Federated Learning for Cellular Networks: Joint User Association and Resource Allocation

2020 ◽  
Author(s):  
Latif U. Khan ◽  
Umer Majeed ◽  
Choong Seon Hong
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
User Association

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 QoS-aware Resource Allocation for Live Streaming in Edge-Clouds Aided HetNets Using Stochastic Network Calculus

2021 ◽  
Author(s):  
Abbas Mirzaei
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
Power Efficiency ◽  
Energy Utilization ◽  
Total Power ◽  
User Association ◽  
Network Calculus ◽  
Stochastic Network ◽  
Data Flows ◽  
Stochastic Network Calculus

Abstract Mobile edge computing (MEC) is a key feature of next generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Edge clouds can be installed as an interface between the cellular networks and the core to provide the required services based on the known concept of the MEC networks. Nonetheless, the problem of green networking will be of great importance in such networks. This paper presents an energy-efficient stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a dynamic user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to provide the superior fairness level for UEs. In this energy-cooperative approach, the power can be shared among the cells using a grid network. This model also performs routing in the multi-hop backhaul to efficiently use the existing infrastructure of small cell networks for simultaneous dual-hop transmissions. The simulation results exhibit that the proposed approach can effectively increase the user throughput and the total power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot UE distribution models. It also proved that the energy utilization index and the system data rate can be significantly improved.

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