Search

Non-terrestrial Network (NTN), utilizing high-altitude platforms (HAP)-based free-space optical (FSO) backhaul and unmanned aerial vehicles (UAV) for last-mile access, is a feasible and promising architecture to achieve high data rate and seamless network coverage in the future 6G era. Effective resource allocation emerges as a pivotal concern for such networks. This paper addresses the data allocation issue for FSO backhaul from the HAP to multiple UAV-mounted base stations (BSs) under the constraints of ground users' requested data rates. We introduce frame allocation schemes (FAS), including rate adaptation with constraints (RAC)- and rate/power adaptation (RPA)-aided FAS. The key idea of these schemes is to allocate data frames effectively based on UAV's turbulence channel conditions, which aims to (i) guarantee the quality of services (QoS), (ii) retain both latency and throughput fairness, and (iii) minimize the transmitted power. Furthermore, the performance of these schemes is also analyzed under the impact of imperfect channel state information (CSI). We newly derive the channel probability density function (PDF) and the cumulative density function (CDF), considering the imperfect CSI due to channel estimation and quantization errors. Capitalizing on the derived PDF and CDF, different performance metrics are analytically obtained, incorporating combined effects of cloud coverage, transceiver misalignment, Fisher-Snedecor $\mathcal {F}$ turbulence, and angle-of-arrival (AoA) fluctuations. Numerical results demonstrate the effectiveness of our design proposals over the state-of-the-art. Finally, Monte Carlo simulations are employed to validate the analysis.

Free-space optics (FSO)-based low-Earth orbit (LEO) satellite systems have recently aroused considerable attention as an enabling technology for the Internet of Vehicles (IoVs) applications. However, such systems face critical challenges, including cloud coverage, atmospheric turbulence, and pointing errors. This article addresses a link-layer error-control design solution, which is a combination of the low-density parity check (LDPC) code and the incremental redundancy (IR)-hybrid automatic repeat request (HARQ) protocol. We consider a subclass of rate-compatible (RC)-LDPC code extension, i.e., protograph-based raptorlike (PBRL)-LDPC codes, for the proposed IR-HARQ design. In addition, we provide a comprehensive analytical framework to obtain performance metrics, including goodput, energy efficiency, and average frame delay. Numerical results highlight the outperformance of the design proposal compared to conventional link-layer solutions in FSO-based LEO satellite systems. Also, we provide a design guideline regarding the proper selection of transmitted power and decoding complexity concerns. Furthermore, we investigate the feasibility of our design proposal for a study case involving the existing Japan LEO satellite networks and the moving vehicles. Finally, we conduct the Monte-Carlo simulations to validate the correctness of theoretical results.

Free-space optical (FSO)-based satellite communications, due to extremely high data rates and global coverage capability, have recently drawn substantial research attention. The adverse issues on FSO-based satellite links, including atmospheric turbulence and pointing error, pose various challenges in designing and deploying such systems. Nevertheless, recent efforts in error control design focusing on adaptation-based mitigation techniques face practical restrictions due to the outdated feedback channel state information (CSI) caused by long-distance/high-latency satellite links. This article addresses the design of an adaptive rate/power control scheme using machine learning (ML)-aided channel prediction for FSO-based satellite systems. Notably, we employ the echo state network (ESN) model, an efficient form of recurrent neural network, for channel prediction. The design proposal facilitates the concurrent control of data rate and satellite's transmitted power for each equal-duration channel state, leveraging accurately predicted CSI. The average required transmitted power and energy efficiency performance metrics are analytically derived. Numerical results demonstrate the severe impact of outdated CSI on the performance of FSO-based satellite systems and highlight the necessity of our design proposal. Moreover, we confirm the effectiveness of the ESN model for FSO channel prediction by comparing its performance with other ML approaches in terms of predicted accuracy and complexity.

Vertical networks, including low Earth orbit (LEO) satellites and high-altitude platforms (HAP), are promising network paradigms to support diverse vehicular services in Internet of Vehicles (IoV). In addition, free-space optical (FSO) and subterahertz (sub-THz) communications are expected to play a vital role in 6G wireless networks due to the vast available bandwidth. This article addresses the performance of vertical networks, where the hybrid FSO/sub-THz link is deployed for the last-mile access of IoV. Specifically, both soft-switching and hard-switching schemes of hybrid FSO/sub-THz links are considered. The hybrid FSO/sub-THz channel is characterized by Doppler effect, atmospheric attenuation, fading, pointing misalignment, and weather conditions. A comprehensive analytical framework based on the cross-layer approach is then developed. This allows for assessing not only the physical-layer performance but also the upper-layer performance of transmission control protocol (TCP) under the impact of transmission errors at the last-mile access of hybrid FSO/sub-THz link. Numerical results quantitatively confirm the effectiveness of our proposal and illustrate the performance of hybrid FSO/sub-THz-based vertical networks in terms of outage probability, average transmission rate, average bit error rate, TCP throughput, and joint throughput-energy over various channel conditions. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.

Satellite–aerial–ground integrated network (SAGIN) has been widely envisioned as a promising network architecture for 6G. In the SAGINs, high-altitude platform (HAP)-aided relaying satellite systems using hybrid free-space optics (FSO)/radio-frequency (RF) communications have recently attracted research efforts worldwide. Nevertheless, the main drawback of hybrid FSO/RF systems is the restricted bandwidth of the RF connection, especially when the FSO one is blocked by cloud coverage. This article explores a novel solution for the hybrid FSO/RF HAP-based SAGIN under the impact of weather and atmospheric conditions. Specifically, an additional unmanned aerial vehicle (UAV) is deployed to diverse the FSO link from the HAP-to-ground station to avoid cloud blockage while maintaining a high-speed connection of the FSO link. A mirror array constructed by reconfigurable intelligent surface (RIS), an emerging technology, is mounted on the UAV to reflect the signals from the HAP. The channel model of RIS–UAV takes into account both atmospheric turbulence and hovering-induced pointing errors. Furthermore, we present a novel link switching design with a multirate adaptation scheme for the proposed network under different weather and turbulence conditions. Numerical results quantitatively confirm the effectiveness of our proposal. Additionally, we provide insightful discussions that can be helpful for the practical system design of RIS–UAV-assisted HAP-based SAGIN using hybrid FSO/RF links. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.