RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912583614726144 |
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| author | Wang, Chunjie Zhang, Xuhui Liu, Wenchao Ren, Jinke Wang, Shuqiang Shen, Yanyan Ye, Kejiang Tsang, Kim Fung |
| author_facet | Wang, Chunjie Zhang, Xuhui Liu, Wenchao Ren, Jinke Wang, Shuqiang Shen, Yanyan Ye, Kejiang Tsang, Kim Fung |
| contents | This paper investigates the data collection enhancement problem in a reconfigurable intelligent surface (RIS)-empowered intelligent consumer transportation system (ICTS). We propose a novel framework where a data center (DC) provides energy to pre-configured roadside unit (RSU) pairs during the downlink stage. While in the uplink stage, these RSU pairs utilize a hybrid rate-splitting multiple access (RSMA) and time-division multiple access (TDMA) protocol to transmit the processed data to the DC, while simultaneously performing local data processing using the harvested energy. Our objective is to maximize the minimal processed data volume of the RSU pairs by jointly optimizing the RIS downlink and uplink phase shifts, the transmit power of the DC and RSUs, the RSU computation resource allocation, and the time slot allocation. To address the formulated non-convex problem, we develop an efficient iterative algorithm integrating alternating optimization and sequential rank-one constraint relaxation methods. Extensive simulations demonstrate that the proposed algorithm significantly outperforms baseline schemes under diverse scenarios, validating its effectiveness in enhancing the data processing performance for intelligent transportation applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems Wang, Chunjie Zhang, Xuhui Liu, Wenchao Ren, Jinke Wang, Shuqiang Shen, Yanyan Ye, Kejiang Tsang, Kim Fung Information Theory Signal Processing This paper investigates the data collection enhancement problem in a reconfigurable intelligent surface (RIS)-empowered intelligent consumer transportation system (ICTS). We propose a novel framework where a data center (DC) provides energy to pre-configured roadside unit (RSU) pairs during the downlink stage. While in the uplink stage, these RSU pairs utilize a hybrid rate-splitting multiple access (RSMA) and time-division multiple access (TDMA) protocol to transmit the processed data to the DC, while simultaneously performing local data processing using the harvested energy. Our objective is to maximize the minimal processed data volume of the RSU pairs by jointly optimizing the RIS downlink and uplink phase shifts, the transmit power of the DC and RSUs, the RSU computation resource allocation, and the time slot allocation. To address the formulated non-convex problem, we develop an efficient iterative algorithm integrating alternating optimization and sequential rank-one constraint relaxation methods. Extensive simulations demonstrate that the proposed algorithm significantly outperforms baseline schemes under diverse scenarios, validating its effectiveness in enhancing the data processing performance for intelligent transportation applications. |
| title | RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2509.09644 |