Spectrum Occupancy Prediction for Internet of Things via Long Short-Term Memory

被引：0
|
作者：
Li, Haoyu [1 ]
Ding, Xiaojin [1 ,2 ,3 ]
Yang, Yiguang [1 ]
Huang, Xiaogu [1 ]
Zhang, Genxin [1 ]
机构：
[1] NUPT, Key Lab Broadband Wireless Commun & Sensor Networ, Nanjing, Peoples R China
[2] NUPT, Jiangsu Engn Res Ctr Commun & Network Technol, Nanjing, Peoples R China
[3] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW) | 2019年
基金：
中国国家自然科学基金;
关键词：
Internet of things; spectrum occupancy prediction; deep learning; long short-term memory;
D O I：
10.1109/icce-tw46550.2019.8991968
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
With the development of Internet of things (IoT), the demand on spectrum is increasing rapidly. Moreover, due to lack of power and the feature of short burst, the signals of IoT may be transmitted relying on accessing the idle spectrum, leading to a higher successful transmitting probability. Thus, the spectrum should be allocated in advance for the ongoing terminals of IoT. In this paper, a long short-term memory aided spectrum-prediction (LSTMSP) scheme has been conceived by analyzing the relationships between time and frequency of historical spectrum data. Performance evaluations on real-world spectrum data show that the accuracy of the spectrum occupancy prediction is above 0.7, demonstrating the benefits of the conceived LSTMSP method.
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页数：2
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