User association for load balancing in coordinated multipoint green HetNets: A Quasi-Newton-based approach

2021 ◽  
pp. 101464
Author(s):  
Mohamad Khattar Awad ◽  
Ali A.M.R. Behiry ◽  
Mohammed W. Baidas
Keyword(s):  
Load Balancing ◽  
User Association ◽  
Coordinated Multipoint ◽  
Quasi Newton

Energy-efficient user association with load-balancing for cooperative IIoT network within B5G era

2021 ◽  
pp. 103110
Author(s):  
Xin Jian ◽  
Langyun Wu ◽  
Keping Yu ◽  
Moayad Aloqaily ◽  
Jalel Ben-Othman
Keyword(s):  
Load Balancing ◽  
Energy Efficient ◽  
User Association

Joint User Association and User Scheduling for Load Balancing in Heterogeneous Networks

2018 ◽  
Vol 17 (5) ◽  
pp. 3211-3225 ◽  
Author(s):  
Xin Ge ◽  
Xiuhua Li ◽  
Hu Jin ◽  
Julian Cheng ◽  
Victor C. M. Leung
Keyword(s):  
Load Balancing ◽  
Heterogeneous Networks ◽  
User Association ◽  
User Scheduling

scholarly journals Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4415 ◽  
Author(s):  
Taewoon Kim ◽  
Chanjun Chun ◽  
Wooyeol Choi
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Mobile Internet ◽  
Base Stations ◽  
Traffic Load ◽  
User Association ◽  
Network Simulator ◽  
Traffic Loads ◽  
Association Strategy ◽  
Radio Access

In networking systems such as cloud radio access networks (C-RAN) where users receive the connection and data service from short-range, light-weight base stations (BSs), users’ mobility has a significant impact on their association with BSs. Although communicating with the closest BS may yield the most desirable channel conditions, such strategy can lead to certain BSs being over-populated while leaving remaining BSs under-utilized. In addition, mobile users may encounter frequent handovers, which imposes a non-negligible burden on BSs and users. To reduce the handover overhead while balancing the traffic loads between BSs, we propose an optimal user association strategy for a large-scale mobile Internet of Things (IoT) network operating on C-RAN. We begin with formulating an optimal user association scheme focusing only on the task of load balancing. Thereafter, we revise the formulation such that the number of handovers is minimized while keeping BSs well-balanced in terms of the traffic load. To evaluate the performance of the proposed scheme, we implement a discrete-time network simulator. The evaluation results show that the proposed optimal user association strategy can significantly reduce the number of handovers, while outperforming conventional association schemes in terms of load balancing.

scholarly journals Energy Efficiency Maximization of Dynamic CoMP-JT Algorithm in Dense Small Cell Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Xuefei Peng ◽  
Jiandong Li ◽  
Yifei Xu
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Optimal Solution ◽  
Small Cell ◽  
Base Stations ◽  
Maximization Problem ◽  
Second Phase ◽  
User Association ◽  
Coordinated Multipoint ◽  
Convex Problem

We firstly formulate the energy efficiency (EE) maximization problem of joint user association and power allocation considering minimum data rate requirement of small cell users (SUEs) and maximum transmit power constraint of small cell base stations (SBSs), which is NP-hard. Then, we propose a dynamic coordinated multipoint joint transmission (CoMP-JT) algorithm to improve EE. In the first phase, SUEs are associated with the SBSs close to them to reduce the loss of power by the proposed user association algorithm, where the associated SBSs of each small cell user (SUE) form a dynamic CoMP-JT set. In the second phase, through the methods of fractional programming and successive convex approximation, we transform the EE maximization subproblem of power allocation for SBSs into a convex problem that can be solved by proposed power allocation optimization algorithm. Moreover, we show that the proposed solution has a much lower computational complexity than that of the optimal solution obtained by exhaustive search. Simulation results demonstrate that the proposed solution has a better performance.

Development of a Hybrid Algorithm for User Association and Resource Allocation to Improve Load Balancing and Energy Efficiency in 5G HetNet

2020 ◽  
Vol 19 (1) ◽  
pp. 17-25
Author(s):  
Elvis Obi ◽  
Aliyu Danjuma Usman ◽  
Suleiman Muhammad Sani ◽  
Abdoulie Momodou Sunkary Tekanyi
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Load Balancing ◽  
Bandwidth Allocation ◽  
Hybrid Algorithm ◽  
Control Algorithm ◽  
User Association ◽  
Network Load ◽  
Power Control Algorithm ◽  
Network Load Balancing

This paper presents the development and integration of a power control algorithm into the User Association Algorithm with Optimal Bandwidth Allocation (UAAOBA) to form a Hybrid Algorithm for User Association and Resource Allocation (HAUARA). The power control algorithm updates the transmit power of the Base Stations (BSs) towards a minimum transmit power that satisfies the minimum data rate requirement (1 Gbps) of the User Equipment UEs. The power update is achieved using the Newton Rhapson’s method and it adapts the transmit powers of the BSs to the number of their connected UEs. The developed HAUARA provides an optimal solution for user associations, bandwidth allocation, and transmit powers to UEs concurrently. This maximizes the network energy efficiency by coordinating the load fairness of the network while guaranteeing the quality of service requirement of the UEs. The network energy efficiency performance of the developed HAUARA is compared with that of the UAAOBA. The results show that the developed algorithm has network energy efficiency improvement of 12.36%, 10.58%, and 13.44% with respect to UAAOBA for increase number of macro BS antennas, pico BSs, and femto BSs, respectively. Also, the network load balancing performance of the developed HAUARA is compared with that of the UAAOBA. The results show that the developed algorithm has network load balancing improvement of 12.62%, 10.04%, and 10.34% with respect to UAAOBA for increase number of macro BS antennas, pico BSs, and femto BSs, respectively. This implies that the developed algorithm outperforms the UAAOBA in terms of network energy efficiency and load balancing.

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