인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This paper proposes a model architecture emphasizing real-time capability to support intelligent mobility services. The proposed model performs channel estimation with reduced computational time, thereby maintaining stable communication quality and enabling channel-based positioning. To this end, Least Squares (LS) estimation is applied to the input, while the output is replaced with the Channel Impulse Response (CIR). Since the proposed technique is independent of the type of reference signal, it reduces memory requirements, and maintains estimation performance while lowering the total number of model parameters by reducting output size. The CIR approach allows channel utilization over all subcarriers, which can also be effectively applied to channel-based positioning. The proposed method evaluates performance using the Mean Squared Error (MSE) metric and compares it with conventional channel estimation techniques. In addition to accuracy, computational time of the proposed model and traditional methods is analyzed to demonstrate the advantages of the machine learning–based approach. As a result, the proposed architecture achieves higher estimation accuracy while maintaining low computational time compared to conventional methods, thereby validating the effectiveness of machine learning–based channel estimation techniques for realizing intelligent wireless access networks.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094731525
