In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates as a monostatic radar system, enabling comprehensive scanning of the monitored area and generating range-angle maps that provide information regarding the position of a group of heterogeneous objects. The acquired maps are subsequently fused in the FC. Then, a convolutional neural network (CNN) is employed to infer the category of the targets, e.g., pedestrians or vehicles, and such information is exploited by an adaptive clustering algorithm to group the detections originating from the same target more effectively. Finally, two multi-target tracking algorithms, the probability hypothesis density (PHD) filter and multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the targets. Numerical results demonstrated that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm, while keeping communication services to UEs with a reduction of the communication capacity in the order of 10% to 20%. The impact of the number of BSs engaged in sensing is also examined, and we show that in the specific case study, 3 BSs ensure a localization error below 1 m.

翻译：本文研究了一种由多个基站组成的联合感知与通信网络性能，这些基站通过融合中心协作交换感知环境信息，同时与一组用户设备建立通信链路。网络中的每个基站作为单基地雷达系统运行，能够对监控区域进行全面扫描，生成提供一组异构目标位置信息的距离-角度图。所获取的距离-角度图随后在融合中心进行融合，并利用卷积神经网络推断目标类别（如行人或车辆），该信息被自适应聚类算法用于更有效地归并源自同一目标的探测结果。最后，应用概率假设密度滤波器和多伯努利混合滤波器两种多目标跟踪算法来估计目标状态。数值结果表明，本框架能够提供显著的感知性能，实现最优子模式分配小于60厘米，同时保持对用户设备的通信服务，通信容量降低幅度约为10%至20%。文中还研究了参与感知的基站数量对系统的影响，并证明在特定案例中，3个基站可确保定位误差低于1米。