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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.13767 |
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| _version_ | 1866910751390695424 |
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| author | Nam, Taesik Lee, Seungjae Park, Kiwoong Kwon, Sunbeom Jeong, Nathan Jo, Han-Shin Yook, Jong-Gwan |
| author_facet | Nam, Taesik Lee, Seungjae Park, Kiwoong Kwon, Sunbeom Jeong, Nathan Jo, Han-Shin Yook, Jong-Gwan |
| contents | Distributed resource allocation algorithms differ from centralized methods by relying on locally collected information for resource selection, leading to a low vehicle-to-everything (V2X) communication quality of service (QoS) in high-traffic congestion. To overcome these challenges, this study proposes a proactive received signal strength indicator (RSSI)-based collision avoidance resource allocation (PR-CARA) algorithm. This algorithm features an extended 1-stage SCI system, which is a critical component that enables resource monitoring of adjacent vehicle user equipment (VUE). Monitored resources were then processed through a deep learning-based proactive RSSI estimator. The estimated proactive RSSI helps avoid resource selection, which leads to packet collisions, thereby significantly reducing the occurrence of this issue during resource allocation. The proposed algorithm is tested in a cooperative adaptive cruise control (CACC)-based platoon driving scenario that requires ultra-reliable and low-latency communication (URLLC) performance. Simulation results demonstrate that the proposed deep-learning-based proactive resource allocation algorithm, with the extended 1-stage SCI system, reduces packet collisions and improves the transmission signal-to-interference-plus-noise ratio (SINR), thereby significantly enhancing communication reliability compared to the benchmark resource allocation algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PR-CARA: Proactive V2X Resource Allocation with Extended 1-Stage SCI and Deep Learning-based Sensing Matrix Estimator Nam, Taesik Lee, Seungjae Park, Kiwoong Kwon, Sunbeom Jeong, Nathan Jo, Han-Shin Yook, Jong-Gwan Systems and Control Distributed resource allocation algorithms differ from centralized methods by relying on locally collected information for resource selection, leading to a low vehicle-to-everything (V2X) communication quality of service (QoS) in high-traffic congestion. To overcome these challenges, this study proposes a proactive received signal strength indicator (RSSI)-based collision avoidance resource allocation (PR-CARA) algorithm. This algorithm features an extended 1-stage SCI system, which is a critical component that enables resource monitoring of adjacent vehicle user equipment (VUE). Monitored resources were then processed through a deep learning-based proactive RSSI estimator. The estimated proactive RSSI helps avoid resource selection, which leads to packet collisions, thereby significantly reducing the occurrence of this issue during resource allocation. The proposed algorithm is tested in a cooperative adaptive cruise control (CACC)-based platoon driving scenario that requires ultra-reliable and low-latency communication (URLLC) performance. Simulation results demonstrate that the proposed deep-learning-based proactive resource allocation algorithm, with the extended 1-stage SCI system, reduces packet collisions and improves the transmission signal-to-interference-plus-noise ratio (SINR), thereby significantly enhancing communication reliability compared to the benchmark resource allocation algorithm. |
| title | PR-CARA: Proactive V2X Resource Allocation with Extended 1-Stage SCI and Deep Learning-based Sensing Matrix Estimator |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.13767 |