Name
Abstract | ||
|---|---|---|
Dense cloud radio access networks (cloud-RANs) provide a promising way to enable scalable connectivity and handle diversified service requirements for massive mobile devices. To fully exploit the performance gains of dense cloud-RANs, channel state information of both the signal link and interference links is required. However, with limited radio resources for training, the channel estimation prob... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TWC.2018.2797969 | IEEE Transactions on Wireless Communications |
Keywords | Field | DocType |
Channel estimation,Training,Cloud computing,Estimation,Wireless communication,Supercomputers,Convex functions | Wireless,Communication channel,Real-time computing,Convex function,Interference (wave propagation),Mathematics,Channel state information,Scalability,Cloud computing,Computation,Distributed computing | Journal |
Volume | Issue | ISSN |
17 | 4 | 1536-1276 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Liu | 1 | 297 | 38.07 |
| Yuanming Shi | 2 | 659 | 53.58 |
| Jun Zhang | 3 | 3772 | 190.36 |
| K. B. Letaief | 4 | 11078 | 879.10 |