A resource efficient scheme for backhaul‐constrained coordinated multipoint cellular networks

2021 ◽  
Author(s):  
Sandeep Gulia ◽  
Anwar Ahmad ◽  
Manish Kumar Singh
Keyword(s):  
Cellular Networks ◽  
Coordinated Multipoint ◽  
Efficient Scheme

How backhaul networks influence the feasibility of coordinated multipoint in cellular networks [Accepted From Open Call]

2013 ◽  
Vol 51 (8) ◽  
pp. 168-176 ◽  
Author(s):  
Thorsten Biermann ◽  
Luca Scalia ◽  
Changsoon Choi ◽  
Wolfgang Kellerer ◽  
Holger Karl
Keyword(s):  
Cellular Networks ◽  
Coordinated Multipoint ◽  
Backhaul Networks

Optimal scheduling of coordinated multipoint transmissions in cellular networks

2017 ◽  
Vol 31 (1) ◽  
pp. e3431 ◽  
Author(s):  
Ali Mohammad Saleh ◽  
Nadira Parveen ◽  
Md. Forkan Uddin
Keyword(s):  
Cellular Networks ◽  
Optimal Scheduling ◽  
Coordinated Multipoint

scholarly journals Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Huibin Lu ◽  
Baozhu Hu ◽  
Zhiyuan Ma ◽  
Shuhuan Wen
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Cellular Networks ◽  
Theoretical Approach ◽  
Energy Efficient ◽  
Local Maximum ◽  
Base Station ◽  
Coordinated Multipoint ◽  
Improve Energy Efficiency ◽  
Simulation Results

Recently, there is an emerging trend of addressing “energy efficiency” aspect of wireless communications. And coordinated multipoint (CoMP) communication is a promising method to improve energy efficiency. However, since the downlink performance is also important for users, we should improve the energy efficiency as well as keeping a perfect downlink performance. This paper presents a control theoretical approach to study the energy efficiency and downlink performance issues in cooperative wireless cellular networks with CoMP communications. Specifically, to make the decisions for optimal base station grouping in energy-efficient transmissions in CoMP, we develop a Reinforcement Learning (RL) Algorithm. We apply theQ-learning of the RL Algorithm to get the optimal policy for base station grouping with introduction of variations at the beginning of theQ-learning to preventQfrom falling into local maximum points. Simulation results are provided to show the process and effectiveness of the proposed scheme.

Stochastic Geometry Analysis of Cellular Networks

2018 ◽  
Author(s):  
Bartłomiej Błaszczyszyn ◽  
Martin Haenggi ◽  
Paul Keeler ◽  
Sayandev Mukherjee
Keyword(s):  
Cellular Networks ◽  
Stochastic Geometry ◽  
Geometry Analysis
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