Sensing-Aided Channel Estimation for Near-Field MIMO ISAC Systems via Cross-Attention Transformer
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子信息工程学院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学通信与信息工程学院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Southeast University（东南大学）
Near-field integrated sensing and communication (ISAC) can deliver the high spatial resolution and transmission capability with the shared spectrum and hardware. Due to the partial overlap between communication scatterers and radar targets, the sensing information can provide valuable priors to enhance the channel estimation while fusing the two heterogeneous modalities remain challenging. To address this problem, a Cross-Attention Transformer based Channel Estimation Neural Network (CAT-CENet) is developed, which includes a communication pilot branch generating the the Key and Value features and a sensing information branch generating the Query feature. By elaborating the three-module structure, CAT-CENet can focus on features of overlapped targets automatically without need of identifying them in advance. The modality contribution is theoretically analyzed based on the Shapley value to verify the cross-attention gain achieved by CAT-CENet. Simulation results show that CAT-CENet outperforms the state-of-the-art schemes, especially with the higher overlapping proportion, and is robust to the model pruning.
Sensing-Constrained Diversity-Multiplexing Tradeoff in MIMO ISAC: A Geometric Approach
Department of Electronic Engineering, BNRist, Tsinghua University（清华大学电子工程系，信息科学技术前沿研究院）；Division of Information Science and Engineering, KTH Royal Institute of Technology（皇家理工学院信息与工程学院）
Joint Beamforming and Position Optimization for Fluid RIS-aided ISAC Systems
Shenzhen University（深圳大学）；State Key Laboratory of Radio Frequency Heterogeneous Integration, Shenzhen University（深圳大学射频异构集成国家重点实验室）；Department of Electrical and Electronic Engineering, the University of Hong Kong（香港大学电子电气工程系）
University of Electronic Science and Technology of China（电子科技大学）；King’s College London（伦敦国王学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Shenzhen University（深圳大学）
Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems
通感一体化（Integrated Sensing and Communication，ISAC）技术在完全共享的平台上同时实现雷达探测和用户通信功能，在节省硬件和频谱资源方面具有巨大潜力。在ISAC系统中采用可重构智能表面（RIS）可以提供一条虚拟视线路径来克服阻塞问题，并引入新的自由度（DoF）来进一步提高系统性能。然而，无源RIS的乘性衰落效应限制了其在无直接链路情况下的应用，这促进了有源RIS的发展。在本文中，我们考虑一个积极的RIS辅助ISAC系统，旨在联合设计的发射波束形成器，积极的RIS反射和雷达接收滤波器，以最大限度地提高雷达输出信噪比（SNR），同时保证预定义的信号干扰加噪声比（SINR）的通信用户。为了解决这个非凸问题，一个有效的算法开发利用块坐标下降（BCD），Dinkelbach的变换和优化最小化（MM）的技术。仿真结果验证了在ISAC系统中部署有源RIS的显著进步，与无源RIS辅助的ISAC系统相比，它可以实现高达32 dB的雷达信噪比增强。
Discrete-Time CRLB-based Power Allocation for CF MIMO-ISAC with Joint Localization and Velocity Sensing
University of Leicester（莱斯特大学）；Institute for Communication Systems (ICS) of University of Surrey（萨里大学通信系统研究所）；Institute for Communication Systems and Measurement of China（中国通信与测量研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）
