Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks

Paikaray, Divya; Chhabra, Divyanshi; Sharma, Sachin; Goswami, Sachin; Shashikala, H K; Jethava, Prof. Gordhan

doi:10.17762/ijcnis.v14i3.5605

Cited by 8 publications

(3 citation statements)

References 17 publications

(26 reference statements)

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“…One technique widely studied for optimizing energy consumption in radio access networks is the association between the base station (BS) and users. Previous studies have proposed various algorithms or methods of UE-BS association with the primary objective of load balancing, quality of service, spectral efficiency, and moderate energy efficiency [14], [15]. However, in heterogeneous networks, traditional association techniques such as Max-SINR or maximum achievable throughput, which rely mainly on the power received by the mobile, are no longer suitable due to the differences in transmission power between macrocells and small cells.…”

Section: Related Workmentioning

confidence: 99%

Towards Sustainable 5G Networks: A Proposed Coordination Solution for Macro and Pico Cells to Optimize Energy Efficiency

et al. 2023

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The 5th generation of mobile communications, currently being rolled out, aims to improve network performance and efficiency over the older generations. With mmWaves, densified cell deployment is necessary for 5G networks, increasing capacity and improving network coverage. This imposes a considerable increase in the energy consumption of the 5G stations, which not only increases operating expenses for operators but also burdens the environment. Optimizing the energy consumption of 5G networks would be necessary to curb the energy curve. In this context, this paper presents a new algorithm called Energy Consumption Optimization Algorithm (ECOA), which combines cell selection and standby techniques to optimize energy consumption while preserving network performance. A comparison is conducted between ECOA and standard cell selection modes to evaluate the performance of the conventional approach. Our algorithm exhibits good performance, particularly in high-density, high-load scenarios. For instance, in a site with 25 Pico base stations serving 500 users, our algorithm achieves an average throughput of 23 Mb/s per user while consuming 1750.75 W of energy. This represents a 2.44% increase in energy consumption compared to the optimal solution.

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Section: Related Workmentioning

confidence: 99%

Towards Sustainable 5G Networks: A Proposed Coordination Solution for Macro and Pico Cells to Optimize Energy Efficiency

et al. 2023

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“…For the above analysis, some scholars believe that cloud computing service management needs to screen data and reduce the proportion of redundant data to optimize the analysis results [8]. Based on the above research, this paper uses machine learning algorithms to analyze cloud computing service management, optimize the data structure in the management process, reduce the complexity of data, and aim to improve the level of cloud computing service management.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study on Machine Learning for Enterprise Cloud Computing Service Management

Liu

2023

AJEBA

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A machine learning method is proposed to aim at the problems of large data processing, complex data, and limited system resources in cloud computing service management. First, multi-department analysis is carried out on cloud service management data, and relevant management data is standardized to form a standardized management data collection; Machine learning is used to classify, mine, and extract service management data, and corresponding management measures are taken promptly to improve the management level of cloud computing services. MATLAB simulation shows that machine learning can improve the level of cloud computing service management, simplify the process of cloud computing service management, shorten the management time of cloud computing services, and meet the actual needs of service management.

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“…Modelling the game in which the evader is clever and has speci c detecting abilities has garnered increasing interest [12,13]. Competitive coevolution, multiagent methods, and multiagent communicating techniques were employed as remedies in additional investigations [14,15]. A multiagent pursuit-evasion game requires that a set of agents persuade a different set of agents (evaders) to cooperate in order to be apprehended [16,17].…”

Section: Introductionmentioning

confidence: 99%

Cooperative control for multi-player pursuit-evasion games embedded on communication technology with reinforcement learning

kavin,

Kamala

et al. 2023

Preprint

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Recent advances in research on the Multi-agent System (MAS) optimal control issue will help sectors like robotics, communications, and power systems. This work looks at the intelligent design of a large-scale multi-pursuer and multi-evader pursuit-evasion game. Based on reinforcement learning, a distributed cooperative pursuit method with communication is created. The famed Curse of Dimensionality poses a serious danger to multi-player pursuit-evasion game designs due to the sheer number of agents, especially in hostile areas where there aren't many communication options available to encourage player information exchange. In order to find the best pursuit-evasion strategies using a novel type of probability density function (PDF) rather than exhaustive data from all the remaining teams or agents, the Mean Field Games (MFG) theory has been used. A novel MAS optimum type oversight system with a decentralised and computer-friendly decision method is urgently needed. Mean field game theory is used to create the Actor-critic-mass (ACM), a decentralised optimal control system, to address the aforementioned issues. Additionally, the homogeneous decentralised Actor-critic-mass (HDACM) which improves the ACM method, does away with restrictions like homogeneous agents and cost functions. Finally, two applications make use of the PAS algorithm.

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Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks

Cited by 8 publications

References 17 publications

Towards Sustainable 5G Networks: A Proposed Coordination Solution for Macro and Pico Cells to Optimize Energy Efficiency

Towards Sustainable 5G Networks: A Proposed Coordination Solution for Macro and Pico Cells to Optimize Energy Efficiency

An Empirical Study on Machine Learning for Enterprise Cloud Computing Service Management

Cooperative control for multi-player pursuit-evasion games embedded on communication technology with reinforcement learning

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