版权说明 操作指南
首页 > 成果 > 详情

RadioCycle: Deep Dual Learning based Radio Map Estimation

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Zheng, Yi;Zhang, Tianqian;Liao, Cunyi;Wang, Ji;Liu, Shouyin
通讯作者:
Liu, SY
作者机构:
[Wang, Ji; Liu, Shouyin; Zhang, Tianqian; Liao, Cunyi; Liu, SY; Zheng, Yi] Cent China Normal Univ, Dept Elect & Informat Engn, Wuhan 430079, Peoples R China.
通讯机构:
[Liu, SY ] C
Cent China Normal Univ, Dept Elect & Informat Engn, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Mean square error;Mobile telecommunication systems;Deep learning;MAP estimation;Radio maps;Receiving power;Reference signal receiving power;Reference signals;Signal receiving;U-net;Urban building map;Urban buildings;Deep learning
期刊:
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
ISSN:
1976-7277
年:
2022
卷:
16
期:
11
页码:
3780-3797
基金类别:
This work was supported in part by the Key Research and Development Program of Hubei Province under Grant 2021BAA170, in part by the National Natural Science Foundation of China under Grant 62101205, in part by the Natural Science Foundation of Hubei Province under Grant 2021CFB248, and in part by the Fundamental Research Funds for the Central Universities of China under grant CCNU20QN004.
机构署名:
本校为第一且通讯机构
院系归属:
物理科学与技术学院
摘要:
The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based stru...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com