A Low-latency Collaborative HARQ Scheme for Control/User-plane Decoupled Railway Wireless Networks

ArticleThe recently proposed Control/User (C/U) plane decoupled railway wireless network is a promising new network architecture to meet the communication demands of both train control systems and onboard passengers by completely separating the C-plane and U-plane into different network nodes operating at different frequency bands. Although the system capacity of this network architecture can be highly increased, the forwarding latency of X3 interfaces to link the C-plane and U-plane becomes a serious concern, especially for hybrid automatic repeat request (HARQ) protocols which demand frequent interactions between the C-plane and U-plane. This concern becomes more pronounced for latency sensitive train control. To address this challenging problem, in this paper we propose a low-latency collaborative HARQ scheme. Through a newly designed collaborative transmission framework, the possible spare resources on lower frequency bands of macro cells by excluding those used by C-plane transmissions are utilized to help small cells relay erroneously received data. Compared to the conventional HARQ scheme, to reach the same transmission reliability, the proposed scheme requires fewer retransmissions on average, thereby mitigating the latency problem caused by HARQ retransmissions. Correspondingly, the channel mapping is also redesigned to conform to the proposed collaborative transmission framework. In the theoretical analysis, the expression of the average retransmission times related to the sum of independent Gamma variables is developed. Finally, simulation results show that a great decrease in the retransmission latency is gained by the proposed scheme, but at the sacrifice of few average system transmission rate

Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving

Collaborative perception has recently gained significant attention in autonomous driving, improving perception quality by enabling the exchange of additional information among vehicles. However, deploying collaborative perception systems can lead to domain shifts due to diverse environmental conditions and data heterogeneity among connected and autonomous vehicles (CAVs). To address these challenges, we propose a unified domain generalization framework applicable in both training and inference stages of collaborative perception. In the training phase, we introduce an Amplitude Augmentation (AmpAug) method to augment low-frequency image variations, broadening the model's ability to learn across various domains. We also employ a meta-consistency training scheme to simulate domain shifts, optimizing the model with a carefully designed consistency loss to encourage domain-invariant representations. In the inference phase, we introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy among CAVs prior to inference. Comprehensive experiments substantiate the effectiveness of our method in comparison with the existing state-of-the-art works. Code will be released at https://github.com/DG-CAVs/DG-CoPerception.git