Back to Search
Start Over
Deep spectrum prediction in high frequency communication based on temporal-spectral residual network
- Source :
- China Communications. 15:25-34
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2018.
-
Abstract
- High frequency (HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on long-term and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.
- Subjects :
- Computer Networks and Communications
business.industry
Computer science
Deep learning
Supervised learning
020206 networking & telecommunications
02 engineering and technology
Function (mathematics)
Residual
computer.software_genre
Convolutional neural network
Automatic link establishment
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
Data mining
Electrical and Electronic Engineering
business
Focus (optics)
computer
Subjects
Details
- ISSN :
- 16735447
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- China Communications
- Accession number :
- edsair.doi...........474e312b12bdb949e6ab45674f7fb125