A Spatiotemporal Approach for Secure Crowdsourced Radio Environment Map Construction
A Spatiotemporal Approach for Secure Crowdsourced Radio Environment Map Construction
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DOI：
10.1109/tnet.2020.2992939
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发表时间：
2020-05
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影响因子：
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通讯作者：
Yidan Hu;Rui Zhang
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文献类型：
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作者：
Yidan Hu;Rui Zhang
数据库驱动的动态频谱共享（DSS）是联邦通信委员会为提高频谱效率而采用的事实上的技术范例，其允许授权频谱被次级用户机会性地使用。在数据库驱动的DSS中，地理位置数据库管理员（DBA）以无线电环境地图（REM）的形式维护其服务区域上的频谱可用性信息，其中在每个位置处来自主用户的接收信号强度经由频谱感测直接测量或经由统计空间内插估计。基于众包的频谱感知是一种很有前途的方法，定期收集频谱测量在一个大的地理区域，但不幸的是，容易受到错误的频谱测量。尽管在安全合作频谱感知方面已有大量的工作，但如何在存在错误测量的情况下构建准确的REM仍然是一个公开的挑战。在本文中，我们介绍了ST-REM，一种新的时空方法，安全地构建一个REM中存在的虚假频谱测量。受为半监督学习开发的自标记技术的启发，ST-REM迭代地从来自可信锚传感器的少量频谱测量和来自移动的用户的更多测量构建REM。在每次迭代期间，DBA通过联合考虑其与其他可信测量的空间适应度和移动的用户的长期行为来评估每个测量的可信度。通过逐渐结合最值得信赖的频谱测量，DBA能够构建具有高精度的REM。使用真实的光谱测量数据集的广泛模拟研究证实了ST-REM的功效和效率。
Database-driven Dynamic Spectrum Sharing (DSS) is the de-facto technical paradigm adopted by Federal Communications Commission for increasing spectrum efficiency, which allows licensed spectrum to be opportunistically used by secondary users. In database-driven DSS, a geo-location database administrator (DBA) maintains spectrum availability information over its service region in the form of a Radio Environment Map (REM), where the received signal strength from the primary user at every location is either directly measured via spectrum sensing or estimated via statistical spatial interpolation. Crowdsourcing-based spectrum sensing is a promising approach for periodically collecting spectrum measurements over a large geographic area but is unfortunately vulnerable to false spectrum measurements. Despite a large body of prior work on secure cooperative spectrum sensing, how to construct an accurate REM in the presence of false measurements remains an open challenge. In this paper, we introduce ST-REM, a novel spatiotemporal approach for securely constructing an REM in the presence of false spectrum measurements. Inspired by the self-label techniques developed for semi-supervised learning, ST-REM iteratively constructs an REM from a small number of spectrum measurements from trusted anchor sensors and many more measurements from mobile users. During each iteration, the DBA evaluates the trustworthiness of each measurement by jointly considering its spatial fitness with other trusted measurements and the mobile user’s long-term behavior. By gradually incorporating the most trustworthy spectrum measurements, the DBA is able to construct a REM with high accuracy. Extensive simulation studies using a real spectrum measurement dataset confirm the efficacy and efficiency of ST-REM.