Crowdsensing Based Spectrum Database with Total Variation Regularization

被引：0

作者：

Suto, Katsuya ^{[1
]}

Hashimoto, Riku ^{[2
]}

机构：

[1] Univ Electrocommun, Grad Sch Informat & Engn, Tokyo, Japan

[2] Univ Electrocommun, Dept Comp & Network Engn, Tokyo, Japan

来源：

2019 IEEE INTERNATIONAL SYMPOSIUM ON DYNAMIC SPECTRUM ACCESS NETWORKS (DYSPAN) | 2019年

关键词：

Crowdsensing; heterogeneous sensing; spectrum database; total variation regularization; RADIO ENVIRONMENT MAP;

D O I：

10.1109/dyspan.2019.8935664

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A concept of crowdsensing based spectrum database has a great deal of attention due to their ability to support largescale and low-cost spectrum monitoring by collecting spectrum data from a swarm of user devices. However, for accurate spectrum database construction, we still have the problem caused by crowdsensing, i.e., the low measurement accuracy of user devices and the measurement noise due to the multipath fading effect. As a remedy for the measurement issue, we develop a novel database reconstruction problem, referred to as TVR+, based on a path loss estimation and total variation regularization (TVR). Further, we propose an iterative algorithm with Split Bregman method to solve the TVR+ problem. Extensive simulations demonstrate that the reconstructed database with TVR+ significantly improves the accuracy.

引用

页码：503 / 508

页数：6

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