A D2D user pairing algorithm based on motion prediction and power control

Author:

Huang Zhifeng1ORCID,Ke Feng2,Song Hui1

Affiliation:

1. School of Electronic and Information Engineering South China Normal University Guangzhou China

2. School of Electronic and Information Engineering South China University of Technology Guangzhou China

Abstract

AbstractUser pairing plays an important role in device‐to‐device (D2D) relay communication, contributing significantly to maintaining low energy consumption, high throughput, and overall energy efficiency in the communication system. To achieve these purposes, an attention‐based long short‐term memory motion prediction model (AT‐LSTM) and propose a joint power control algorithm. Leveraging these techniques, we also propose a D2D user pairing algorithm, distance–power–SINR pairing algorithm (DPSPA), which comprehensively considers factors such as D2D communication distances, transmit power, and signal‐to‐interference‐plus‐noise ratio. Initially, the AT‐LSTM model is utilized to predict the location of users. Subsequently, the distance between the user terminal device and each communication point and the base station, filtering cache points, and non‐cache points within the D2D communication radius are calculated. Then, based on the distance, required transmission power, and signal‐to‐interference‐plus‐noise ratio of each point, the evaluation index (the best matching product) is obtained. Finally, the point with the maximum best matching product is selected for D2D direct communication mode, D2D relay communication mode, or cellular communication mode. Simulation results demonstrate that DPSPA effectively reduces system energy consumption, enhances system throughput, and improves overall energy efficiency.

Funder

National Youth Foundation of China

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3