TY - GEN
T1 - An Oblivious Game-Theoretic Perspective of RRM in Vehicular Communications
AU - Chen, Xianfu
AU - Wu, Celimuge
N1 - Funding Information:
ACKNOWLEDGEMENTS This research was supported in part by ROIS NII Open Collaborative Research 2020-20S0502, and JSPS KAKENHI grant numbers 18KK0279, 19H04093 and 20H00592.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/9
Y1 - 2020/11/9
N2 - This paper focuses on a vehicle-to-vehicle communication network, for which we study the radio resource management problem accounting for the dynamics of both vehicle mobility and traffic variations. Across the discrete scheduling slots, the vehicle user equipment (VUE)-pairs covered by a roadside unit (RSU) compete with each other for the limited frequency resource, aiming to optimize the long-term performance. We model such a competitive decision-making process as a stochastic game. Due to the semi-continuous state space, we define a partitioned control policy, which facilitates the transformation of the stochastic game into an equivalent stochastic game. For the equivalent stochastic game with a large number of VUE-pairs, the Markov perfect equilibrium can be well approximated using an oblivious equilibrium (OE). Moreover, to avoid the dependence on the statistical information of network dynamics, an online reinforcement learning scheme is obtained to arrive at the OE solution. Simulations validate the theoretical studies performed in this paper.
AB - This paper focuses on a vehicle-to-vehicle communication network, for which we study the radio resource management problem accounting for the dynamics of both vehicle mobility and traffic variations. Across the discrete scheduling slots, the vehicle user equipment (VUE)-pairs covered by a roadside unit (RSU) compete with each other for the limited frequency resource, aiming to optimize the long-term performance. We model such a competitive decision-making process as a stochastic game. Due to the semi-continuous state space, we define a partitioned control policy, which facilitates the transformation of the stochastic game into an equivalent stochastic game. For the equivalent stochastic game with a large number of VUE-pairs, the Markov perfect equilibrium can be well approximated using an oblivious equilibrium (OE). Moreover, to avoid the dependence on the statistical information of network dynamics, an online reinforcement learning scheme is obtained to arrive at the OE solution. Simulations validate the theoretical studies performed in this paper.
UR - http://www.scopus.com/inward/record.url?scp=85097558366&partnerID=8YFLogxK
U2 - 10.1109/ICCC49849.2020.9238926
DO - 10.1109/ICCC49849.2020.9238926
M3 - Conference article in proceedings
AN - SCOPUS:85097558366
SN - 978-1-7281-7328-3
T3 - IEEE International Conference on Communications in China workshops
SP - 85
EP - 89
BT - 2020 IEEE/CIC International Conference on Communications in China, ICCC 2020
PB - IEEE Institute of Electrical and Electronic Engineers
T2 - 2020 IEEE/CIC International Conference on Communications in China, ICCC 2020
Y2 - 9 August 2020 through 11 August 2020
ER -