Technology Category
Intelligent Robots: Multimodal Perception & Sensor FusionPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMultiagent Systems: Agent Communication
3D Extended Target Sensing in ISAC: Cram'er-Rao Bound Analysis and Beamforming Design
🤖 AI Summary
研究ISAC系统中三维大目标的感知与通信融合，通过模型分析与数学优化，设计信号加强方案，提出图神经网络波束形成方法，实现感知通信最优平衡，性能优于传统方法。
Technology Category
Application Category
📝 Abstract
This paper investigates an integrated sensing and communication (ISAC) system where the sensing target is a three-dimensional (3D) extended target, for which multiple scatterers from the target surface can be resolved. We first introduce a second-order truncated Fourier series surface model for an arbitrarily-shaped 3D ET. Utilizing this model, we derive tractable Cramer-Rao bounds (CRBs) for estimating the ET kinematic parameters, including the center range, azimuth, elevation, and orientation. These CRBs depend explicitly on the transmit covariance matrix and ET shape. Then we formulate two transmit beamforming optimization problems for the base station (BS) to simultaneously support communication with multiple users and sensing of the 3D ET. The first minimizes the sensing CRB while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for each user, and it is solved using semidefinite relaxation. The second balances minimizing the CRB and maximizing communication rates through a weight factor, and is solved via successive convex approximation. To reduce the computational complexity, we further propose ISACBeam-GNN, a novel graph neural network-based beamforming method that employs a separate-then-integrate structure, learning communication and sensing (C&S) objectives independently before integrating them to balance C&S trade-offs. Simulation results show that the proposed beamforming designs that account for ET shapes significantly outperform existing baselines, offering better communication-sensing performance trade-offs as well as an improved beampattern for sensing. Results also demonstrate that ISACBeam-GNN is an efficient alternative to the optimization-based methods, with remarkable adaptability and scalability.
Problem
Research questions and friction points this paper is trying to address.
ISAC System Optimization
3D Large Target Perception
Signal Enhancement
Innovation
Methods, ideas, or system contributions that make the work stand out.
3D Target Perception
Graph Neural Network Beamforming
Communication-Enhanced Sensing
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