Considering the increase of communication requirements from marine users, more studies try to introduce terrestrial wireless communication techniques into maritime communications to improve communication qualities. However, different communication scenarios introduce challenges for the application of terrestrial techniques to maritime networks. Furthermore, the communication coverage requirement is much larger than that of terrestrial networks, while the quality of service (QoS) requirement of marine users is expected to be like that on land, which makes network design more complicated. To address these problems, a user-centric communication architecture based on distributed antennas is proposed for maritime communications. The scenario of coastal networks based on cellular techniques is modeled mathematically, based on which the performance of such a network is analyzed and closed-form expressions of network performance are presented. To guarantee the QoS requirements of users, an antenna selection scheme is proposed, which can form a virtual service cloud for a targeted user. In addition, the precondition of the antenna selection scheme is discussed. Simulation results verify the analyses and indicate that the proposed scheme can guarantee the QoS of marine users under different cases.

Considering that future cellular networks are more content-centric, and content sharing among user equipments (UEs) is more universal, device-to-device (D2D) communication techniques become attractive for their applications to content delivery due to the characteristic of proximal transmissions without needs of high-rate backhaul. However, D2D content delivery may not be energy efficient due to shortage of useful contents for neighbor UEs when content caching and delivery are not well designed. In this paper, we propose an architecture using D2D multicast for content delivery in cellular networks and a practical methodology, which can indicate the time for delivery, the content to be disseminated, and the UEs to receive the delivery, to achieve more energy efficient content delivery for future green cellular networks.

Considering that the future cellular networks are more content-centric and content sharing among user equipments is more universal, device-to-device (D2D) technique attracts attention on supplying a feasible way for content delivery among proximal users without deploying additional infrastructure. Whereas some problems are still not clear such as the limitation on D2D communication for content delivery and which transmission mode (unicast or multicast) is more efficient. In this paper, the performance of content delivery is analyzed for D2D unicast and multicast, respectively. Several metrics such as the number of caching users, the number of request users, and the serving time of a content request are quantified with the consideration of user deployment and interference environment. These analyses provide an insight into D2D content delivery and deep understanding of D2D unicast and multicast on content delivery. To further distinguish appropriate conditions of employing unicast and multicast, energy efficiency is also investigated for two transmission modes. Extensive evaluations show that a better transmission mode can be selected based on the work presented in this paper. It is also verified that the efficiency of content delivery in cellular networks can be improved by this mode selection.

Stimulated by the increase in user demands and the development of intelligent driving, the automotive industry is pursuing high-bandwidth techniques, low-cost network deployment and deterministic data transmission. Time-sensitive networking (TSN) based on Ethernet provides a possible solution to these targets, which is arousing extensive attention from both academia and industry. We review TSN-related academic research papers published by major academic publishers and analyze research trends in TSN. This paper provides an up-to-date comprehensive survey of TSN-related standards, from the perspective of the physical layer, data link layer, network layer and protocol test. Then we classify intelligent driving products with TSN characteristics. With the consideration of more of the latest specified TSN protocols, we further analyze the minimum complete set of specifications and give the corresponding demo setup for the realization of TSN on automobiles. Open issues to be solved and trends of TSN are identified and analyzed, followed by possible solutions. Therefore, this paper can be an investigating basis and reference of TSN, especially for the TSN on automotive applications.

Device-to-device (D2D) technique provides a feasible way for content delivery among proximal users without deploying additional infrastructures. For the realization of D2D content delivery, it is important to guarantee quality-of-service (QoS) for supporting real-time content transmissions. In this paper, we formulate the content delivery problem based on a flow model and investigate mechanisms providing delay QoS guarantee. The delay performance is analyzed for the content dissemination with the aid of D2D communication. Based on the analyses of parameters affecting the performance of content delivery, an optimal resource allocation scheme is proposed to achieve the best energy efficiency of content delivery in cellular networks with guaranteed delay constraints for hybrid content requests. Then effects of content caching on D2D content delivery is studied and a caching management scheme is proposed to avoid cache overflow of users with a certain probability and satisfy QoS requirements of content delivery. Simulation results are presented that verify the analyzed influencing factors on D2D content delivery and the proposed QoS-provisioning schemes.

Maritime object detection is essential for resource monitoring, maritime defense, and public safety, yet detecting diverse targets beyond ships remains challenging. This paper presents YOLO-SEA, an efficient detection framework based on the enhanced YOLOv8 architecture. The model incorporates the SESA (SimAM-Enhanced SENetV2 Attention) module, which integrates the channel-adaptive weight adjustment of SENetV2 with the parameter-free spatial-channel modeling of SimAM to enhance feature representation. An improved BiFPN (Bidirectional Feature Pyramid Network) structure enhances multi-scale fusion, particularly for small object detection. In the post-processing stage, Soft-NMS (Soft Non-Maximum Suppression) replaces traditional NMS to reduce false suppression in dense scenes. YOLO-SEA detects eight maritime object types. Experiments show it achieves a 5.8% improvement in mAP@0.5 and 7.2% improvement in mAP@0.5:0.95 over the baseline, demonstrating enhanced accuracy and robustness in complex marine environments.