Reliable communication over oceans and in remote areas remains challenging because most wireless networks depend on land-based infrastructure. Space–air–ground–sea integrated networks (SAGSINs) provide a promising solution by integrating space, aerial, terrestrial, and maritime network components to extend coverage beyond the reach of land-based networks. In this paper, we consider a maritime relay-assisted SAGSIN where a sea-surface station (SS) communicates with a base station (BS) through one relay selected from three candidate platforms: an onshore station, a high-altitude platform, and a satellite (SAT). Since these relay links operate in different propagation environments and network segments, accurate channel estimation and relay selection become challenging. The proposed framework considers least squares (LS), linear minimum mean square error (LMMSE), and an adapted denoising convolutional neural network (DnCNN)-based estimator for channel estimation over heterogeneous maritime relay links. The DnCNN-based estimator learns the nonlinear mapping between the initial channel estimate and the corresponding refined channel estimate, thereby reducing estimation errors caused by noise and limited pilot observations. The refined channel estimates are then used to support relay selection, so that the SS can choose a suitable relay for forwarding its data to the BS. The simulation results confirm that the adapted DnCNN-based estimator generally provides lower normalized mean square error than the traditional LS and LMMSE estimators, especially at low transmit power and short pilot length. The results further show that the proposed relay selection method achieves an end-to-end data rate close to the perfect channel state information benchmark. These results confirm that accurate channel estimation improves relay selection and enhances maritime communication performance.
Waruni U Bandara, Omar Maraqa, Ahmed A. Al-habob et al.· IEEE Open Journal of the Com...· 0 citations
Ensuring reliable and low-latency vehicle-to-everything (V2X) communications in high-speed transport settings remains a significant challenge due to severe path loss brought about by non-line-of-sight (NLoS) and coverage gaps in conventional cellular infrastructure. While dielectric waveguide-based pinching antenna (PA) systems have been proposed to mitigate these physical limitations, they suffer from substantial in-waveguide attenuation over long distances. To address these challenges, we propose a segmented waveguide-enabled pinching-antenna (SWAN) architecture in platoon-based V2X networks. By employing dynamic segment selection, SWAN maintains robust line-of-sight (LoS) connectivity while mitigating the in-waveguide attenuation inherent in conventional PA structures. We formulate a joint resource allocation (RA) and mode selection problem to minimise the age of information (AoI) for both uplink platoon monitoring and downlink traffic broadcasting, whilst ensuring the exchange of intra-platoon cooperative awareness messages (CAMs) and minimising power consumption. To solve this high-dimensional problem, we propose a decomposed multi-agent deep deterministic policy gradient (DE-MADDPG) algorithm augmented with twin delayed (TD3) critics by considering each vehicle platoon (VP) as an agent. This approach decouples system-wide coordination from local executions of VPs, enabling efficient learning in dynamic environments. Extensive simulations demonstrate that the proposed framework significantly outperforms standard reinforcement learning (RL) baseline methods, achieving near-optimal uplink and downlink AoI performance, with an average gap of 4.8% to exhaustive search, and near-perfect CAM delivery probability (CDP), which approaches 100%, even under dense traffic conditions.
Yuxiang Zheng, Simon L. Cotton, T. Q. Duong· IEEE Transactions on Cogniti...· 0 citations