Loading [MathJax]/extensions/MathZoom.js
5G Ultradense Cellular-Network-Based Edge Demand Response: Energy Consumption Reduction | IEEE Journals & Magazine | IEEE Xplore

5G Ultradense Cellular-Network-Based Edge Demand Response: Energy Consumption Reduction


Abstract:

With the development of 5G technology, ultradense cellular networks are becoming a trend, while the deployment of large-area and high-density base stations (BSs) will bri...Show More

Abstract:

With the development of 5G technology, ultradense cellular networks are becoming a trend, while the deployment of large-area and high-density base stations (BSs) will bring new energy consumption problems. In this article, we explore the energy consumption of the edge demand response (EDR) from the two perspectives of edge users and edge facility providers. While guaranteeing the essential Quality of Experience (QoE), we try to improve the load balancing of edge servers and reduce system energy consumption. On the edge facility provider side, we mainly consider the physical machine turn-on problem and resource allocation of the two-phase EDR. On the user side, we start with user cost reduction and subchannel power allocation to emphasize the QoE. First, considering the density impacts of users and edge servers, we take inspiration from the classical PageRank algorithm and propose a method to calculate the edge nodes’ weights and, thus, determine the infrastructure’s state. Subsequently, combining distance factors, we design a subchannel power allocation method based on dynamic planning for 5G power-domain multiplexing nonorthogonal multiple access (PDM-NOMA). More importantly, based on the above work, we optimize the two-phase EDR process based on the upper confidence bound (UCB) algorithm of the multiarmed bandit (MAB) algorithm framework and dynamic planning. We compare the proposed QEL-UCB algorithm with two classical and five state-of-the-art algorithms on a real-world data set. The experimental results demonstrate that the proposed method improves by 18.52% in load balancing and reduces by 18.59% in energy consumption, which validates the method’s effectiveness.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 7, 01 April 2024)
Page(s): 12799 - 12814
Date of Publication: 28 November 2023

ISSN Information:

Funding Agency:

No metrics found for this document.

I. Introduction

With the proliferation of wireless communications and the dramatic growth of the Internet of Things (IoT) devices, data sources for big data are shifting from large-scale cloud data centers to an ever wider range of edge devices [1]. In the future Internet and wireless communication systems, massive data traffic processing will become a key feature, and high data rate and low delivery latency also become two key performances [2].

Usage
Select a Year
2025

View as

Total usage sinceNov 2023:379
010203040JanFebMarAprMayJunJulAugSepOctNovDec52439000000000
Year Total:68
Data is updated monthly. Usage includes PDF downloads and HTML views.
Contact IEEE to Subscribe

References

References is not available for this document.