Name
Abstract | ||
|---|---|---|
Millimeter-wave (mmWave) bands will be an important choice for future vehicular communication to support Gbps links for reliable data transfer in high-rate applications. The recently online learning technologies addressed the problem of the fast beam tracking by exploiting the user location information and mining the received data in mmWave vehicular systems to adapt to the vehicle's environmental situation. However, the fairness and efficiency over mmWave beams are difficult to maintain on the move, especially for high dense traffic, since the number of available beams is quite limited by hardware and cost for current antenna arrays. Fortunately, the social structure of preferences between the neighboring smart cars and their passengers may be leveraged to improve the beam coverage efficiency by performing the broadcast transmission via a single beam. In this paper, we propose a double-layer online learning algorithm, namely context-and-social aware machine learning (CSML), that is based on the context and social preference information of vehicles and passengers, to realize fast beam access with broadcast coverage in mmWave communication systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/WCSP49889.2020.9299785 | 2020 International Conference on Wireless Communications and Signal Processing (WCSP) |
Keywords | DocType | ISSN |
Data-driven networks,millimeter-wave vehicular communications,cross layer data mining,online learning,beam selection | Conference | 2325-3746 |
ISBN | Citations | PageRank |
978-1-7281-7237-8 | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shichao Wang | 1 | 0 | 1.01 |
| Dapeng Li | 2 | 0 | 5.75 |
| Haitao Zhao | 3 | 1 | 3.73 |
| Xiaoming Wang | 4 | 57 | 12.72 |