Digital Twin for Non-Terrestrial Networks: Vision, Challenges, and Enabling Technologies

The ongoing digital transformation has sparked the emergence of various new network applications that demand cutting-edge technologies to enhance their efficiency and functionality. One of the promising technologies in this direction is the digital twin, which is a new approach to design and manage complicated cyber-physical systems with a high degree of automation, intelligence, and resilience. This article discusses the use of digital twin technology as a new approach for modeling non-terrestrial networks (NTNs). Digital twin technology can create accurate data-driven NTN models that operate in real-time, allowing for rapid testing and deployment of new NTN technologies and services, besides facilitating innovation and cost reduction. Specifically, we provide a vision on integrating the digital twin into NTNs and explore the primary deployment challenges, as well as the key potential enabling technologies within NTN realm. In closing, we present a case study that employs a data-driven digital twin model for dynamic and service-oriented network slicing within an open radio access network (O-RAN) NTN architecture.

On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized, (2) passed through a high-performance cloud filtering via the Cloud-Net model, and (3) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.

Energy-Efficient Precoding and Feeder-Link-Beam Matching Design for Bent-Pipe SATCOM Systems

This paper proposes a joint optimization framework for energy-efficient precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jointly optimizes the precoding vectors at the gateways and amplifying-and-matching mechanism at the satellite to maximize the system weighted energy efficiency under the transmit power budget constraint. The technical designs are formulated into a non-convex sparsity problem consisting of a fractional-form objective function and sparsity-related constraints. To address these challenges, two iterative efficient designs are proposed by utilizing the concepts of Dinkelbach's method and the compress-sensing approach. The simulation results demonstrate the effectiveness of the proposed scheme compared to another benchmark method.

Integrated Access and Backhaul via Satellites

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

Two-tier User Association and Resource Allocation Design for Integrated Satellite-Terrestrial Networks

This paper presents a study of an integrated satellite-terrestrial network, where Low-Earth-Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and the core network. The mobility of LEO satellites raises the challenge of determining the optimal association between LEO satellites, BSs, and users (UEs). The goal is to satisfy the UE demand while ensuring load balance and optimizing the capacity of the serving link between the BS and the LEO satellite. To tackle this complex optimization problem, which involves mixed-integer non-convex programming, we propose an iterative algorithm that leverages approximation and relaxation methods. The proposed solution aims to find the optimal two-tier satellite-BS-UE association, sub-channel assignment, power and bandwidth allocation in the shortest possible time, fulfilling the requirements of the integrated satellite-terrestrial network.

Large-Scale Beam Placement and Resource Allocation Design for MEO-Constellation SATCOM

This paper presents a centralized framework for optimizing the joint design of beam placement, power, and bandwidth allocation in an MEO satellite constellation to fulfill the heterogeneous traffic demands of a large number of global users. The problem is formulated as a mixed integer programming problem, which is computationally complex in large-scale systems. To overcome this challenge, a three-stage solution approach is proposed, including user clustering, cluster-based bandwidth and power estimation, and MEO-cluster matching. A greedy algorithm is also included as a benchmark for comparison. The results demonstrate the superiority of the proposed algorithm over the benchmark in terms of satisfying user demands and reducing power consumption.

Energy-Efficient RIS-Enabled NOMA Communication for 6G LEO Satellite Networks

This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.

LEO-to-User Assignment and Resource Allocation for Uplink Transmit Power Minimization

This paper aims to develop satellite-user association and resource allocation mechanisms to minimize the total transmit power for integrated terrestrial and non-terrestrial networks wherein a constellation of LEO satellites provides the radio access services to both terrestrial base stations (BSs) and the satellite-enabled users (SUEs). In this work, beside maintaining the traditional SatCom connection for SUEs, the LEO satellites provide backhaul links to the BSs to upload the data received from their ground customers. Taking the individual SUE traffic demands and the aggregated BS demands, we formulate a mixed integer programming which consists of the binary variables due to satellite association selection, power control and bandwidth allocation related variables. To cope with this challenging problem, an iterative optimization-based algorithm is proposed by relaxing the binary components and alternating updating all variables. A greedy mechanism is also presented for comparison purpose. Then, numerical results are presented to confirm the effectiveness of our proposed algorithms.

Space-Terrestrial Cooperation Over Spatially Correlated Channels Relying on Imperfect Channel Estimates: Uplink Performance Analysis and Optimization

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network's spectral efficiency. We first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput when maximum ratio combining is utilized for detecting the desired signals. As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed to allow us to address the congestion issues appearing when at least one user is served at a rate below the target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

Intelligent and Secure Radio Environments for 6G Vehicular Aided HetNets: Key Opportunities and Challenges

Reconfigurable meta-surfaces are emerging as a novel and revolutionizing technology to enable intelligent wireless environments. Due to the low cost, improved efficiency, and passive nature of reflecting elements, it is becoming possible to program and control the wireless environment. Since wireless physical layer technologies can generally adapt to the wireless environment, their combination with reconfigurable surfaces and deep learning approaches can open new avenues for achieving secure 6G vehicular aided heterogeneous networks (HetNets). Motivated by these appealing advantages, this work provides an intelligent and secure radio environment (ISRE) paradigm for 6G vehicular aided HetNets. We present an overview of enabling technologies for ISRE-based 6G vehicular aided HetNets. We discuss features, design goals, and applications of such networks. Next, we outline new opportunities provided by ISRE-based 6G vehicular HetNets and we present a case study using the contextual bandit approach in terms of best IRS for secure communications. Finally, we discuss some future research opportunities.