Integrated sensing and communication (ISAC) is regarded as a pivotal technology for 6G communication. In this paper, we employ Kullback-Leibler divergence (KLD) as the unified performance metric for ISAC systems and investigate constellation and beamforming design in the presence of clutters. In particular, the constellation design problem is solved via the successive convex approximation (SCA) technique, and the optimal beamforming in terms of sensing KLD is proven to be equivalent to maximizing the signal-to-interference-plus-noise ratio (SINR) of echo signals. Numerical results demonstrate the tradeoff between sensing and communication performance under different parameter setups. Additionally, the beampattern generated by the proposed algorithm achieves significant clutter suppression and higher SINR of echo signals compared with the conventional scheme.

The joint beamforming design challenge for dual-functional radar-communication systems is addressed in this paper. The base station in these systems is tasked with simultaneously sending shared signals for both multi-user communication and target sensing. The primary objective is to maximize the sum rate of multi-user communication, while also ensuring sufficient beampattern gain at particular angles that are of interest for sensing, all within the constraints of the transmit power budget. To tackle this complex non-convex problem, an effective algorithm that iteratively optimizes the joint beamformers is developed. This algorithm leverages the techniques of fractional programming and semidefinite relaxation to achieve its goals. The numerical results confirm the effectiveness of the proposed algorithm.

Integrated sensing and communication (ISAC) technology is a promising candidate for next-generation communication systems. However, severe co-site interference in existing ISAC systems limits the communication and sensing performance, posing significant challenges for ISAC interference management. In this work, we propose a novel interference management scheme based on the normalized least mean square (NLMS) algorithm, which mitigates the impact of co-site interference by reconstructing the interference from the local transmitter and canceling it from the received signal. Simulation results demonstrate that, compared to typical adaptive interference management schemes based on recursive least square (RLS) and stochastic gradient descent (SGD) algorithms, the proposed NLMS algorithm effectively cancels co-site interference and achieves a good balance between computational complexity and convergence performance.

A cooperative passive sensing framework for millimeter wave (mmWave) communication systems is proposed and demonstrated in a scenario with one mobile signal blocker. Specifically, in the uplink communication with at least two transmitters, a cooperative detection method is proposed for the receiver to track the blocker’s trajectory, localize the transmitters and detect the potential link blockage jointly. To facilitate detection, the receiver collects the signal of each transmitter along a line-of-sight (LoS) path and a non-line-of-sight (NLoS) path separately via two narrow-beam phased arrays. The NLoS path involves scattering at the mobile blocker, allowing its identification through the Doppler frequency. By comparing the received signals of both paths, the Doppler frequency and angle-of-arrival (AoA) of the NLoS path can be estimated. To resolve the blocker’s trajectory and the transmitters’ locations, the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations. Finally, a gradient-descent-based algorithm is proposed for joint detection. With the reconstructed trajectory, the potential link blockage can be predicted. It is demonstrated that the system can achieve decimeter-level localization and trajectory estimation, and predict the blockage time with an error of less than 0.1 s.

This paper compares the benefits of communication-assisted sensing and sensing-assisted communication in the context of integrated sensing and communication (ISAC). Communication-assisted sensing leverages the extensive cellular infrastructure to create a vast and cooperative sensor network, enhancing environmental perception accuracy and coverage. On the other hand, sensing-assisted communication utilizes advanced sensing technologies to improve predictive beamforming and channel estimation performance in high-frequency and high-mobility scenarios, thereby increasing communication efficiency and reliability. To validate our analysis, we present an example of channel knowledge map (CKM)-assisted beam tracking. This example demonstrates the practical advantages of incorporating CKM in enhancing beam tracking accuracy. Our analysis confirms that communication-assisted sensing may offer greater development potential due to its wide coverage and cost-effectiveness in large-scale applications.

Integrated sensing and communication (ISAC) is one of the main usage scenarios for 6G wireless networks. To most efficiently utilize the limited wireless resources, integrated super-resolution sensing and communication (ISSAC) has been recently proposed to significantly improve sensing performance with super-resolution algorithms for ISAC systems, such as the Multiple Signal Classification (MUSIC) algorithm. However, traditional super-resolution sensing algorithms suffer from prohibitive computational complexity of orthogonal-frequency division multiplexing (OFDM) systems due to the large dimensions of the signals in the subcarrier and symbol domains. To address such issues, we propose a novel two-stage approach to reduce the computational complexity for super-resolution range estimation significantly. The key idea of the proposed scheme is to first uniformly decimate signals in the subcarrier domain so that the computational complexity is significantly reduced without missing any target in the range domain. However, the decimation operation may result in range ambiguity due to pseudo peaks, which is addressed by the second stage where the total collocated subcarrier data are used to verify the detected peaks. Compared with traditional MUSIC algorithms, the proposed scheme reduces computational complexity by two orders of magnitude, while maintaining the range resolution and unambiguity. Simulation results verify the effectiveness of the proposed scheme.

An integrated sensing and communication (ISAC) scheme for a millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) Vehicle-to-Infrastructure (V2I) system is presented, in which both the access point (AP) and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss, meanwhile compromise between hardware complexity and system performance. Based on the sparse scattering nature of the mmWave channel, the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure. A CANDECOMP/PARAFAC (CP) decomposition-based method is proposed for time-varying channel parameter extraction, including angles of departure/arrival (AoDs/AoAs), Doppler shift, time delay and path gain. Then leveraging the estimates of channel parameters, a nonlinear weighted least-square problem is proposed to recover the location accurately, heading and velocity of vehicles. Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMO-OFDM V2I systems.

In unmanned aerial vehicle (UAV) networks, the high mobility of nodes leads to frequent changes in network topology, which brings challenges to the neighbor discovery (ND) for UAV networks. Integrated sensing and communication (ISAC), as an emerging technology in 6G mobile networks, has shown great potential in improving communication performance with the assistance of sensing information. ISAC obtains the prior information about node distribution, reducing the ND time. However, the prior information obtained through ISAC may be imperfect. Hence, an ND algorithm based on reinforcement learning is proposed. The learning automaton (LA) is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms. Besides, an efficient ND algorithm in the neighbor maintenance phase is designed, which applies the Kalman filter to predict node movement. Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32% compared with the Scan-Based Algorithm (SBA), which proves the efficiency of the proposed ND algorithms.

This paper reviews task scheduling frameworks, methods, and evaluation metrics of central processing unit-graphics processing unit (CPU-GPU) heterogeneous clusters. Task scheduling of CPU-GPU heterogeneous clusters can be carried out on the system level, nodelevel, and device level. Most task-scheduling technologies are heuristic based on the experts’ experience, while some technologies are based on statistic methods using machine learning, deep learning, or reinforcement learning. Many metrics have been adopted to evaluate and compare different task scheduling technologies that try to optimize different goals of task scheduling. Although statistic task scheduling has reached fewer research achievements than heuristic task scheduling, the statistic task scheduling still has significant research potential.

Three-dimensional reconstruction technology plays an important role in indoor scenes by converting objects and structures in indoor environments into accurate 3D models using multi-view RGB images. It offers a wide range of applications in fields such as virtual reality, augmented reality, indoor navigation, and game development. Existing methods based on multi-view RGB images have made significant progress in 3D reconstruction. These image-based reconstruction methods not only possess good expressive power and generalization performance, but also handle complex geometric shapes and textures effectively. Despite facing challenges such as lighting variations, occlusion, and texture loss in indoor scenes, these challenges can be effectively addressed through deep neural networks, neural implicit surface representations, and other techniques. The technology of indoor 3D reconstruction based on multi-view RGB images has a promising future. It not only provides immersive and interactive virtual experiences but also brings convenience and innovation to indoor navigation, interior design, and virtual tours. As the technology evolves, these image-based reconstruction methods will be further improved to provide higher quality and more accurate solutions to indoor scene reconstruction.

As the wireless communication network undergoes continuous expansion, the challenges associated with network management and optimization are becoming increasingly complex. To address these challenges, the emerging artificial intelligence (AI) and machine learning (ML) technologies have been introduced as a powerful solution. They empower wireless networks to operate autonomously, predictively, on-demand, and with smart functionality, offering a promising resolution to intricate optimization problems. This paper aims to delve into the prevalent applications of AI/ML technologies in the optimization of wireless networks. The paper not only provides insights into the current landscape but also outlines our vision for the future and considerations regarding the development of an intelligent 6G network.

Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols facilitates a secure framework for identity authentication, data encryption, and message integrity verification. However, with the recent development in quantum computing technology, the security of conventional key-based SSL/TLS protocols faces vulnerabilities. In this paper, we propose a scheme by integrating the quantum key into the SSL/TLS framework. Furthermore, the application of post-quantum algorithms is used to enhance and complement the existing encryption suites. Experimental results show that the proposed SSL/TLS communication system based on quantum keys exhibits high performance in latency and throughput. Moreover, the proposed system showcases good resilience against quantum attacks.

Differential spatial modulation (DSM) is a multiple-input multiple-output (MIMO) transmission scheme. It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain. In this paper, DSM is investigated using two mapping algorithms: Look-Up Table Order (LUTO) and Permutation Method (PM). Then, the bit error rate (BER) performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments. The results show that PM has a lower BER than the LUTO mapping algorithm, and the latter has lower complexity than the former.