Distributed satellite information networks: Architecture, enabling technologies, and trends

Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, grant-free massive access, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach

This paper presents a novel auto-encoder based end-to-end channel encoding and decoding. It integrates deep reinforcement learning (DRL) and graph neural networks (GNN) in code design by modeling the generation of code parity-check matrices as a Markov Decision Process (MDP), to optimize key coding performance metrics such as error-rates and code algebraic properties. An edge-weighted GNN (EW-GNN) decoder is proposed, which operates on the Tanner graph with an iterative message-passing structure. Once trained on a single linear block code, the EW-GNN decoder can be directly used to decode other linear block codes of different code lengths and code rates. An iterative joint training of the DRL-based code designer and the EW-GNN decoder is performed to optimize the end-end encoding and decoding process. Simulation results show the proposed auto-encoder significantly surpasses several traditional coding schemes at short block lengths, including low-density parity-check (LDPC) codes with the belief propagation (BP) decoding and the maximum-likelihood decoding (MLD), and BCH with BP decoding, offering superior error-correction capabilities while maintaining low decoding complexity.

Wireless Human-Machine Collaboration in Industry 5.0

Wireless Human-Machine Collaboration (WHMC) represents a critical advancement for Industry 5.0, enabling seamless interaction between humans and machines across geographically distributed systems. As the WHMC systems become increasingly important for achieving complex collaborative control tasks, ensuring their stability is essential for practical deployment and long-term operation. Stability analysis certifies how the closed-loop system will behave under model randomness, which is essential for systems operating with wireless communications. However, the fundamental stability analysis of the WHMC systems remains an unexplored challenge due to the intricate interplay between the stochastic nature of wireless communications, dynamic human operations, and the inherent complexities of control system dynamics. This paper establishes a fundamental WHMC model incorporating dual wireless loops for machine and human control. Our framework accounts for practical factors such as short-packet transmissions, fading channels, and advanced HARQ schemes. We model human control lag as a Markov process, which is crucial for capturing the stochastic nature of human interactions. Building on this model, we propose a stochastic cycle-cost-based approach to derive a stability condition for the WHMC system, expressed in terms of wireless channel statistics, human dynamics, and control parameters. Our findings are validated through extensive numerical simulations and a proof-of-concept experiment, where we developed and tested a novel wireless collaborative cart-pole control system. The results confirm the effectiveness of our approach and provide a robust framework for future research on WHMC systems in more complex environments.

Communication-Control Codesign for Large-Scale Wireless Networked Control Systems

Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots. The interdependence between communication and control requires integrated design, but traditional methods treat them separately, leading to inefficiencies. Current codesign approaches often rely on simplified models, focusing on single-loop or independent multi-loop systems. However, large-scale WNCSs face unique challenges, including coupled control loops, time-correlated wireless channels, trade-offs between sensing and control transmissions, and significant computational complexity. To address these challenges, we propose a practical WNCS model that captures correlated dynamics among multiple control loops with spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading channels. We formulate the codesign problem as a sequential decision-making task that jointly optimizes scheduling and control inputs across estimation, control, and communication domains. To solve this problem, we develop a Deep Reinforcement Learning (DRL) algorithm that efficiently handles the hybrid action space, captures communication-control correlations, and ensures robust training despite sparse cross-domain variables and floating control inputs. Extensive simulations show that the proposed DRL approach outperforms benchmarks and solves the large-scale WNCS codesign problem, providing a scalable solution for industrial automation.

Secure Enhancement for RIS-Aided UAV with ISAC: Robust Design and Resource Allocation

This paper analyses the security performance of a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication system with integrated sensing and communications (ISAC). We consider a multiple-antenna UAV transmitting ISAC waveforms to simultaneously detect an untrusted target in the surrounding environment and communicate with a ground Internet-of-Things (IoT) device in the presence of an eavesdropper (Eve). Given that the Eve can conceal their channel state information (CSI) in practical scenarios, we assume that the CSI of the eavesdropper channel is imperfect. For this RIS-aided ISAC-UAV system, we aim to maximize the average communication secrecy rate by jointly optimizing UAV trajectory, RIS passive beamforming, transmit beamforming, and receive beamforming. However, this joint optimization problem is non-convex due to multi-variable coupling. As such, we solve the optimization using an efficient and tractable algorithm using a block coordinate descent (BCD) method. Specifically, we develop a successive convex approximation (SCA) algorithm based on semidefinite relaxation (SDR) to optimise the joint optimization as four separate non-convex subproblems. Numerical results show that our proposed algorithm can successfully ensure the accuracy of sensing targets and significantly improve the communication secrecy rate of the IoT communication devices.

Movable Antenna Enabled ISAC Beamforming Design for Low-Altitude Airborne Vehicles

In mobile systems with low-altitude vehicles, integrated sensing and communication (ISAC) is considered an effective approach to increase the transmission rate due to limited spectrum resources. To further improve the ISAC performance, this paper proposes a novel method called integrated sensing and communication-movable antenna (ISAC-MA) to optimize the antenna's position. Our goal is to support low-space vehicles by optimizing radar and communication joint beamforming and antenna position in the presence of clutter. This scheme not only guarantees the required signal-to-noise ratio (SNR) for sensing but also further improves the SNR for communication. A successive convex approximation (SCA)-based block coordinate descent (BCD) algorithm is proposed to maximize communication capacity under the condition of sensing SNR. Numerical results show that, compared with the traditional ISAC system and various benchmark schemes, the proposed ISAC-MA system can achieve higher communication capacity under the same sensing SNR constraints.

Improving Physical-Layer Security in ISAC-UAV System: Beamforming and Trajectory Optimization

This paper investigates a novel unmanned aerial vehicle (UAV) secure communication system with integrated sensing and communications. We consider wireless security enhancement for a multiple-antenna UAV transmitting ISAC waveforms to communicate with multiple ground Internet-of-Thing devices and detect the surrounding environment. Specifically, we aim to maximize the average communication secrecy rate by optimizing the UAV trajectory and beamforming vectors. Given that the UAV trajectory optimization problem is non-convex due to multi-variable coupling develop an efficient algorithm based on the successive convex approximation (SCA) algorithm. Numerical results show that our proposed algorithm can ensure the accuracy of sensing targets and improve the communication secrecy rate.

Floor-Plan-aided Indoor Localization: Zero-Shot Learning Framework, Data Sets, and Prototype

Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new communication environment. Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs. Based on the coarse locations, the floor-plan image between an MD and an AP is exploited to improve localization accuracy in a floor-plan-aided deep neural network. To further improve the generalization ability, we develop a synthetic data generator that provides synthetic data samples in different scenarios, where real-world samples are not available. We implement the framework in a prototype that estimates the locations of MDs. Experimental results show that our zero-shot learning method can reduce localization errors by around $30$\% to $55$\% compared with three baselines from the existing literature.

Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-assisted Networks with Rate-Splitting Multiple Access

Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional methods deploying passive RIS on UAVs, this study delves into the efficacy of an aerial active RIS (AARIS). Specifically, the downlink transmission of an AARIS network is investigated, where the base station (BS) leverages rate-splitting multiple access (RSMA) for effective interference management and benefits from the support of an AARIS for jointly amplifying and reflecting the BS's transmit signals. Considering both the non-trivial energy consumption of the active RIS and the limited energy storage of the UAV, we propose an innovative element selection strategy for optimizing the on/off status of RIS elements, which adaptively and remarkably manages the system's power consumption. To this end, a resource management problem is formulated, aiming to maximize the system energy efficiency (EE) by jointly optimizing the transmit beamforming at the BS, the element activation, the phase shift and the amplification factor at the RIS, the RSMA common data rate at users, as well as the UAV's trajectory. Due to the dynamicity nature of UAV and user mobility, a deep reinforcement learning (DRL) algorithm is designed for resource allocation, utilizing meta-learning to adaptively handle fast time-varying system dynamics. Simulations indicate that incorporating an active RIS at the UAV leads to substantial EE gain, compared to passive RIS-aided UAV. We observe the superiority of the RSMA-based AARIS system in terms of EE, compared to existing approaches adopting non-orthogonal multiple access (NOMA).

Graph Neural Network-Based Bandwidth Allocation for Secure Wireless Communications

This paper designs a graph neural network (GNN) to improve bandwidth allocations for multiple legitimate wireless users transmitting to a base station in the presence of an eavesdropper. To improve the privacy and prevent eavesdropping attacks, we propose a user scheduling algorithm to schedule users satisfying an instantaneous minimum secrecy rate constraint. Based on this, we optimize the bandwidth allocations with three algorithms namely iterative search (IvS), GNN-based supervised learning (GNN-SL), and GNN-based unsupervised learning (GNN-USL). We present a computational complexity analysis which shows that GNN-SL and GNN-USL can be more efficient compared to IvS which is limited by the bandwidth block size. Numerical simulation results highlight that our proposed GNN-based resource allocations can achieve a comparable sum secrecy rate compared to IvS with significantly lower computational complexity. Furthermore, we observe that the GNN approach is more robust to uncertainties in the eavesdropper's channel state information, especially compared with the best channel allocation scheme.