Matchmaking of Volunteers and Channels for Dynamic Spectrum Access Enforcement
Matchmaking of Volunteers and Channels for Dynamic Spectrum Access Enforcement
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DOI：
10.1109/globecom42002.2020.9322635
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Debarun Das;T. Znati;M. Weiss;Marcela M. Gomez;Pedro J. Bustamante;J. Rose
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文献类型：
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作者：
Debarun Das;T. Znati;M. Weiss;Marcela M. Gomez;Pedro J. Bustamante;J. Rose
无线网络中动态频谱共享的成功依赖于频谱接入策略的可靠自动执行。本文采用众包的方法选择志愿者来检测频谱滥用。志愿者的选择是基于多种标准的，包括他们的声誉、在一个地区的可能性以及有效发现渠道滥用的能力。我们将志愿者选择问题表述为一个稳定匹配问题，即志愿者的监控偏好与渠道属性匹配。给定一组志愿者，目标是确保频谱执法区域的最大覆盖范围，并准确检测该区域所有信道的频谱接入违规行为。志愿匹配（VM）和反向志愿匹配（RVM）这两种匹配算法是基于Gale-Shapley算法的变体来实现稳定匹配的。我们还提出了两种混合算法，Hybrid - vm和Hybrid - rvm，它们通过基于秘书的算法来增强匹配算法，以克服单个香草算法的缺点。仿真结果表明，与其他测试算法相比，使用HYBRID-VM进行志愿者选择具有更好的区域覆盖率（与基于阈值的秘书算法相比提高了19.2%），更好的检测准确性和更好的志愿者幸福感。
The success of dynamic spectrum sharing in wireless networks depends on reliable automated enforcement of spectrum access policies. In this paper, a crowdsourced approach is used to select volunteers to detect spectrum misuse. Volunteer selection is based on multiple criteria, including their reputation, likelihood of being in a region and ability to effectively detect channel misuse. We formulate the volunteer selection problem as a stable matching problem, whereby, volunteers’ monitoring preferences are matched to channels’ attributes. Given a set of volunteers, the objective is to ensure maximum coverage of the spectrum enforcement area and accurate detection of spectrum access violation of all channels in the area. The two matching algorithms, Volunteer Matching (VM) and Reverse Volunteer Matching (RVM) are based on variants of the Gale-Shapley algorithm for stable matching. We also propose two Hybrid algorithms, HYBRID-VM and HYBRID-RVM that augment the matching algorithms with a Secretary-based algorithm to overcome the shortcomings of the individual vanilla algorithms. Simulation results show that volunteer selection by using HYBRID-VM gives better coverage of region (better by 19.2% when compared to threshold-based Secretary algorithm), better accuracy of detection and better volunteer happiness when compared to the other algorithms that are tested.