Aerial wardens could pose significant security threats to satellite-ground communications due to their stronger received signals than legitimate ground users. To address this issue, the signals from all jamming satellites in low Earth orbit satellite networks, i.e., full jamming strategy (FJS), are utilized to counter the detection of the aerial warden. However, this worsens the communication quality of ground users. To improve it, we utilize the difference in visible spherical crowns between the ground user and the aerial warden due to the Earth blockage to propose the safeguard-zone strategy (SGS) via merely muting the jamming satellites visible to the ground user. To evaluate the effectiveness of the proposed strategies, we propose a stochastic geometry-based analytical framework to derive the covert probability and connection probability. To capture the trade-off between covertness and reliability, the effective covert rate, defined as the product of transmission rate, covert probability, and connection probability, is also analyzed and optimized. The results validate the accuracy of the analytical expressions and illustrate that SGS outperforms the FJS in the connection probability and effective covert rate with a small loss in covert probability, which can be compensated by increasing the transmit power or the number of jamming satellites.

Integrated sensing and communication (ISAC) is expected to enable the fifth-generation (5G) networks to provide ubiquitous communication and sensing. However, some high-dynamic scenarios hinder applications of conventional ISAC schemes owing to the high overhead and poor real-time performance. In this paper, we design a novel ISAC architecture and propose a large language model (LLM) based two-stage beamforming prediction scheme. Specifically, in the first stage, we develop an LLM-based approach to predict the future channel state information (CSI) according to the history echoes. Via the data preprocessing and supervised fine-tuning, the LLM can achieve effective channel prediction task with unstructured data. In the second stage, according to the predicted/estimated CSI, we formulate a beamforming optimization problem to maximize the achievable sum rate while satisfying the quality of service (QoS). Then, we propose a Primary-dual network with the unsupervised adversarial learning to handle it, facilitating the on-line beamforming. Simulation results verify that, compared with the benchmarks, our proposed beamforming prediction scheme not only enjoys a higher channel prediction accuracy but also achieves a better balance between the performance and computational complexity.

Integrated sensing and communication (ISAC) can boost the spectrum efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, it may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning aided transceiver design for ISAC. Particularly, the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio is minimized subject to the constraints of constant modulus signal and waveform similarity by transceiver design. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to solve this non-convex optimization problem. To reduce the complexity, we propose a deep unfolding neural network (NN), which can unfold the underlying ADMMbased iterative algorithm to a lightweight NN with some learnable parameters and circumvent the bisection method using the projected gradient descent. Simulation results demonstrate the effectiveness of our proposed deep unfolding NN.

Abstract Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

integrated sensing and communication (ISAC) is a promising paradigm to alleviate spectrum congestion and facilitate a variety of emerging Internet of Things (IoT) applications. However, the direct links from the ISAC base station (BS) to the users may be blocked due to the obstacles. In this article, we investigate the double-active reconfigurable intelligent surfaces (RISs) assisted ISAC, where two active RISs are used to establish virtual line-of-sight (LoS) links from the ISAC BS to the users. In addition, the sum of the minimum sensing signal-to-interference-plus-noise ratios (SINRs) among multiple targets during a series of time slots is maximized, subject to Quality of Service (QoS) and transmit power constraints, through the joint optimization of transmit, reflection and receive beamforming. We first transform this nonconvex optimization problem in the dynamic environment into a Markov decision process (MDP), and then propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm to solve it. Moreover, to enhance the generalization and stability, we integrate the generative adversarial network (GAN) into the TD3 algorithm and propose a GAN-TD3-based algorithm to handle the beamforming optimization problem. Compared with the TD3-based algorithm, the proposed GAN-TD3-based algorithm achieves the better performance and higher stability at the cost of higher computational complexity and slower convergence speed. Simulation results are presented to verify the effectiveness of our proposed algorithms and the superiority of the active RIS over the passive counterpart.

To achieve sustainable communication and sensing, simultaneous wireless information and power transfer (SWIPT) has been introduced into integrated sensing and communication (ISAC). However, this combination brings significant security challenges due to signal multiplexing and spectrum sharing. In this paper, an active intelligent reflecting surface (IRS) assisted secure integrated sensing and SWIPT system is proposed with the power splitting (PS) model adopted. To maximize the harvested power while satisfying the constraints of sidelobe level ratio and secrecy rate, a problem is formulated to jointly optimize the transmit beamforming, artificial noise (AN) vectors, PS ratios, and amplification factors and phase shifts of active IRS, which is difficult to solve due to the coupled variables. To this end, we decompose it into two sub-problems, and propose two alternating optimization (AO) algorithms to solve them. First, an AO algorithm based on semi-definite relaxation (SDR) is developed. Specifically, we develop a two-layer algorithm to obtain the transmit beamforming matrix, AN covariance matrix and PS ratios, and utilize the penalty-based method to design the coefficients of active IRS. To reduce the complexity caused by the high-dimensional matrix operation of SDR, an AO algorithm based on successive convex approximation (SCA) is proposed, which can approximate the original problem as a sequence of convex counterparts via the first-order Taylor expansion. Simulation results show that the SCA-based AO algorithm can achieve the performance close to that of SDR with lower complexity.

Integrated sensing and communication (ISAC) can enhance spectral efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, effective operation of ISAC may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning-aided transceiver design scheme for ISAC in a cluttered environment. In particular, we optimize the transmit waveform and receive filtering to minimize the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio (SINR), while adhering to the constraints of a constant modulus signal and waveform similarity. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to address this non-convex optimization problem with both equality and inequality constraints. To further reduce the computational complexity, we develop two deep unfolding neural networks (NNs), termed ADMM-DL-NET and ADMM-PGD-NET, to handle this problem, which can unfold the underlying ADMM-based iterative algorithm to a lightweight neural network with learnable parameters and eliminate the need for the bisection method by adopting the Uzawa’s method and projected gradient descent, respectively. Simulation results demonstrate that our proposed deep unfolding NNs can achieve comparable performance to the ADMM-based iterative algorithm with significantly reduced complexity, and outperform the unsupervised learning benchmarks in performance and number of learnable parameters.

Integrated sensing and communication (ISAC), is emerging as a promising technology for future mobile networks. This paper studies the robust secure transmission for intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV)-ISAC networks without eavesdropping channel state information. Particularly, the UAV, as a dual-functional ISAC base station, serves$K$communication users and senses$J$targets with an IRS. Furthermore, an eavesdropper aims at eavesdropping the private information from the UAV to$K$users. Without eavesdropping channel state information, a secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, and the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization, the successive convex approximation and the manifold optimization is proposed to obtain a sub-optimal solution. Simulation results verify the effectiveness of the proposed scheme.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme.

Reconfigurable intelligent surface (RIS) has been widely deployed to assist the covert transmission thanks to its ability of channel reconfiguration. Compared with the conventional RIS, simultaneous transmitting and reflecting RIS (STAR-RIS) can transmit and reflect the incident signal simultaneously. This paper investigates the STAR-RIS assisted covert multicasting with the hardware impairment. Specifically, Alice covertly transmits the common information to two users assisted by one STAR-RIS against two wardens. The covert rate is maximized via jointly optimizing the transmit beamforming, and the reflection and transmission phase shifts, satisfying the transmit power constraint, the covertness constraint and the protocol of STAR-RIS. Owing to the non-convexity, we propose an iterative algorithm based on the alternating optimization, successive convex approximation and penalty-based semi-definite relaxation to obtain a sub-optimal solution. Simulation results verify the effectiveness of STAR-RIS.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. Moreover, ISAC outperforms traditional separate radar and communication systems in terms of both power consumption and spectral efficiency. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, both cases of independent and coupled phase shifts at STAR-RIS are investigated. For independent phase shifts, we develop an alternating direction method of multipliers (ADMM)-based algorithm to decouple the original problem into several tractable subproblems that facilitates the derivation of a closed-form solution to each subproblem. In the scenario with the coupled phase shifts, we first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed schemes, demonstrating STAR-RIS’s superiority over conventional RIS. Moreover, the adopted protocol of STAR-RIS can maintain an excellent balance between performance and complexity.

Reconfigurable intelligent surface (RIS) has been widely deployed to assist the covert transmission thanks to its ability of channel reconfiguration. Compared with the conventional RIS, simultaneous transmitting and reflecting RIS (STAR-RIS) can transmit and reflect the incident signal simultaneously, which provides an opportunity for the full-space covert transmission. This paper investigates the robust covert multicasting aided by the STAR-RIS with the hardware impairment. Specifically, Alice covertly transmits the common information to two single-antenna users assisted by the STAR-RIS against two non-colluding multi-antenna wardens. Furthermore, both energy splitting (ES) and mode switching (MS) protocols of the STAR-RIS are considered. With perfect wiretap channel state information (CSI), the covert rate is maximized via jointly optimizing the transmit beamforming, the reflection and transmission coefficient matrices, satisfying the transmit power constraint, the covertness constraint and the protocol of the STAR-RIS. Moreover, we also investigate the covert rate maximization problem under the case of imperfect wiretap CSI. Due to the non-convexity of the problem, we propose iterative algorithms based on the alternating optimization, successive convex approximation and penalty-based semi-definite relaxation to obtain a near-optimal solution to each problem. Simulation results verify the effectiveness of the STAR-RIS, and show that the ES is superior to the MS.

Due to its capability of channel reconfiguration and enhancement, intelligent reflecting surface (IRS) can be introduced to improve the secrecy rate of non-orthogonal multiple access (NOMA) networks. However, the cost and hardware complexity of full-digital beamforming in existing related studies are high, especially for the systems with massive antennas. This paper studies the robust secure transmission for IRS-aided NOMA networks with cost-effective hybrid beamforming. Specifically, we deploy an IRS to assist the secure transmission from a base station with cost-effective hybrid beamforming to a cell-center user (U1) and a cell-edge user (U2), with the existence of a potential eavesdropper. Two schemes are proposed for guaranteeing the secure transmission of U1 with the perfect and imperfect channel state information (CSI), respectively. With the perfect CSI, the secrecy rate of U1 is maximized subject to the constant modulus constraint and the quality of service (QoS) constraint of U2 via optimizing the hybrid beamforming and phase shifts of IRS. With the imperfect CSI, the achievable rate at U1 is maximized, satisfying its worst-case eavesdropping rate constraint, the constant modulus constraint and the QoS constraint of U2. Because of the non-convexity, we first decompose each problem into two subproblems, respectively. Then, the subproblems are solved via the penalty-based algorithm and the successive convex approximation. Simulation results verify that the two proposed schemes have higher energy efficiency and can boost the security of IRS-aided NOMA networks with perfect and imperfect CSI, respectively.

Integrated sensing and communication (ISAC), which can make full use of the wireless platform and the spectrum for concurrent sensing and communication purposes, is emerging as a promising technology for future mobile networks. This paper studies the secure transmission for intelligent reflecting surface (IRS) aided unmanned aerial vehicle (UAV)-ISAC networks. Particularly, the UAV, as a dual-functional ISAC base station, servesKcommunication users and sensesJtargets with the help of an IRS. Furthermore, a potential eavesdropper, whose channel state information is not available, aims at eavesdropping the private information from the UAV toKusers. A secure transmission scheme is proposed to maximize the average achievable rate via jointly designing the transmit power allocation, the scheduling of users and targets, the phase shifts at IRS, as well as the trajectory and velocity of the UAV. Owing to the non-convexity, an iterative algorithm based on the alternating optimization (AO), the successive convex approximation (SCA) and the manifold optimization (MO) is proposed to obtain a near-optimal solution. Moreover, we also investigate the energy efficiency maximization problem. We develop another iterative algorithm based on the AO, the SCA, the MO and the Dinkelbach’s algorithm to obtain a near-optimal solution to this non-convex fractional programming problem. The effectiveness of the proposed schemes is verified via simulation results.

Due to the ability of channel reconfiguration, intelligent reflecting surface (IRS) can be used to boost the secrecy rate of cognitive non-orthogonal multiple access (NOMA) systems. However, the cost and hardware complexity of full-digital beamforming in existing related studies is high, especially for the systems with massive antennas. In this paper, we investigate the secure transmission for IRS-aided cognitive NOMA systems with cost-effective analog beamforming. The secrecy rate of primary user is maximized subject to the quality of service constraint of secondary user via joint analog and passive beamforming optimization. Owing to the non-convexity, we first transform the problem into two subproblems. Then, each subproblem is tackled via the penalty-based algorithm and the successive convex approximation. Simulation results demonstrate that the proposed transmission scheme has higher energy efficiency and can boost the security of IRS-aided cognitive NOMA systems.

A state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA [1], which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.