Low-altitude wireless networks (LAWNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling flexible, on-demand, and infrastructure-light connectivity for communication, sensing, and security-critical applications. However, the highly dynamic propagation conditions, frequent blockages, and evolving security threats inherent to LAWNs pose fundamental challenges to conventional static network architectures. In this article, we investigate flying reconfigurable intelligent surfaces (FRIS) as a transformative enabler for cell-free integrated sensing and communication (CF-ISAC) in LAWNs. By jointly exploiting aerial mobility and programmable electromagnetic reflections, FRIS introduces a new degree of freedom for three-dimensional and environment-aware control of wireless propagation. We present a comprehensive overview of enabling technologies, highlight physical layer security benefits, and provide a representative case study demonstrating the performance gains and sensing-communication trade-offs of FRIS-assisted CF-ISAC systems. Finally, we discuss key research challenges and future directions toward practical and secure 6G low-altitude deployments.
Shanza Shakoor, Quang Nhat Le, Minh-Hien T. Nguyen et al.· IEEE Communications Magazine· 0 citations
Vehicular crowdsensing (VCS) is a paradigm that exploits vehicle mobility, on-board sensing capabilities, and drivers' smartphone sensors to collect large-scale, distributed information to provide intelligent, location-based services. Satellite-assisted VCS architecture can complement terrestrial networks by enabling wide-area and infrastructure-independent data collection. Incentivizing vehicles to participate in satellite-assisted VCS campaign remains a major challenge due to associated sensing and communication costs, requiring each vehicle to optimize their sensing level to maximize their received reward. Moreover, unlike conventional assumptions where all vehicles participate simultaneously, practical VCS scenarios are asynchronous, as vehicle may start and complete sensing tasks at different times. To capture this realistic setting, we propose an asynchronous multi-agent proximal policy optimization (A-MAPPO) algorithm within a centralized training and decentralized execution (CTDE) framework to optimize the sensing strategies of individual vehicles in a satellite-assisted VCS setting. A dynamic social network effect among vehicles is also incorporated to encourage vehicle participation driven by social benefits. Extensive numerical experiments are conducted to evaluate the performance of the proposed approach, demonstrating that A-MAPPO achieves superior performance compared with MASAC, DQN, Greedy-Q, and Random baselines.
Arbil Chakma, Jingrong Wang, Quang Nhat Le et al.· IEEE Transactions on Network...· 1 citation
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
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