In the upcoming next-generation (5G-Advanced and 6G) wireless networks, sensing as a service will play a more important role than ever before. Recently, the concept of perceptive network is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. This type of technology is typically referred to as Integrated Sensing and Communications (ISAC). In this paper, we propose the concept of sensing quality of service (QoS) in terms of diverse applications. Specifically, the probability of detection, the Cramer-Rao bound (CRB) for parameter estimation and the posterior CRB for moving target indication are employed to measure the sensing QoS for detection, localization, and tracking, respectively. Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services. The proposed schemes can flexibly allocate the limited power and bandwidth resources according to both S&C QoSs. Finally, we study the performance trade-off between S&C services in different resource allocation schemes by numerical simulations.
Integrated Sensing and Communication: Towards Multifunctional Perceptive Network
National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Southern University of Science and Technology（南方科技大学）；Beijing China Internet Network Information Center（中国互联网络信息中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University College London（伦敦大学学院）
RHOSI: Efficient Anti-Jamming Resource Allocation with Holographic Surfaces in UAV-enabled ISAC
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；University of British Columbia（不列颠哥伦比亚大学）；JuliaSpace LLC（JuliaSpace有限责任公司）；University of York（约克大学）
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）
CRLB and Parameter Estimation for OFDM-ISAC with Non-Uniform Sparse Resource Allocation
National Mobile Communications Research Laboratory, Southeast University（东南大学）；School of Information Science and Engineering, Hohai University（河海大学）；Purple Mountain Laboratories（紫金山实验室）
Geometry-Aware Resource Allocation for Network-Level ISAC Systems
China Mobile Research Institute（中国移动研究院）；China Mobile Communications Group Co., Ltd.（中国移动通信集团有限公司）；University of Manchester（曼彻斯特大学）；University College London（伦敦大学学院）
Constellation Selection and Power Allocation for Multi-Cell OFDM-ISAC: Managing Inter-Cell Interference and Sensing Sidelobes
University of Manchester（曼彻斯特大学）；Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）；University College London（伦敦大学学院）；University of Southampton（南安普顿大学）
RSMA-Enabled ISAC Networks with Fluid Antenna Systems: Stochastic Geometry Analysis and Low-Complexity Resource Allocation
University of Benghazi（班加西大学）；King Fahd University of Petroleum and Minerals (KFUPM)（法赫德国王石油矿物大学）；Henan Polytechnic University（河南理工大学）；Southeast University（东南大学）；Taif University（塔伊夫大学）
The Role of ISAC in 6G Networks: Enabling Next-Generation Wireless Systems
Hangzhou International Innovation Institute of Beihang University（北京航空航天大学杭州国际创新研究院）；Southern University of Science and Technology（南方科技大学）；Technische Universität Dortmund（多特蒙德工业大学）；Linköping University（林雪平大学）；Tampere University（坦佩雷大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；School of Automation Science and Electrical Engineering at Beihang University（北京航空航天大学自动化科学与电气工程学院）；State Key Laboratory of Intelligent Manufacturing Systems Technology（智能制造系统技术国家重点实验室）；University of California, San Diego（加州大学圣地亚哥分校）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；CentraleSupélec（中央理工-高等电力学院）
Tensor Decomposition-Based Wireless Sensing for MIMO-OFDM ISAC via Flexible Spatial-Temporal-Spectral Optimization
Nanjing University of Science and Technology（南京理工大学）；Key Laboratory of Near-Range RF Sensing ICs and Microsystems (NJUST), Ministry of Education, School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院、近场射频集成电路与微系统教育部重点实验室）；Qian Xuesen College, Nanjing University of Science and Technology（南京理工大学钱学森学院）；College of Communications Engineering, Army Engineering University of PLA（中国人民解放军陆军工程大学通信工程学院）；The Sixty-Third Research Institute, National University of Defense Technology（国防科技大学第六十三研究所）；National University of Singapore（新加坡国立大学）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）
