5G와 B5G(Beyond 5G)가 버티컬 산업의 인프라가 되기 위해서는 궁극적으로 사람의 개입을 최소화하면서 인프라 스스로 최적의 성능을 제공해야 하므로 기계학습 (Machine learning) 기술을 Self-Organizing Network(SON) 에 적용하는 것이 필요하다.. 따라서 본 논문에서는 기계학습에 의한 SON 기술의 특허 정보를 거시적·심층적 동향 분석 결과를 제시하였다. SON 특허 정보의 정량적 분석 결과에 따르면 기계학습을 적용한 SON 특허는 전체SON 특허의 27 %를 차지하며 기계학습을 적용한 자체 최적화 중 자원 할당과 로드 밸런싱 (Load Balancing)에편중되어 있다. 특허 경쟁력 지수 측면에서 기계학습 적용 특허는 기계학습 미적용 특허의 약 1/2 정도로 작았다.따라서 기계학습이 적용된 SON 특허는 초기 연구개발 단계에 있으므로 기계학습을 적용한 SON 프레임 워크와자기 구성·최적화·치유 기술에 대한 연구 개발이 필요하다.
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