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.
引用
收藏
页数：2
相关论文
共 50 条
  • [21] Can Eruptions Be Predicted? Short-Term Prediction of Volcanic Eruptions via Attention-Based Long Short-Term Memory
    Le, Hiep, V
    Murata, Tsuyoshi
    Iguchi, Masato
    THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 34 : 13320 - 13325
  • [22] Prediction of Short-term Load of Microgrid Based on Multivariable and Multistep Long Short-term Memory
    Li, Dashuang
    SENSORS AND MATERIALS, 2022, 34 (04) : 1275 - 1285
  • [23] Smart occupancy detection system based on long short-term memory units
    Husnain, Asif
    Choe, Tae-Young
    Journal of Computers (Taiwan), 2020, 31 (05) : 159 - 175
  • [24] ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE
    Dogan, Erdem
    SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT, 2020, 107 : 19 - 32
  • [25] RETRACTED ARTICLE: Detection of malware on the internet of things and its applications depends on long short-term memory network
    K. Priyadarsini
    Nilamadhab Mishra
    M. Prasad
    Varun Gupta
    Syed Khasim
    Journal of Ambient Intelligence and Humanized Computing, 2022, 13 : 31 - 31
  • [26] Long short-term memory neural network for glucose prediction
    Carrillo-Moreno, Jaime
    Perez-Gandia, Carmen
    Sendra-Arranz, Rafael
    Garcia-Saez, Gema
    Hernando, M. Elena
    Gutierrez, Alvaro
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (09): : 4191 - 4203
  • [27] Enhanced Long Short-Term Memory Model for Runoff Prediction
    Feng, Rui
    Fan, Guangwei
    Lin, Jianyi
    Yao, Baozhen
    Guo, Qinghai
    JOURNAL OF HYDROLOGIC ENGINEERING, 2021, 26 (02)
  • [28] Long Short-Term Memory Network for Wireless Channel Prediction
    Tong, Xiaoyun
    Sun, Songlin
    SIGNAL AND INFORMATION PROCESSING, NETWORKING AND COMPUTERS, 2018, 473 : 19 - 26
  • [29] Long short-term memory
    Hochreiter, S
    Schmidhuber, J
    NEURAL COMPUTATION, 1997, 9 (08) : 1735 - 1780
  • [30] Design of internet of things online oral English teaching platform based on long-term and short-term memory network
    Chen, Shunmeng
    INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING, 2021, 31 (01) : 104 - 118
12345