Academic Journal
An automatic modulation classification network for IoT terminal spectrum monitoring under zero-sample situations
| Title: | An automatic modulation classification network for IoT terminal spectrum monitoring under zero-sample situations |
|---|---|
| Authors: | Quan Zhou, Ronghui Zhang, Fangpei Zhang, Xiaojun Jing |
| Source: | EURASIP Journal on Wireless Communications and Networking, Vol 2022, Iss 1, Pp 1-18 (2022) |
| Publisher Information: | SpringerOpen, 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Telecommunication LCC:Electronics |
| Subject Terms: | Internet of things, Automatic modulation classification, Zero-shot learning, Generative adversarial network, Telecommunication, TK5101-6720, Electronics, TK7800-8360 |
| More Details: | Abstract Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment we live. However, the proliferation of IoT devices has led to the misuse of spectrum resources, and many IoT devices occupy the frequency band without permission. As a consequence, the spectrum regulation has become an essential part of the development of IoT. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. Generally, deep learning (DL)-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. The accuracy of one unseen class and two unseen classes exceeds 90% and 76%, respectively. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1687-1499 |
| Relation: | https://doaj.org/toc/1687-1499 |
| DOI: | 10.1186/s13638-022-02099-2 |
| Access URL: | https://doaj.org/article/c6bdd017596f46a495f7d214b7c92325 |
| Accession Number: | edsdoj.6bdd017596f46a495f7d214b7c92325 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 16871499 |
|---|---|
| DOI: | 10.1186/s13638-022-02099-2 |
| Published in: | EURASIP Journal on Wireless Communications and Networking |
| Language: | English |