TY - GEN
T1 - Device-to-device relay assisted cellular networks with token-based incentives
AU - Mastronarde, Nicholas
AU - Patel, Viral
AU - Liu, Lingjia
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - We consider a device-to-device (D2D) relay-assisted 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.
AB - We consider a device-to-device (D2D) relay-assisted 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.
KW - cellular networks
KW - device-to-device relaying
KW - incentives
KW - Markov decision process
KW - online learning
KW - tokens
UR - https://www.scopus.com/pages/publications/84947782453
U2 - 10.1109/ICCW.2015.7247263
DO - 10.1109/ICCW.2015.7247263
M3 - Conference contribution
AN - SCOPUS:84947782453
T3 - 2015 IEEE International Conference on Communication Workshop, ICCW 2015
SP - 698
EP - 704
BT - 2015 IEEE International Conference on Communication Workshop, ICCW 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Communication Workshop, ICCW 2015
Y2 - 8 June 2015 through 12 June 2015
ER -