Modulation Recognition Algorithm based on Digital Communication Signal Time-Frequency Image

Li, Kuixian; Shi, Jie

doi:10.1109/dsa52907.2021.00114

Cited by 10 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, due to the serious degradation suffered by terahertz wireless links in outdoor scenarios, the SNR of the identification stage is always varying, resulting in a different SNR level in the training stage. Besides, a degraded performance of neural networks at low SNR levels has been reported [33]. Thus, the efficiency of the identification method with neural works should be evaluated under different SNR levels.…”

Section: Performance Under Different Weather Conditionsmentioning

confidence: 99%

Identification of Terahertz Link Modulation in Atmospheric Weather Conditions

Qiao

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

With the rapid increase of wireless connectivity, current spectrum resources are not enough for significant requirements for large data capacity. Research interests are moving towards the high-frequency band in the terahertz range for wider bandwidth. However, multipath scattering and induced time delay, suffered by terahertz links propagating in outdoor weather, lead inevitably to increasing expenses in baseband signal processing. This trades away the advantage of low time latency and high stability, which are commonly considered as important merits of terahertz wireless communication techniques. To reduce the burden in signal processing and explore the feasibility of modulation identification in the terahertz band, a method of wireless link modulation identification is considered as a potential solution. In this work, it is investigated theoretically by employing two kinds of neural networks: the convolutional neural network (CNN) and the long short-term memory network (LSTM). Link deterioration caused by different atmospheric weather is introduced into the theoretical model, and the performance of this method is evaluated. Results show that the identification accuracy of the constructed neural networks can be up to 99%, which means such a method is efficient for identification of the modulation format of terahertz wireless links under different weather conditions. This work demonstrates the feasibility of modulation identification in outdoor terahertz communication scenarios and provides specific references.

show abstract

Section: Performance Under Different Weather Conditionsmentioning

confidence: 99%

Identification of Terahertz Link Modulation in Atmospheric Weather Conditions

Qiao

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…STFT images of signals were used as a dataset to train the CNN, but no comparison was made between the performance of the STFT and other signal transformations like WVD and HHT. In [37], [38] and [39], an algorithm based on the fusion of features extracted by a CNN and a local binary pattern was proposed for modulation classification. Smooth pseudo WVD was used to preprocess signals.…”

Section: Similar Related Workmentioning

confidence: 99%