Machine learning for 5G and beyond: From model-based to data-driven mobile wireless networks
During the past few decades, mobile wireless communications have experienced four generations of technological revolution, namely from 1G to 4G, and the deployment of the latest 5G networks is expected to take place in 2019. One fundamental question is how we can push forward the development of mo...
Lưu vào:
| Tác giả chính: | |
|---|---|
| Đồng tác giả: | |
| Định dạng: | BB |
| Ngôn ngữ: | en_US |
| Thông tin xuất bản: |
IEEE Explore
2020
|
| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/9972 |
| Từ khóa: |
Thêm từ khóa bạn đọc
Không có từ khóa, Hãy là người đầu tiên gắn từ khóa cho biểu ghi này!
|
| Tóm tắt: | During the past few decades, mobile wireless communications have experienced four generations of technological revolution, namely from 1G to 4G, and the deployment of the latest 5G networks is expected to
take place in 2019. One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system. We believe that the answer lies in the huge volumes of data produced by the network itself, and machine learning may become a key to exploit such information. In this paper, we elaborate why the conventional model-based paradigm, which has been widely proved useful in pre-5G networks, can be less efficient or even less practical in the future 5G and beyond mobile networks. Then, we explain how the data-driven paradigm, using state-of-the-art machine learning techniques, can become a promising solution. At last, we provide a typical use case of the data-driven paradigm, i.e., proactive load balancing, in which online learning is utilized to adjust cell mobile wireless networks; |
|---|