Integrated sensing and communication (ISAC) enables several emerging applications while suffering severe security issues. In this article, the novel illegal sensing suppression (ISS) is proposed to protect the privacy of the specific legitimate user in the presence of an adversary with the sensing capacity in the ISAC system. The target detection and the direction of arrival estimation as common sensing tasks are investigated to deteriorate the sensing performance at the adversary side. To this end, we propose the missed detection probability maximization and the Cramér-Rao bound (CRB) maximization-based ISS model. This model also ensures the achievable rate and the legitimate sensing CRB toward the adversary at the ISAC base station side. The beamforming optimization algorithms are developed based on the alternating optimization and semidefinite relaxation techniques in order to address the nonconvex issue in the formulated ISS models and explore the rate-ISS tradeoff in the ISAC system. We further propose the closed-form solution for the single user case in the target detection suppression scenario. Numerical results demonstrate the necessity of the ISS and the ISS gain of proposed algorithms compared with other baseline schemes.

Unmanned aerial vehicles (UAVs), also known as drones, have already been widely used in wireless networks. UAV-assisted integrated communication and sensing (ISAC) networks are feasible solutions to many challenging scenarios in which ground users (GUs) are inaccessible by terrestrial networks. However, the uncertainty of GU's locations undermines the performance of UAV-assisted networks, especially for UAV trajectory designs. To tackle this issue, we formulate this optimal UAV trajectory design problem to a catenary shape determination problem, which transforms the objective of maximizing the overall performance to that of minimizing the potential of the catenary. In the proposed scheme, an arbitrary partial distribution of GU's locations is represented by a matter with areal mass density in an artificial potential field (APF). To obtain an optimal solution, we derive a second-order mechanical equation representing the shape of this catenary, by analyzing its static equilibrium state when achieving minimal potential. Different from conventional path discretization methods, the obtained trajectory solution in this paper is a continuous-form second-order equation with remarkable path compression. The numerical results show that, when compared to conventional UAV trajectory optimization methods, the proposed approach can achieve an optimal solution of UAV trajectory with low computational complexity. It further demonstrates that the proposed approach can flexibly and continuously adjust the UAV trajectory under the scenario of uncertain GU's locations.

Low Earth Orbit Satellite Networks (LEO-SNs) have emerged as a promising paradigm for future space information networks. However, the time-varying topology, link intermittency, limited onboard resource and relatively long transmission distance imposed unprecedented challenges on guaranteeing the delay Quality of Service (QoS). In contrast with existing routing-based or resource optimization-based solutions, onboard processing provides an alternative way to reduce the transmission delay by dwindling the transmitted data size. The employment of onboard processing makes it critical to select a routing path with sufficient energy and properly allocate resources for transmission and processing. This paper studies the untouched onboard processing aided transmission delay minimization problem of LEO-SNs. A Distributed Network State Learning (DNSL) mechanism is proposed for synchronizing the network states, which induces Potential Field (PF) to model both the attraction of resources and the repulsion of transmission load. By jointly considering the channel conditions, onboard resources and transmission load, a Deep Q-network (DQN) based Intelligent In-orbit Routing (DIIR) algorithm is proposed for selecting a routing path with good channel conditions, sufficient energy and low transmission load to facilitate onboard processing. Moreover, a Deep Deterministic Policy Gradient (DDPG) based Intelligent Resource Allocation (DIRA) algorithm is provided to achieve intelligent and continuous resource allocation for exploiting onboard processing to minimize the transmission delay, while the resource and load states of satellites on the routing path are taken into consideration by including PF as an input. Extensive simulation results demonstrate that employing onboard processing with the proposed DIIR and DIRA algorithms significantly reduces the average transmission delay and packet loss rate.

Heterogeneous information network (HIN) embedding aims to map heterogeneous nodes to the low-dimensional vector space. The existing embedding models cannot determine the optimal length of semantics automatically and reveal full semantic information adaptively for different heterogeneous networks. To address this challenge, an HIN embedding model with adaptive semantic mining is proposed. First, we project heterogeneous nodes into the same space and aggregate the features of target types in the first-degree range. Then, the semantics of different node types is combined through the attention mechanism, and latent meta-paths are mined using the attention coefficients. Finally, multiple feature aggregation layers are stacked with residual blocks. The residual weights control the proportion of semantics transferred between layers to aggregate more distal features selectively. In addition, we designed the Selected DropLink unit to remove links which transfer negative information, which can further improve the resistance of model to over-smoothing. Experiments show that our model can obtain more accurate embedding results and can automatically mine complex semantic connections between heterogeneous nodes without prior definition of meta-path and semantic depth.