A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks
This paper presents a Vehicle-to-Vehicle (V2V) communication modeling framework that addresses the challenges of reliable state estimation and beamforming control in dynamic, multi-lane road environments. By integrating an extended Unscented Kalman Filter (UKF) with adaptive process and measurement...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/11083750/ |
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| author | Nivetha Kanthasamy Raghvendra V. Cowlagi Alexander Wyglinski |
| author_facet | Nivetha Kanthasamy Raghvendra V. Cowlagi Alexander Wyglinski |
| author_sort | Nivetha Kanthasamy |
| collection | DOAJ |
| description | This paper presents a Vehicle-to-Vehicle (V2V) communication modeling framework that addresses the challenges of reliable state estimation and beamforming control in dynamic, multi-lane road environments. By integrating an extended Unscented Kalman Filter (UKF) with adaptive process and measurement noise models, the proposed approach accurately tracks vehicle trajectories under abrupt speed variations, frequent lane changes, and adverse weather conditions. A Markov chain-based lane-switching mechanism enables realistic multi-lane traffic simulations with smooth centerline trajectories spanning straight and curved road segments. To further enhance robustness, an adaptive Minimum Variance Distortionless Response (MVDR) beamforming scheme compensates for beam misalignment and mitigates interference, thereby significantly improving the Signal-to-Interference-Plus-Noise Ratio (SINR). The results demonstrate that the framework not only offers improved positioning accuracy but also achieves reliable communication performance compared to conventional methods, reinforcing its effectiveness in complex vehicular scenarios. |
| format | Article |
| id | doaj-art-b584eb2a6bec4fc1ac135e3e9f2bcb6d |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-b584eb2a6bec4fc1ac135e3e9f2bcb6d2025-08-20T03:43:52ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-0162085210010.1109/OJVT.2025.359067311083750A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V NetworksNivetha Kanthasamy0https://orcid.org/0009-0002-4746-8682Raghvendra V. Cowlagi1https://orcid.org/0000-0002-9365-3021Alexander Wyglinski2https://orcid.org/0000-0002-3357-0064Worcester Polytechnic Institute, Worcester, MA, USAWorcester Polytechnic Institute, Worcester, MA, USAWorcester Polytechnic Institute, Worcester, MA, USAThis paper presents a Vehicle-to-Vehicle (V2V) communication modeling framework that addresses the challenges of reliable state estimation and beamforming control in dynamic, multi-lane road environments. By integrating an extended Unscented Kalman Filter (UKF) with adaptive process and measurement noise models, the proposed approach accurately tracks vehicle trajectories under abrupt speed variations, frequent lane changes, and adverse weather conditions. A Markov chain-based lane-switching mechanism enables realistic multi-lane traffic simulations with smooth centerline trajectories spanning straight and curved road segments. To further enhance robustness, an adaptive Minimum Variance Distortionless Response (MVDR) beamforming scheme compensates for beam misalignment and mitigates interference, thereby significantly improving the Signal-to-Interference-Plus-Noise Ratio (SINR). The results demonstrate that the framework not only offers improved positioning accuracy but also achieves reliable communication performance compared to conventional methods, reinforcing its effectiveness in complex vehicular scenarios.https://ieeexplore.ieee.org/document/11083750/V2V communicationunscented kalman filteradaptive MVDR beamforminginterference managementtrajectory trackingSINR optimization |
| spellingShingle | Nivetha Kanthasamy Raghvendra V. Cowlagi Alexander Wyglinski A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks IEEE Open Journal of Vehicular Technology V2V communication unscented kalman filter adaptive MVDR beamforming interference management trajectory tracking SINR optimization |
| title | A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks |
| title_full | A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks |
| title_fullStr | A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks |
| title_full_unstemmed | A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks |
| title_short | A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks |
| title_sort | unified framework for adaptive beamforming and state estimation in dynamic multi lane v2v networks |
| topic | V2V communication unscented kalman filter adaptive MVDR beamforming interference management trajectory tracking SINR optimization |
| url | https://ieeexplore.ieee.org/document/11083750/ |
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