Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation
Symbiotic radio (SR) holds promise for large-scale Internet of Things (IoT) networks by allowing both primary and secondary systems to share spectrum resources for mutually beneficial transmission. However, in high-mobility scenarios, rapidly changing channels pose significant challenges for reliabl...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11010104/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849715288319197184 |
|---|---|
| author | Taoyu Xie Siyao Li |
| author_facet | Taoyu Xie Siyao Li |
| author_sort | Taoyu Xie |
| collection | DOAJ |
| description | Symbiotic radio (SR) holds promise for large-scale Internet of Things (IoT) networks by allowing both primary and secondary systems to share spectrum resources for mutually beneficial transmission. However, in high-mobility scenarios, rapidly changing channels pose significant challenges for reliable and low-latency communications. To enable SR transmission over high-mobility channels, this paper explores the secondary transmission and equivalent channel estimation problems in intelligent reflecting surface (IRS)-SR systems utilizing orthogonal time frequency space (OTFS) modulation. The IRS is divided into multiple groups, each attached with informative frequencies, for secondary transmission. Exploiting the spectrum structure induced by the secondary information, a deep learning (DL)-based secondary information detector is proposed. Moreover, after transforming the channel estimation problem into a compressed sensing (CS) problem, a DL-sparse Bayesian learning (SBL) network is proposed to improve the estimation accuracy and ease the computational afford by avoiding the large amount of iterations in conventional CS algorithms, e.g. the SBL algorithm. Finally, numerical results are provided to illustrate the superiority of the proposed detector and estimator over several benchmarks. |
| format | Article |
| id | doaj-art-ee4ed630e7bf42a68505cf1eb5bba7b1 |
| institution | DOAJ |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-ee4ed630e7bf42a68505cf1eb5bba7b12025-08-20T03:13:27ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0161872188010.1109/OJVT.2025.357238711010104Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space ModulationTaoyu Xie0https://orcid.org/0009-0005-8229-9644Siyao Li1https://orcid.org/0000-0002-1893-7876College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, ChinaDepartment of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, Daytona Beach, FL, USASymbiotic radio (SR) holds promise for large-scale Internet of Things (IoT) networks by allowing both primary and secondary systems to share spectrum resources for mutually beneficial transmission. However, in high-mobility scenarios, rapidly changing channels pose significant challenges for reliable and low-latency communications. To enable SR transmission over high-mobility channels, this paper explores the secondary transmission and equivalent channel estimation problems in intelligent reflecting surface (IRS)-SR systems utilizing orthogonal time frequency space (OTFS) modulation. The IRS is divided into multiple groups, each attached with informative frequencies, for secondary transmission. Exploiting the spectrum structure induced by the secondary information, a deep learning (DL)-based secondary information detector is proposed. Moreover, after transforming the channel estimation problem into a compressed sensing (CS) problem, a DL-sparse Bayesian learning (SBL) network is proposed to improve the estimation accuracy and ease the computational afford by avoiding the large amount of iterations in conventional CS algorithms, e.g. the SBL algorithm. Finally, numerical results are provided to illustrate the superiority of the proposed detector and estimator over several benchmarks.https://ieeexplore.ieee.org/document/11010104/Channel estimationdeep learningintelligent reflecting surfaceorthogonal time frequency spacesparse Bayesian learningsymbiotic radio |
| spellingShingle | Taoyu Xie Siyao Li Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation IEEE Open Journal of Vehicular Technology Channel estimation deep learning intelligent reflecting surface orthogonal time frequency space sparse Bayesian learning symbiotic radio |
| title | Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation |
| title_full | Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation |
| title_fullStr | Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation |
| title_full_unstemmed | Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation |
| title_short | Secondary Transmission and Channel Estimation for Symbiotic Radio With Orthogonal Time Frequency Space Modulation |
| title_sort | secondary transmission and channel estimation for symbiotic radio with orthogonal time frequency space modulation |
| topic | Channel estimation deep learning intelligent reflecting surface orthogonal time frequency space sparse Bayesian learning symbiotic radio |
| url | https://ieeexplore.ieee.org/document/11010104/ |
| work_keys_str_mv | AT taoyuxie secondarytransmissionandchannelestimationforsymbioticradiowithorthogonaltimefrequencyspacemodulation AT siyaoli secondarytransmissionandchannelestimationforsymbioticradiowithorthogonaltimefrequencyspacemodulation |