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.

scholarly journals Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2904 ◽  
Author(s):  
Hyebin Park ◽  
Yujin Lim
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Control Algorithm ◽  
Access Network ◽  
Mobility Prediction ◽  
Switching Operation ◽  
Cloud Radio Access Network ◽  
Radio Access ◽  
Power Control Algorithm ◽  
Heterogeneous Cloud

In 5G networks, heterogeneous cloud radio access network (H-CRAN) is considered a promising future architecture to minimize energy consumption and efficiently allocate resources. However, with the increase in the number of users, studies are performed to overcome the energy consumption problems. In this study, we propose a power control algorithm with mobility prediction to provide a high-energy efficiency for 5G H-CRAN. In particular, the proposed algorithm predicts UE mobility in vehicular mobility scenarios and performs remote radio head (RRH) switching operations based on % prediction results. We formulate an optimization problem to maximize the energy efficiency while satisfying the outage probability requirement. We then propose an RRH switching operation based on Markov mobility prediction and optimize the transmission power based on a gradient method. Simulation results demonstrate the improved energy efficiency compared with those of existing RRH switching-operation algorithms.

Study on Network Load Balancing Algorithm Based on Server Cluster System

2012 ◽  
Vol 182-183 ◽  
pp. 1978-1981 ◽  
Author(s):  
Li Lan ◽  
Chu Huan Qi
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Cluster System ◽  
Utilization Efficiency ◽  
Server Cluster ◽  
Use Of Resources ◽  
Network Load ◽  
System Resource ◽  
Network Load Balancing ◽  
Load Balancing Algorithm

The utilization efficiency of system resources is a key issue for cluster system while load balance is an important tool to realize the efficient use of resources. Based on server cluster system, this paper puts forwards an improved self-adaptive algorithm for network load balancing. Simulation results show that the algorithm can improve the utilization efficiency of system resource and reduce the server’s response time so as to achieve the request of real time when dealing with tasks and high availability of system.

scholarly journals An Optimum User Association Algorithm in Heterogeneous 5G Networks Using Standard Deviation of the Load

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1495
Author(s):  
Noha Hassan ◽  
Xavier Fernando
Keyword(s):  
Energy Efficiency ◽  
Standard Deviation ◽  
High Power ◽  
Coverage Probability ◽  
Base Station ◽  
Base Stations ◽  
Superior Performance ◽  
User Association ◽  
Optimum Number ◽  
Network Load

Fifth-generation (5G) wireless networks and beyond will be heterogeneous in nature, with a mixture of macro and micro radio cells. In this scenario where high power macro base stations (MBS) coexist with low power micro base stations (mBS), it is challenging to ensure optimal usage of radio resources to serve users with a multitude of quality of service (QoS) requirements. Typical signal to interference and noise ratio (SINR)-based user allocation protocols unfairly assign more users to the high power MBS, starving mBS. There have been many attempts in the literature to forcefully assign users to mBS with limited success. In this paper, we take a different approach using second order statistics of user data, which is a better indicator of traffic fluctuations. We propose a new algorithm for user association to the appropriate base station (BS) by utilizing the standard deviation of the overall network load. This is done through an exhaustive search of the best user equipment (UE)–BS combinations that provide a global minimum to the standard deviation. This would correspond to the optimum number of UEs assigned to every BS, either macro or micro. We have also derived new expressions for coverage probability and network energy efficiency for analytical performance evaluation. Simulation results prove the validity of our proposed methods to balance the network load, improve data rate, average energy efficiency, and coverage probability with superior performance compared with other algorithms.

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