Leveraging A Variety of Anchors in Cellular Network for Ubiquitous Sensing

TL;DR

The goal is to devise a novel 6G-oriented sensing architecture where BSs, UEs, and RISs can work together to provide ubiquitous sensing services.

Abstract

Integrated sensing and communication (ISAC) has recently attracted tremendous attention from both academia and industry, being envisioned as a key part of the standards for the sixth-generation (6G) cellular network. A key challenge of 6G-oriented ISAC lies in how to perform ubiquitous sensing based on the communication signals and devices. Previous works have made great progresses on studying the signal waveform design that leads to optimal communication-sensing performance tradeoff. In this article, we aim to focus on issues arising from the exploitation of the communication devices for sensing in 6G network. Particularly, we will discuss about how to leverage various nodes available in the cellular network as anchors to perform ubiquitous sensing. On one hand, the base stations (BSs) will be the most important anchors in the future 6G ISAC network, since they can generate/process radio signals with high range/angle resolutions, and their positions are precisely known. Correspondingly, we will first study the BS-based sensing technique. On the other hand, the BSs alone may not enable ubiquitous sensing, since they cannot cover all the places with strong line-of-sight (LOS) links. This motivates us to investigate the possibility of using other nodes that are with higher density in the network to act as the anchors. Along this line, we are interested in two types of new anchors - user equipments (UEs) and reconfigurable intelligent surfaces (RISs). This paper will shed light on the opportunities and challenges brought by UE-assisted sensing and RIS-assisted sensing. Our goal is to devise a novel 6G-oriented sensing architecture where BSs, UEs, and RISs can work together to provide ubiquitous sensing services.

Figures (6)

• Figure 1: An illustration of 6G-oriented sensing architecture with a variety of anchors such as the BS, the UE, and the RIS. Because the BS has a LOS path to the bike, it can act as an anchor to localize the bike alone. Because the BS and the two UEs all have LOS paths to the pedestrian, they can both serve as anchors to perform joint localization. Because merely the RIS has a LOS path to the vehicle, it acts as a passive anchor to reflect the signals from the vehicle to the BS, which can leverage these signals to localize the vehicle based on its relative position to the RIS.
• Figure 2: An illustration of RIS-assisted location: An RIS assists the BS to localize the targets without LOS paths to it. Because the ranges and the AOAs from the targets to the BS are not contained in the signals over the target-RIS-BS paths, we have to localize the targets based on their ranges and AOAs to the passive anchor, i.e., the RIS.
• Figure 3: An illustration of networked sensing: 5 BSs connected to the same cloud via the backhaul network are deployed along the road to monitor the traffic conditions. For example, the pedestrian can be jointly localized by three BSs with very high accuracy.
• Figure 4: Performance of the data association algorithm proposed in Liu_ISAC_2022.
• Figure 5: An illustration of localization accuracy when position information of the anchors is not always precise.

Theorems & Definitions (5)

• Example 1
• Example 2
• Example 3
• Example 4
• Example 5