Machine Learning-Based Antenna Selection in Wireless Communications

Citations

WEB OF SCIENCE

161
Citations

SCOPUS

202

초록

This letter is the first attempt to conflate a machine learning technique with wireless communications. Through interpreting the antenna selection (AS) in wireless communications (i.e., an optimization-driven decision) to multiclass-classification learning (i.e., data-driven prediction), and through comparing the learning-based AS using k-nearest neighbors and support vector machine algorithms with conventional optimization-driven AS methods in terms of communications performance, computational complexity, and feedback overhead, we provide insight into the potential of fusion of machine learning and wireless communications.

키워드

Machine learning; multiclass classification; k-NN; SVM; data-driven prediction (DDP); optimization-driven decision (ODD); antenna selection; MIMO
제목
Machine Learning-Based Antenna Selection in Wireless Communications
저자
Joung, Jingon
DOI
10.1109/LCOMM.2016.2594776
발행일
2016-11
유형
Article
저널명
IEEE Communications Letters
권
20
호
11
페이지
2241 ~ 2244