Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks
The evolution toward sixth-generation (6G) wireless communications introduces unprecedented demands for ultra-reliable low-latency communication (URLLC) in vehicle-to-everything (V2X) networks, where fast-moving vehicles and the use of high-frequency bands make it challenging to acquire the channel...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11030661/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850114129654710272 |
|---|---|
| author | Ghazi Gharsallah Georges Kaddoum |
| author_facet | Ghazi Gharsallah Georges Kaddoum |
| author_sort | Ghazi Gharsallah |
| collection | DOAJ |
| description | The evolution toward sixth-generation (6G) wireless communications introduces unprecedented demands for ultra-reliable low-latency communication (URLLC) in vehicle-to-everything (V2X) networks, where fast-moving vehicles and the use of high-frequency bands make it challenging to acquire the channel state information to maintain high-quality connectivity. Traditional methods for estimating channel coefficients rely on pilot symbols transmitted during each coherence interval; however, the combination of high mobility and high frequencies significantly reduces the coherence times, necessitating substantial bandwidth for pilot transmission. Consequently, these conventional approaches are becoming inadequate, potentially causing inefficient channel estimation and degraded throughput in such dynamic environments. This paper presents a novel multimodal collaborative perception framework for dynamic channel prediction in 6G V2X networks, integrating LiDAR data to enhance the accuracy and robustness of channel predictions. Our approach synergizes information from connected agents and infrastructure, enabling a more comprehensive understanding of the dynamic vehicular environment. A key innovation in our framework is the prediction horizon optimization (PHO) component, which dynamically adjusts the prediction interval based on real-time evaluations of channel conditions, ensuring that predictions remain relevant and accurate. Extensive simulations using the MVX (Multimodal V2X) high-fidelity co-simulation framework demonstrate the effectiveness of our solution. Compared to baseline methods—namely, a classical LS-LMMSE approach and a wireless-based model that solely relies on channel measurements—our framework achieves up to a 30.82% reduction in mean squared error (MSE) and a 32.76% increase in goodput. These gains underscore the efficiency of the PHO component in reducing prediction errors, maintaining low bit error rates, and meeting the stringent requirements of 6G V2X communications. Consequently, our framework establishes a new benchmark for AI-driven channel prediction in next-generation wireless networks, particularly in challenging urban and rural scenarios. |
| format | Article |
| id | doaj-art-72f1aef12d784349a18bbf039d55a2cb |
| institution | OA Journals |
| issn | 2831-316X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-72f1aef12d784349a18bbf039d55a2cb2025-08-20T02:36:59ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-01372574310.1109/TMLCN.2025.357857711030661Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X NetworksGhazi Gharsallah0https://orcid.org/0009-0001-0902-9673Georges Kaddoum1https://orcid.org/0000-0002-5025-6624Electrical Engineering Department, École de Technologie Supérieure, University of Quebec, Quebec, QC, CanadaElectrical Engineering Department, École de Technologie Supérieure, University of Quebec, Quebec, QC, CanadaThe evolution toward sixth-generation (6G) wireless communications introduces unprecedented demands for ultra-reliable low-latency communication (URLLC) in vehicle-to-everything (V2X) networks, where fast-moving vehicles and the use of high-frequency bands make it challenging to acquire the channel state information to maintain high-quality connectivity. Traditional methods for estimating channel coefficients rely on pilot symbols transmitted during each coherence interval; however, the combination of high mobility and high frequencies significantly reduces the coherence times, necessitating substantial bandwidth for pilot transmission. Consequently, these conventional approaches are becoming inadequate, potentially causing inefficient channel estimation and degraded throughput in such dynamic environments. This paper presents a novel multimodal collaborative perception framework for dynamic channel prediction in 6G V2X networks, integrating LiDAR data to enhance the accuracy and robustness of channel predictions. Our approach synergizes information from connected agents and infrastructure, enabling a more comprehensive understanding of the dynamic vehicular environment. A key innovation in our framework is the prediction horizon optimization (PHO) component, which dynamically adjusts the prediction interval based on real-time evaluations of channel conditions, ensuring that predictions remain relevant and accurate. Extensive simulations using the MVX (Multimodal V2X) high-fidelity co-simulation framework demonstrate the effectiveness of our solution. Compared to baseline methods—namely, a classical LS-LMMSE approach and a wireless-based model that solely relies on channel measurements—our framework achieves up to a 30.82% reduction in mean squared error (MSE) and a 32.76% increase in goodput. These gains underscore the efficiency of the PHO component in reducing prediction errors, maintaining low bit error rates, and meeting the stringent requirements of 6G V2X communications. Consequently, our framework establishes a new benchmark for AI-driven channel prediction in next-generation wireless networks, particularly in challenging urban and rural scenarios.https://ieeexplore.ieee.org/document/11030661/6GV2Xcollaborative perceptionchannel prediction |
| spellingShingle | Ghazi Gharsallah Georges Kaddoum Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks IEEE Transactions on Machine Learning in Communications and Networking 6G V2X collaborative perception channel prediction |
| title | Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks |
| title_full | Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks |
| title_fullStr | Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks |
| title_full_unstemmed | Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks |
| title_short | Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks |
| title_sort | multimodal collaborative perception for dynamic channel prediction in 6g v2x networks |
| topic | 6G V2X collaborative perception channel prediction |
| url | https://ieeexplore.ieee.org/document/11030661/ |
| work_keys_str_mv | AT ghazigharsallah multimodalcollaborativeperceptionfordynamicchannelpredictionin6gv2xnetworks AT georgeskaddoum multimodalcollaborativeperceptionfordynamicchannelpredictionin6gv2xnetworks |