EDBT 2026 Demo / reviewers in the wild / expert
Mani Bharathi Pandian
dblp:162/3874
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-author
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 · 67% Network optimization and economics · 33% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless networking
cognitive radio |
0.2 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Wireless networking › cognitive radio
cooperative cognitive radio |
0.2 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Network optimization and economics
resource allocation |
0.2 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Wireless networking › WLAN
IEEE 802.11 |
0.1 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Network optimization and economics
spectrum leasing |
0.1 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Wireless networking
WLAN |
0.1 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Algorithmic game theory and mechanism design › negotiation
bargaining game |
0.1 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Algorithmic game theory and mechanism design › cooperative game theory
nash bargaining solution |
0.1 | 1 | 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio Networks · IEEE Trans. Mob. Comput. 2015 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.4nash solution · 0.4bargaining game · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Optimal Resource Allocation in Random Access Cooperative Cognitive Radio NetworksabstractCooperative cognitive radio networks (CCRNs) incorporates cooperative communication into cognitive radio networks, in which, primary users lease their spectrum to secondary users, and in exchange, the primary users leverage secondary users as cooperative relays to enhance their own throughput. Mobile operators offload their Internet traffic to privately owned Wi-Fi access points (APs), much to the inconvenience of non-cellular users served by the APs. However, by employing the CCRN scheme, the mobile operator can lease a licensed channel to the AP, effectively doubling its capacity. In this paper, we propose an implementation of the CCRN framework applied to IEEE 802.11 WLANs. The cooperation is cast as a two-player bargaining game where the two players are the primary users (users of the mobile operator) and the secondary users (users of the AP before spectrum leasing) who bargain for either throughput share or channel access time share. The optimal resource allocation that ensures efficiency as well as fairness among users is provided by the Nash solution. Simulation results show that the users achieve higher throughput via the proposed CCRN scheme, thus providing the mobile operator (e.g., AT&T) and the private Wi-Fi provider (e.g., a Starbucks coffee shop) with incentives for cooperation. Mani Bharathi Pandian, Mihail L. Sichitiu, Huaiyu Dai |
IEEE Trans. Mob. Comput. | 1 |