In this paper, we consider the problem of sensing the environment within a wireless cellular framework. Specifically, multiple user equipments (UEs) send sounding signals to one or multiple base stations (BSs) and then a centralized processor retrieves the environmental information from all the channel information obtained at the BS(s). Taking into account the occlusion effect that is common in the wireless context, we make full use of the different views of the environment from different users and/or BS(s), and propose an effective sensing algorithm called GAMP-MVSVR (generalized-approximate-message-passing-based multi-view sparse vector reconstruction). In the proposed algorithm, a multi-layer factor graph is constructed to iteratively estimate the scattering coefficients of the cloud points and their occlusion relationship. In each iteration, the occlusion relationship between the cloud points of the sparse environment is recalculated according to a simple occlusion detection rule, and in turn, used to estimate the scattering coefficients of the cloud points. Our proposed algorithm can achieve improved sensing performance with multi-BS collaboration in addition to the multi-views from the UEs. The simulation results verify its convergence and effectiveness.

中文翻译：

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考虑遮挡效应的环境感知：多视图方法


在本文中，我们考虑在无线蜂窝框架内感知环境的问题。具体地，多个用户设备(UE)向一个或多个基站(BS)发送探测信号，然后中央处理器从在BS处获得的所有信道信息中检索环境信息。考虑到无线环境中常见的遮挡效应，我们充分利用不同用户和/或 BS 对环境的不同视图，并提出了一种有效的感知算法，称为 GAMP-MVSVR（广义近似） -基于消息传递的多视图稀疏向量重建）。在该算法中，构建多层因子图来迭代估计浊点的散射系数及其遮挡关系。在每次迭代中，根据简单的遮挡检测规则重新计算稀疏环境的云点之间的遮挡关系，进而用于估计云点的散射系数。除了来自 UE 的多视图之外，我们提出的算法还可以通过多 BS 协作来实现更高的感知性能。仿真结果验证了其收敛性和有效性。