Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS
The integration of orthogonal time frequency space signals into integrated sensing and communication systems has emerged as a highly promising approach for constructing intelligent transportation systems. Among these advancements, beamforming prediction techniques that incorporate multiple-input mul...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10849526/ |
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| author | Chunyue Wang Yong Wang Huiming Zheng Yunhao Chai Ying Dong |
| author_facet | Chunyue Wang Yong Wang Huiming Zheng Yunhao Chai Ying Dong |
| author_sort | Chunyue Wang |
| collection | DOAJ |
| description | The integration of orthogonal time frequency space signals into integrated sensing and communication systems has emerged as a highly promising approach for constructing intelligent transportation systems. Among these advancements, beamforming prediction techniques that incorporate multiple-input multiple-output technology have gained widespread attention. However, challenges remain in acquiring initial high-precision state parameters of vehicles and establishing continuous, reliable communication links in high-mobility scenarios. In this paper, we propose a novel MIMO-OTFS beamforming prediction scheme, leveraging a continuous-delay-and-Doppler-shift channel to facilitate information exchange between vehicles and the roadside unit. Furthermore, we develop a three-dimensional parameter estimation algorithm named orthogonal matching pursuit based on maximum likelihood dictionary correction, which offers high accuracy and low complexity. Utilizing this algorithm, we achieve precise multi-target beamforming prediction without approximation. Simulation results demonstrate that our approach significantly outperforms the traditional unscented Kalman filter method based on matched filtering in terms of multi-target beamforming prediction in the ISAC system. |
| format | Article |
| id | doaj-art-0e55c1c314ed46cb9fd0b53db09bc790 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0e55c1c314ed46cb9fd0b53db09bc7902025-01-31T00:01:57ZengIEEEIEEE Access2169-35362025-01-0113166231663610.1109/ACCESS.2025.353282710849526Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFSChunyue Wang0https://orcid.org/0000-0002-7717-8494Yong Wang1Huiming Zheng2Yunhao Chai3Ying Dong4https://orcid.org/0000-0001-8505-5825College of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaThe integration of orthogonal time frequency space signals into integrated sensing and communication systems has emerged as a highly promising approach for constructing intelligent transportation systems. Among these advancements, beamforming prediction techniques that incorporate multiple-input multiple-output technology have gained widespread attention. However, challenges remain in acquiring initial high-precision state parameters of vehicles and establishing continuous, reliable communication links in high-mobility scenarios. In this paper, we propose a novel MIMO-OTFS beamforming prediction scheme, leveraging a continuous-delay-and-Doppler-shift channel to facilitate information exchange between vehicles and the roadside unit. Furthermore, we develop a three-dimensional parameter estimation algorithm named orthogonal matching pursuit based on maximum likelihood dictionary correction, which offers high accuracy and low complexity. Utilizing this algorithm, we achieve precise multi-target beamforming prediction without approximation. Simulation results demonstrate that our approach significantly outperforms the traditional unscented Kalman filter method based on matched filtering in terms of multi-target beamforming prediction in the ISAC system.https://ieeexplore.ieee.org/document/10849526/Integrated sensing and communicationorthogonal time frequency spacecontinuous-delay-and-Doppler-shiftmulti-target beamforming prediction |
| spellingShingle | Chunyue Wang Yong Wang Huiming Zheng Yunhao Chai Ying Dong Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS IEEE Access Integrated sensing and communication orthogonal time frequency space continuous-delay-and-Doppler-shift multi-target beamforming prediction |
| title | Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS |
| title_full | Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS |
| title_fullStr | Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS |
| title_full_unstemmed | Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS |
| title_short | Precision Sensing-Aided Multi-Target Beamforming Prediction in High-Mobility ISAC Systems Based on OTFS |
| title_sort | precision sensing aided multi target beamforming prediction in high mobility isac systems based on otfs |
| topic | Integrated sensing and communication orthogonal time frequency space continuous-delay-and-Doppler-shift multi-target beamforming prediction |
| url | https://ieeexplore.ieee.org/document/10849526/ |
| work_keys_str_mv | AT chunyuewang precisionsensingaidedmultitargetbeamformingpredictioninhighmobilityisacsystemsbasedonotfs AT yongwang precisionsensingaidedmultitargetbeamformingpredictioninhighmobilityisacsystemsbasedonotfs AT huimingzheng precisionsensingaidedmultitargetbeamformingpredictioninhighmobilityisacsystemsbasedonotfs AT yunhaochai precisionsensingaidedmultitargetbeamformingpredictioninhighmobilityisacsystemsbasedonotfs AT yingdong precisionsensingaidedmultitargetbeamformingpredictioninhighmobilityisacsystemsbasedonotfs |