Deep data hiding‐based channel state information feedback within audio signals
| Title: | Deep data hiding‐based channel state information feedback within audio signals |
|---|---|
| Authors: | Yun Hu, Ronghui Zhang |
| Source: | Electronics Letters, Vol 59, Iss 13, Pp n/a-n/a (2023) |
| Publisher Information: | Wiley, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: | artificial intelligence, multiple‐input multiple‐output communication, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: | Abstract Deep learning‐based channel state information (CSI) hiding within images has been introduced to eliminate the downlink CSI feedback overhead in frequency division duplexing systems. In this letter, a deep data hiding‐based CSI feedback framework (named Au_EliCsiNet), which hides/superimposes downlink CSI within the transmitted audio signals, is proposed. Convolution neural networks are adopted to extract CSI features, hide CSI within audio signals, and reconstruct CSI from the audio signals. Simulation results show that the proposed Au_EliCsiNet can feed back downlink CSI accurately with no (or fewer) effects on the original audio transmission. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1350-911X 0013-5194 |
| Relation: | https://doaj.org/toc/0013-5194; https://doaj.org/toc/1350-911X |
| DOI: | 10.1049/ell2.12854 |
| Access URL: | https://doaj.org/article/5c85c34de885491ea0a29c8e0c450e9b |
| Accession Number: | edsdoj.5c85c34de885491ea0a29c8e0c450e9b |
| Database: | Directory of Open Access Journals |
| ISSN: | 1350911X 00135194 |
|---|---|
| DOI: | 10.1049/ell2.12854 |
| Published in: | Electronics Letters |
| Language: | English |