Environment Sensing Considering the Occlusion Effect: A Multi-View Approach

TL;DR

This paper introduces a multi-view sensing algorithm for environment detection in wireless networks that accounts for occlusion effects, utilizing multiple user and base station perspectives to improve accuracy.

Contribution

It proposes the GAMP-MVSVR algorithm, which reconstructs sparse environmental features considering occlusion, enhancing sensing performance through multi-view and multi-BS collaboration.

Findings

Algorithm converges reliably in simulations.

Multi-BS collaboration improves sensing accuracy.

Effective occlusion detection enhances environmental reconstruction.

Abstract

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.