Name
Abstract | ||
|---|---|---|
We consider a device-to-device (D2D) relayassisted cellular network where mobile transceivers that are owned by self-interested users are incentivized to relay each other's data using tokens, which they exchange electronically to "buy" and "sell" downlink relay services. We formulate the decision problem faced by each UE, namely, the problem of deciding whether or not to relay, as a Markov decision process (MDP). We propose a supervised learning algorithm that devices can deploy to learn their optimal relay policies online given their experienced network environment. Our simulation results show that, within the proposed token system, self-interested devices can achieve almost 15% higher throughput on average, and almost 40% higher throughput at the 90th percentile, than with only direct base-station-to-device communications. Additionally, we show that the token system performs best when the network contains neither too few nor too many tokens. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION WORKSHOP (ICCW) | cellular networks, device-to-device relaying, tokens, incentives, Markov decision process, online learning |
Field | DocType | ISSN |
Wireless network,Relay channel,Computer science,Computer network,Markov decision process,Cellular network,Throughput,Security token,Relay,Telecommunications link | Conference | 2164-7038 |
Citations | PageRank | References |
1 | 0.37 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas Mastronarde | 1 | 240 | 26.93 |
| Viral Patel | 2 | 1 | 0.70 |
| Lingjia Liu | 3 | 799 | 92.58 |