Resource allocation and user association for HTTP adaptive streaming in heterogeneous cellular networks with small cells

2016 ◽  
Vol 13 (9) ◽  
pp. 1-11 ◽  
Author(s):  
Jiang Liu ◽  
Renchao Xie ◽  
F. Richard Yu
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
Small Cells ◽  
User Association ◽  
Adaptive Streaming ◽  
Heterogeneous Cellular Networks ◽  
Http Adaptive Streaming

Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks

2019 ◽  
Vol 18 (11) ◽  
pp. 5141-5152 ◽  
Author(s):  
Nan Zhao ◽  
Ying-Chang Liang ◽  
Dusit Niyato ◽  
Yiyang Pei ◽  
Minghu Wu ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Cellular Networks ◽  
User Association ◽  
Heterogeneous Cellular Networks

scholarly journals Reinforcement Learning for Interference Coordination Stackelberg Games in Heterogeneous Cellular Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen Sun ◽  
Shiyi Wu ◽  
Bo Zhang
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Power Control ◽  
Cellular Networks ◽  
Stackelberg Game ◽  
Small Cells ◽  
Stackelberg Games ◽  
Interference Coordination ◽  
Heterogeneous Cellular Networks ◽  
Cost Parameters

In future heterogeneous cellular networks with small cells, such as D2D and relay, interference coordination between macro cells and small cells should be addressed through effective resource allocation and power control. The two-step Stackelberg game is a widely used and feasible model for resource allocation and power control problem formulation. Both in the follower games for small cells and in the leader games for the macro cell, the cost parameters are a critical variable for the performance of Stackelberg game. Previous studies have failed to adequately address the optimization of cost parameters. This paper presents a reinforcement learning approach for effectively training cost parameters for better system performance. Furthermore, a two-stage pretraining plus ε -greedy algorithm is proposed to accelerate the convergence of reinforcement learning. The simulation results can demonstrate that compared with the three beachmarking algorithms, the proposed algorithm can enhance average throughput of all users and cellular users by up to 7% and 9.7%, respectively.

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