Name
Abstract | ||
|---|---|---|
This letter studies an ultra-reliable low latency communication problem focusing on a vehicular edge computing network in which vehicles either fetch and synthesize images recorded by surveillance cameras or acquire the synthesized image from an edge computing server. The notion of
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">risk-sensitive</italic>
in financial mathematics is leveraged to define a reliability measure, and the studied problem is formulated as a risk minimization problem for each vehicle’s end-to-end (E2E) task fetching and offloading delays. Specifically, by resorting to a joint utility and policy estimation-based learning algorithm, a distributed risk-sensitive solution for task fetching and offloading is proposed. Simulation results show that our proposed solution achieves performance improvements up to 40% variance reduction and steeper distribution tail of the E2E delay over an averaged-based baseline. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LCOMM.2019.2960777 | IEEE Communications Letters |
Keywords | DocType | Volume |
5G and beyond,vehicular edge computing,URLLC,risk-sensitive learning | Journal | 24 |
Issue | ISSN | Citations |
3 | 1089-7798 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sadeep Batewela | 1 | 1 | 0.35 |
| Chen-Feng Liu | 2 | 112 | 9.53 |
| Mehdi Bennis | 3 | 3652 | 217.26 |
| Himal A. Suraweera | 4 | 2639 | 117.31 |
| Choong Seon Hong | 5 | 2044 | 277.88 |