scholarly journals Bargaining solutions in heterogeneous networks: A reinforcement learning‐based approach

2021 ◽  
Author(s):  
Atena Ebrahimkhani ◽  
Bahareh Akhbari
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Networks ◽  
Bargaining Solutions

scholarly journals Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

2019 ◽  
Vol 18 (8) ◽  
pp. 3933-3947 ◽  
Author(s):  
Roohollah Amiri ◽  
Mojtaba Ahmadi Almasi ◽  
Jeffrey G. Andrews ◽  
Hani Mehrpouyan
Keyword(s):  
Reinforcement Learning ◽  
Power Control ◽  
Heterogeneous Networks ◽  
Self Organization

Multi-Agent Actor Critic for Channel Allocation in Heterogeneous Networks

2020 ◽  
Vol 11 (1) ◽  
pp. 23-41
Author(s):  
Nan Zhao ◽  
Zehua Liu ◽  
Yiqiang Cheng ◽  
Chao Tian
Keyword(s):  
Reinforcement Learning ◽  
Communication Networks ◽  
Heterogeneous Networks ◽  
High Efficiency ◽  
Channel Allocation ◽  
Optimal Solution ◽  
New Method ◽  
Multi Agent ◽  
Optimal Approach ◽  
The Cost

Heterogeneous networks (HetNets) can equalize traffic loads and cut down the cost of deploying cells. Thus, it is regarded to be the significant technique of the next-generation communication networks. Due to the non-convexity nature of the channel allocation problem in HetNets, it is difficult to design an optimal approach for allocating channels. To ensure the user quality of service as well as the long-term total network utility, this article proposes a new method through utilizing multi-agent reinforcement learning. Moreover, for the purpose of solving computational complexity problem caused by the large action space, deep reinforcement learning is put forward to learn optimal policy. A nearly-optimal solution with high efficiency and rapid convergence speed could be obtained by this learning method. Simulation results reveal that this new method has the best performance than other methods.

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