We consider an uplink integrated sensing and communications (ISAC) scenario where the detection of data symbols from multiple user equipment (UEs) occurs simultaneously with a three-dimensional (3D) estimation of the environment, extracted from the scattering features present in the channel state information (CSI) and utilizing the same physical layer communications air interface, as opposed to radar technologies. By exploiting a discrete (voxelated) representation of the environment, two novel ISAC schemes are derived with purpose-built message passing (MP) rules for the joint estimation of data symbols and status (filled/empty) of the discretized environment. The first relies on a modular feedback structure in which the data symbols and the environment are estimated alternately, whereas the second leverages a bilinear inference framework to estimate both variables concurrently. Both contributed methods are shown via simulations to outperform the state-of-the-art (SotA) in accurately recovering the transmitted data as well as the 3D image of the environment. An analysis of the computational complexities of the proposed methods reveals distinct advantages of each scheme, namely, that the bilinear solution exhibits a superior robustness to short pilots and channel blockages, while the alternating solution offers lower complexity with large number of UEs and superior performance in ideal conditions.

翻译：我们考虑一个上行链路集成感知与通信（ISAC）场景：其中，来自多个用户设备（UE）的数据符号检测与基于信道状态信息（CSI）中散射特征提取的环境三维（3D）估计同时进行，且利用相同的物理层通信空中接口，而非雷达技术。通过利用环境的离散（体素化）表示，我们推导出两种新颖的ISAC方案，这些方案采用针对数据符号和离散化环境状态（填充/空置）联合估计而专门设计的消息传递（MP）规则。第一种方案依赖于模块化反馈结构，其中数据符号与环境交替估计；而第二种方案则利用双线性推断框架同时估计这两个变量。仿真结果表明，这两种方法在准确恢复传输数据以及环境3D图像方面均优于当前最先进（SotA）技术。对所提方法计算复杂度的分析揭示了每种方案的独特优势：双线性解在短导频和信道阻塞情况下表现出更强的鲁棒性，而交替解在理想条件下对大量UE具有更低的复杂度并展现出更优性能。