Songlin Sun’s research while affiliated with Beijing University of Posts and Telecommunications and other places

Point clouds, a prominent method of 3D representation, are extensively utilized across industries such as autonomous driving, surveying, electricity, architecture, and gaming, and have been rigorously investigated for their accuracy and resilience. The extraction of semantic information from scenes enhances both human understanding and machine perception. By integrating semantic information from two-dimensional scenes with three-dimensional point clouds, researchers aim to improve the precision and efficiency of various tasks. This paper provides a comprehensive review of the diverse applications and recent advancements in the integration of semantic information within point clouds. We explore the dual roles of semantic information in point clouds, encompassing both implicit guidance and explicit representation, across traditional and emerging tasks. Additionally, we offer a comparative analysis of publicly available datasets tailored to specific tasks and present notable observations. In conclusion, we discuss several challenges and potential issues that may arise in the future when fully utilizing semantic information in point clouds, providing our perspectives on these obstacles. The classified and organized articles related to semantic based point cloud tasks, and continuously followed up on relevant achievements in different fields, which can be accessed through https://github.com/Jasmine-tjy/Semantic-based-Point-Cloud-Tasks.

As an emerging reconfigurable antenna technology, fluid antenna system (FAS) has the capability of improving both sensing and communication (S&C) performance by switching the antenna position over the available ports. This increased spatial degree-of-freedom (DoF) by FAS can be translated into enlarging the trade-off region for integrated sensing and communication (ISAC). In this letter, we propose a signal model for FAS-enabled ISAC and jointly optimize the transmit beamforming and port selection of FAS. In particular, our objective is to minimize the transmit power, while satisfying both communication and sensing requirements. To tackle this, an efficient iterative algorithm based on sparse optimization, convex approximation, and a penalty approach is developed. Our simulation results illustrate that the proposed scheme can attain 33% reductions in the transmit power with guaranteed S&C performance, showing the great potential of FAS for striking a balance between S&C in ISAC systems.

Integrated sensing and communications (ISAC) systems employ dual-functional signals to simultaneously accomplish radar sensing and wireless communication tasks. However, ISAC systems open up new sensing security vulnerabilities to malicious illegitimate eavesdroppers (Eves) that can also exploit the transmitted waveform to extract sensing information from the environment. In this paper, we investigate the beamforming design to enhance the sensing security of an ISAC system, where the communication user (CU) serves as a sensing Eve. Our objective is to maximize the mutual information (MI) for the legitimate radar sensing receiver while considering the constraint of the MI for the Eve and the quality of service to the CUs. Then, we consider the artificial noise (AN)-aided beamforming to further enhance the sensing security. Simulation results demonstrate that our proposed methods achieve MI improvement of the legitimate receiver while limiting the sensing MI of the Eve, compared with the baseline scheme, and that the utilization of AN further contributes to sensing security.

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramér-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.