Intelligent Resource Allocation Based on Reinforcement Learning for NOMA Vehicular Cooperative Communication Networks

Author(s):  
Hao Zeng ◽  
Lintao Zhang ◽  
Yuliang Tang ◽  
Yanglong Sun ◽  
Yuqi Ruan
Author(s):  
Oluseye Adeleke ◽  
Wasana Boonsong

In this paper, we propose a new scheme for optimal resource (i.e, power) allocation in a cooperative wireless communication system, using a type of game called the Bidding game. Previous related works have all considered networks with multiple source nodes interacting with either single or multiple relays, without paying so much attention to how partners are selected for cooperation. However because of the importance of partner selection as an integral part of an efficient cooperative communication network, which also includes resource allocation, we propose this new game-based resource allocation scheme, in which the conventional theories of economic bidding are applied. In this work, we model the cooperative communication network as a single-user, multi-relay system in which the source acts as the auctioneer while the relays or partners act as the bidders in the game. The resource being auctioned here is power. The relay which offers the highest bid in terms of price is first selected by the source node and then allocated power by the source node. Our proposed scheme is aimed at answering the question of how maximally or optimally the power should be allocated in the network by the source node so as not to violate the power constraint. We show that there exists bidding and pricing mechanisms or strategies that lead to the maximization of network throughput or utility in cooperative communication networks. We also see in our simulation results that there is convergence to the Nash equilibrium which proves the correctness of our scheme.


Author(s):  
Zhiyuan Xu ◽  
Jian Tang ◽  
Chengxiang Yin ◽  
Yanzhi Wang ◽  
Guoliang Xue ◽  
...  

Author(s):  
Waleed Ahsan ◽  
Wenqiang Yi ◽  
Zhijin Qin ◽  
Yuanwei Liu ◽  
Arumugam Nallanathan

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