The paper extends the Cooperative Multi-Task Semantic Communication (CMT‑SemCom) framework to handle heterogeneous classification and regression tasks on the Cityscapes dataset. By incorporating the InfoMax principle, the system accommodates mixed discrete and continuous semantic variables, and it is benchmarked against single‑task training, task‑agnostic digital transmission, and single‑encoder multi‑decoder SemCom. Experiments show that CMT‑SemCom outperforms all baselines and provide insights into how the common unit capacity affects joint task performance.
The paper introduces HMS‑SCP, a hierarchical multi‑scale semantic‑aware cooperative perception framework for V2X communication. It uses a spatial importance predictor to select task‑relevant grid elements at multiple scales and maps them directly into complex‑valued symbols for joint source‑channel coding, achieving ultra‑low symbol rates and noise resilience. Experiments on OPV2V and DAIR‑V2X show that HMS‑SCP maintains high‑confidence far‑field detection with sub‑16 ms latency even under severe Rayleigh fading and extreme compression.
By Chun-Yeow Yeoh, Chee Keong Tan, Joanne Mun-Yee Lim, Heng-Siong Lim
arXiv:2502.08221v2 Announce Type: replace
Abstract: The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions ha...
By Xiang Chen, Shuying Gan, Chenyuan Feng, Xijun Wang, Tony Q. S. Quek
arXiv:2608. 14603v1 Announce Type: cross Abstract: Cooperative perception enables vehicles and infrastructure to exchange sensor data via Vehicle-to-Everything (V2X) communication, extending sensing coverage beyond occlusions and mitigating blind spots.
By Chun-Yeow Yeoh, Chee Keong Tan, Joanne Mun-Yee Lim, Heng-Siong Lim
arXiv:2601. 17216v3 Announce Type: replace-cross Abstract: Intelligent Transportation Systems (ITS) demand real-time collision prediction to ensure road safety and reduce accident severity.
By Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Aisha Syed, Matthew Andrews, Sean Kennedy
The paper introduces SemISAC, a unified framework that integrates semantic communication and semantic sensing into a single dual‑function waveform. It employs a joint semantic encoder to extract task‑specific information for both communication and sensing, and uses an adaptive pilot configuration to balance channel estimation and sensing needs. In vehicular scenarios, SemISAC achieves segmentation accuracy comparable to dedicated semantic communication systems while outperforming conventional and semantic baselines in target recognition and range estimation.
By Muhammad Abubakar Rashid, Muhammad Hannan Akram, Haejoon Jung, Syed Ali Hassan
arXiv:2603.19308v2 Announce Type: replace-cross
Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key...
By Wentao Wang, Haoran Xu, Guang Tan
arXiv:2505. 20853v3 Announce Type: replace-cross Abstract: Fusing heterogeneous information remains a persistent challenge in modern data analysis.
By Shuo Wang, Shunyang Huang, Jinghui Yuan, Zhixiang Shen, Zhao Kang
arXiv:2607. 00995v1 Announce Type: cross Abstract: Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome.
By Huichao Li, Tong Wang, Sanguo Zhang, Shuangge Ma
arXiv:2607. 21426v1 Announce Type: new Abstract: Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations.
By Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy
Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks.
arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
By Juntong Peng, Juanwu Lu, Yupeng Zhou, Can Cui, Yaobin Chen, Ziran Wang