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다중 사용자 MIMO 시스템에서 그래프신경망 기반 확장 가능한 빔포밍 최적화
Graph Neural Network Based Scalable Beamforming Optimization for Multi-User MIMO System
- 김준범;
- 유대성
초록
This paper addresses a beamforming optimization inmulti-user multiple input multiple output (MIMO) systemapplying a graph neural network (GNN) that can bescaled to accommodate varying numbers of base station(BS) antennas, users, and user antennas. The intractablebeamforming optimization problem for multi-user MIMOsystem is simplified by adopting a multi-user multipleinput single output (MISO) system-induced approximation. Additionally, we propose an efficient optimization methodwith low complexity by applying a GNN-based inferenceprocedure. The effectiveness of the proposed method isdemonstrated through the numerical results, showingconsiderable sum rate performance and system scalabilityin the dynamic wireless environments.
키워드
Beamforming optimization; Graph neural network; Multi-user MIMO system; Scalability
- 제목
- 다중 사용자 MIMO 시스템에서 그래프신경망 기반 확장 가능한 빔포밍 최적화
- 제목 (타언어)
- Graph Neural Network Based Scalable Beamforming Optimization for Multi-User MIMO System
- 저자
- 김준범; 유대성
- 발행일
- 2024-11
- 저널명
- 한국정보통신학회논문지
- 권
- 28
- 호
- 11
- 페이지
- 1415 ~ 1418