The integration of non-terrestrial networks (NTNs) in 6G enables seamless connectivity and unleashes the potential of stringent Internet of Things (IoT) services worldwide. Among these, safety-critical IoT aims to provide ubiquitous service along with comprehensive protection against diversified over-the-air threats, which requires high-security technology under the unique constraints of NTNs. As a promising secure transmission technology rooted in information theory, wireless covert communication can conceal the presence of transmission so as to eliminate possible attack threats. In this article, we point out that the continuous evolution of covert communication paves a comprehensive avenue to tackle the complicated security challenges of safety-critical IoT in 6G NTN. We elucidate several typical security threats that may be encountered in NTN IoT and analyze how covert communication can resist these security vulnerabilities through its endogenous potential. Further, we propose an artificial intelligence-based three-participant collaborative-adversarial network to strike a balance between communication efficiency and covertness performance. Finally, some future research directions and challenges of covert communication are discussed.

Artificial intelligence (AI) has become a promising solution for meeting the stringent performance requirements on wireless physical layer in sixth-generation (6G) communication systems, due to its strong ability to learn complex model, achieve end-to-end optimization and adapt to dynamic environments. This article provides a comprehensive review with respect to artificial intelligence for wireless physical-layer technologies (AI4PHY). Specifically, we first analyze the characteristics of the classic AI techniques and their potential applications for physical-layer technologies. Then we study the AI-enhanced designs from the point of view of the basic physical-layer modules, including coding, modulation, multiple access, multiple-input-multiple-output (MIMO), channel estimation, as well as relay transmission. The standardization progress of AI4PHY in 3GPP is also discussed. Based on the current AI4PHY researches, we propose some potential future research directions to inspire and encourage the further exploration.

Assessing the vulnerability of mega-constellation networks (MCNs) to large-scale failure is challenging, due to the time-varying three-dimensional constellation. In this paper, we propose a geometrical method to assess the vulnerability of the MCNs under large-scale geographical failure based on the standard +Grid inter-satellite connectivity pattern, using hop count as the metric. We first equivalently map the original constellation to a two-dimensional flat torus, offering a simplified and time-invariant representation of the geographical failure. The failure’s properties are then revealed on the torus to identify the blockage on the inter-satellite paths that might increase the hop count between end users. Under the blockage, a closed-form hop count expression is then obtained by deriving the explicit expressions of the optimal detours in the scaling limit. Finally, we propose a low-complexity hop count estimation algorithm that achieves a maximum relative error of 1.5% compared with the network simulation while being $10^{5}$ times faster. Evaluations using traffic sourced from the 100 most populous cities show that the geographical failure covering the latitude corresponding to the MCNs’ inclination has the greatest impact on the hop count, whereas the MCNs are generally robust even under extreme scenarios.

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

With the ever-increasing densification of low Earth orbit constellations, lens antenna array has become an attractive option for ground terminals to simultaneously connect to multiple satellites with improved coverage under insufficient energy supply. To this end, an optimization problem is formulated for maximizing the energy efficiency (EE) of the system by respecting the uplink transmit power budget and the quality-of-service (QoS) constraints. This problem has a discrete non-concave objective function and is solved with reduced computational complexity using zero forcing-based precoding and problem decomposition. The proposed algorithm iterates between the following three steps. In the first step, zero forcing-based fully-digital precoder is first optimized under a fixed number of RF chains by adopting Dinkelbach’s algorithm. Then, a hybrid analog-digital precoding based on analog beam selection and baseband digital precoding is designed to approximate the optimal fully-digital precoder under Euclidean distance criteria. On this basis, simulated annealing algorithm (SAA) is applied to further optimize the number of required RF chains with the aim of reducing total energy cost. Simulation results demonstrate that the proposed design achieves an improved performance in EE comparing with existing schemes with fixed number of RF chains by up to 15%.

Reducing data download time helps in improving the time efficiency of unmanned aerial vehicle (UAV) for emergency services. While download time can be shortened either by increasing the data download rate, or by reducing the data size via on-board computation, the optimal trade-off between the two parts is still an open question. In this letter, the joint optimization of on-board computation and communication is studied to minimize the download time under realistic energy constraint. To decouple computation from communication, we first derive the exact expressions for the optimal computation frequency and transmit power based on a given computation strategy. As the resulting univariate optimization problem is an implicit one, we develop a Dinkelbach-like algorithm that iterates between solving a simplified explicit parametric problem to find a candidate strategy, and updating the parameter to approach the optima. The proposed scheme reduces at least 33.3% download time compared with conventional strategies, and the advantage becomes more significant under stringent energy constraint.