DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing
DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing
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DeepTxFinder：众包频谱感知中通过深度学习进行多发射机定位
DOI：
10.1109/icccn49398.2020.9209727
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
F. Dressler
中科院分区：
文献类型：
--
作者：
A. Zubow;S. Bayhan;P. Gawłowicz;F. Dressler
随着移动数据量的不断增加和网络的超密集部署，无线频谱已经成为瓶颈资源，在时间、空间和频率维度上更加灵活地使用频谱至关重要。然而，灵活的频谱分配带来的更高的频谱使用效率是有代价的，即增加了频谱监控和管理的复杂性。确定发射机尤其符合频谱执法当局的利益，以确保频谱的合法用户按照预期使用频谱。对于可扩展、高效和高精度的操作，我们提出了一种基于人群感知的解决方案，其中传感设备将其测量的接收功率电平报告给中央实体，该中央实体随后融合收集的信息以定位未知数量的发射机。我们的解决方案DeepTxFinder利用深度学习来处理操作环境中的许多不确定性来源：即发射机数量、其发射功率水平和信道条件(阴影)。利用深度学习，DeepTxFinder将自己与以前的最先进技术区分开来，后者需要了解发射机的数量和发射功率，或者要求发射机在空间中间隔数十至数百米，使其不适合应用于预期的超密集小蜂窝部署。此外，我们还提出了一种基于分块的方法，通过降低计算复杂度来增加方案的可扩展性。我们的模拟研究表明，DeepTxFinder即使只从非常少量的传感器收集数据，也可以提供高检测精度。更具体地说，在传感器密度为1%-2%的情况下，DeepTxFinder可以高概率地估计出发射机的数量和位置，这证明了稀疏感知的可行性。
As the radio spectrum has become the bottleneck resource with increasing volume of mobile data and ultra-dense network deployments, it is crucial to use spectrum more flexibly in time, space, and frequency dimensions. However, higher efficiency in spectrum usage facilitated by flexible spectrum allocation comes with a cost, namely the increased complexity of spectrum monitoring and management. Identifying the transmitters is at the interest of particularly spectrum enforcement authorities to ensure that spectrum is used as intended by the legitimate users of the spectrum. For a scalable, efficient, and highly-accurate operation, we propose a crowd-sensing based solution where sensing devices report their measured receive power levels to a central entity which later fuses the collected information for localizing an unknown number of transmitters. Our solution, referred to as DeepTxFinder, leverages deep learning to handle many sources of uncertainty in the operation environment: namely number of transmitters, their transmission power levels, and channel conditions (shadowing). Using deep-learning, DeepTxFinder distinguishes itself from the prior state-of-the art which requires knowledge of the number and transmission power of transmitters or require the transmitters to be well separated in space by tens to hundreds of meters making them ill-suited for application in expected ultra-dense deployment of small-cells. Moreover, we propose a tiling-based approach to increase the scalability of our proposal by reducing the computational complexity. Our simulation studies show that DeepTxFinder can provide a high detection accuracy even only by collecting data from a very small number of sensors. More specifically, with 1 %–2 % sensor density DeepTxFinder can estimate the number of transmitters and their locations with high probability which proves that sparse sensing is feasible.
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DOI：
10.1109/infocom41043.2020.9155230
发表时间：
2020-07
期刊：
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
影响因子：
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作者：
A. Bhattacharya;Caitao Zhan;Himanshu Gupta;Samir R Das;P. Djurić
通讯作者：
A. Bhattacharya;Caitao Zhan;Himanshu Gupta;Samir R Das;P. Djurić
DOI：
10.1109/mass.2018.00064
发表时间：
2018-10
期刊：
2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
影响因子：
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作者：
Xiaonan Zhang;Pei Huang;Qi Jia;Linke Guo
通讯作者：
Xiaonan Zhang;Pei Huang;Qi Jia;Linke Guo
DOI：
10.1109/dyspan.2019.8935739
发表时间：
2019-11
期刊：
2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
影响因子：
--
作者：
Mohammad Ghaderibaneh;Mallesham Dasari;Himanshu Gupta
通讯作者：
Mohammad Ghaderibaneh;Mallesham Dasari;Himanshu Gupta
使用频谱观测站启用全国范围的无线电频率清单
影响因子：
7.9
作者：
Zheleva, Zhivkova Mariya;Chandra, Ranveer;Chowdhery, Aakanksha;Garnett, Paul;Gupta, Anoop;Kapoor, Ashish;Valerio, Matt
通讯作者：
Valerio, Matt
DOI：
10.1109/sahcn.2019.8824872
发表时间：
2019-05
期刊：
2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
影响因子：
--
作者：
M. Joneidi;H. Yazdani;A. Vosoughi;Nazanin Rahnavard
通讯作者：
M. Joneidi;H. Yazdani;A. Vosoughi;Nazanin Rahnavard