithms is adopted. By establishing a signal reception model and optimizing the objective function, the access point clustering is optimized using genetic algorithms to ensure user transmission quality and system performance.

🎯Benefits of technology

It increases the minimum transmission rate for all users in the system, meets the latency and error rate requirements of URLLC, reduces the computational burden on the fronthaul link, and improves network transmission quality.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a genetic algorithm-based cell-free massive MIMO clustering method, and the method comprises the following steps: by derivation, an associated mathematical expression between a user reachable rate lower bound and a clustering strategy is obtained under uplink URLLC transmission, estimation error of a user channel is considered, and it is assumed that partial zero-forcing reception precoding is adopted at an AP, and large-scale fading decoding is adopted at a center; according to the derivation, a 0-1 programming problem form with the maximum minimum user upload rate as the target is obtained, and a genetic algorithm is designed to solve the clustering problem. Simulation results show that the application can improve the minimum user transmission rate of the system under the delay and error rate requirements of URLLC through an effective user-centered clustering strategy, thereby ensuring the transmission quality of all users in the network, and has a wide application prospect.
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