| LDR | | 01919nam u200385 4500 |
| 001 | | 000000421962 |
| 005 | | 20190215165737 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438169005 |
| 035 | |
▼a (MiAaPQ)AAI10824660 |
| 035 | |
▼a (MiAaPQ)ucsd:17493 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Ding, Yacong. |
| 245 | 10 |
▼a Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery. |
| 260 | |
▼a [S.l.]:
▼b University of California, San Diego.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 194 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Bhaskar D. Rao. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, San Diego, 2018. |
| 520 | |
▼a Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems, where the base station (BS) is equipped with a large number of antenna elements to serve multiple user equipments. With the large |
| 520 | |
▼a To reduce the training and feedback overhead, compressive sensing methods and sparse recovery algorithms are proposed to robustly estimate the downlink and uplink channel by exploiting the sparse representation of the massive MIMO channel. Previ |
| 590 | |
▼a School code: 0033. |
| 650 | 4 |
▼a Electrical engineering. |
| 690 | |
▼a 0544 |
| 710 | 20 |
▼a University of California, San Diego.
▼b Electrical Engineering (Communication Theory and Systems). |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0033 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998690
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |