TY - JOUR
T1 - An Asymmetric Evolutionary Bayesian Coalition Formation Game for Distributed Resource Sharing in a Multi-Cell Device-to-Device Enabled Cellular Network
AU - Asheralieva, Alia
AU - Quek, Tony Q.S.
AU - Niyato, Dusit
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2018/6
Y1 - 2018/6
N2 - We present a novel game, called evolutionary Bayesian coalition formation game, to model and analyze the problem of distributed resource sharing in a multi-cell device-to-device (D2D) enabled cellular network where the rationality of the players, i.e., device pairs, is bounded, e.g., due to limited information. Each player can make its decision on the channel to access with and without coordination. In the former case, the player works in D2D mode. In the latter case, the player forms a coalition with some other players and they connect to one base station in cellular mode. In this case, the player realizes its action after observing the actions of other players. Unlike classical coalition formation games where the player decides on its coalition to form by estimating its payoff, in the proposed game, the player forms a coalition and selects an action based on its current population state which is updated using a simple and scalable learning algorithm. We prove that the evolutionary coalition formation process converges to the unique equilibrium that induces a stable coalitional agreement. The proposed process is applied to a long-term evolution-advanced network where it shows a superior performance compared with other baseline resource sharing strategies.
AB - We present a novel game, called evolutionary Bayesian coalition formation game, to model and analyze the problem of distributed resource sharing in a multi-cell device-to-device (D2D) enabled cellular network where the rationality of the players, i.e., device pairs, is bounded, e.g., due to limited information. Each player can make its decision on the channel to access with and without coordination. In the former case, the player works in D2D mode. In the latter case, the player forms a coalition with some other players and they connect to one base station in cellular mode. In this case, the player realizes its action after observing the actions of other players. Unlike classical coalition formation games where the player decides on its coalition to form by estimating its payoff, in the proposed game, the player forms a coalition and selects an action based on its current population state which is updated using a simple and scalable learning algorithm. We prove that the evolutionary coalition formation process converges to the unique equilibrium that induces a stable coalitional agreement. The proposed process is applied to a long-term evolution-advanced network where it shows a superior performance compared with other baseline resource sharing strategies.
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U2 - 10.1109/TWC.2018.2815628
DO - 10.1109/TWC.2018.2815628
M3 - Article
AN - SCOPUS:85044763879
SN - 1536-1276
VL - 17
SP - 3752
EP - 3767
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 6
ER -