基于NOMA的ISAC系统中感知目标分组技术研究

\justifying This study focuses on Integrated Sensing and Communication (ISAC) systems based on Non-Orthogonal Multiple Access (NOMA) technology, aiming to address the negative impact of the increasing number of sensing users on communication and sensing performance, as well as the challenges in base station beamforming design. To this end, this paper proposes a joint optimization strategy for sensing user grouping and base station beamforming. Firstly, in response to the performance degradation caused by the increasing number of sensing users, this paper proposes the idea of grouping sensing targets and presents a grouping algorithm aimed at reducing the channel correlation between sensing targets within each group. Building on this, to optimize the base station beamforming parameters corresponding to each group, this paper develops an optimization algorithm based on Successive Convex Approximation (SCA) and utilizes Semidefinite Relaxation (SDR) technology to obtain feasible solutions. Simulation results demonstrate that the proposed joint optimization strategy can significantly enhance the system's sensing performance by over 20\% than the existed algorithm while ensuring communication performance, thereby validating the application potential of this strategy in NOMA-based ISAC systems.