Demo Data from EDBT 2026 / reviewers in the wild / expert
Jia-Wei Chiang
dblp:185/2779
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Wireless networking · 61% Internet architecture and protocols · 30% Physical-layer communications · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless networking
cognitive radio |
0.2 | 1 | 2016 | Unified Opportunistic Scheduling for Layered Multicast over Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2016 |
Internet architecture and protocols › multicast
layered multicast |
0.2 | 1 | 2016 | Unified Opportunistic Scheduling for Layered Multicast over Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2016 |
Wireless networking
opportunistic scheduling |
0.2 | 1 | 2016 | Unified Opportunistic Scheduling for Layered Multicast over Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2016 |
Physical-layer communications › channel coding › adaptive coding › rate-adaptive coding
rateless coding |
0.1 | 1 | 2016 | Unified Opportunistic Scheduling for Layered Multicast over Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2016 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.2optimization · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Unified Opportunistic Scheduling for Layered Multicast over Cognitive Radio NetworksabstractWe investigate the multicast of layered multimedia content in single hop cognitive radio (CR) networks and ways of improving the subscriber utility. The proposed unified opportunistic scheduling (UOS) aggressively utilizes diversity potential present in CR by exploiting two types of multiuser diversity (MUD), one between multicast groups and another between data stream subscribers in a group. As a result, the resource usage efficiency is significantly increased, and better utility can be achieved. According to derived effective average subscriber throughput under unified MUD, a scheduling algorithm is designed to approach the theoretical throughput improvement. We specifically introduce controlled competition between multicast groups to balance the increasing resource efficiency and the risk of overstretching the limited available spectrum. The algorithm combines prioritization, rateless coding, and data fragmentation to inherently cope with dynamic spectrum availability in CR. The presented simulation results show improved performance in various situations in comparison to other schemes not taking full advantage of the present MUD potential. Pavol Polacek, Chih-Wei Huang, Jia-Wei Chiang |
IEEE Trans. Mob. Comput. | 3 |