Off-Network Communications For Future Railway Mobile Communication Systems: Challenges and Opportunities

GSM-R is predicted to be obsoleted by 2030, and a suitable successor is needed. Defined by the International Union of Railways (UIC), the Future Railway Mobile Communication System (FRMCS) contains many future use cases with strict requirements. These use cases should ensure regular communication not only in network coverage but also uncovered scenarios. There is still a lack of standards on off-network communication in FRMCS, so this article focuses on off-network communication and intends to provide reference and direction for standardization. We first provide a comprehensive summary and analysis of off-network use cases in FRMCS. Then we give an overview of existing technologies (GSM-R, TETRA, DMR, LTE-V2X, and NR-V2X) that may support off-network communication. In addition, we simulate and evaluate the performance of existing technologies. Simulation results show that it is possible to satisfy the off-network communication requirements in FRMCS with enhancements based on LTE-V2X or NR-V2X. Finally, we give some future research directions to provide insights for industry and academia.

CODA: Counting Objects via Scale-aware Adversarial Density Adaption

Recent advances in crowd counting have achieved promising results with increasingly complex convolutional neural network designs. However, due to the unpredictable domain shift, generalizing trained model to unseen scenarios is often suboptimal. Inspired by the observation that density maps of different scenarios share similar local structures, we propose a novel adversarial learning approach in this paper, i.e., CODA (\emph{Counting Objects via scale-aware adversarial Density Adaption}). To deal with different object scales and density distributions, we perform adversarial training with pyramid patches of multi-scales from both source- and target-domain. Along with a ranking constraint across levels of the pyramid input, consistent object counts can be produced for different scales. Extensive experiments demonstrate that our network produces much better results on unseen datasets compared with existing counting adaption models. Notably, the performance of our CODA is comparable with the state-of-the-art fully-supervised models that are trained on the target dataset. Further analysis indicates that our density adaption framework can effortlessly extend to scenarios with different objects. \emph{The code is available at https://github.com/Willy0919/CODA.}