User Equipment Assisted Localization for 6G Integrated Sensing and Communication

TL;DR

This work tackles UE-assisted localization in 6G ISAC networks where a BS and multiple UEs cooperate to localize multiple passive targets using range information. It introduces a two-phase protocol for the passive sensing mode, employing LASSO-based range estimation and iterative removal of UEs with faulty GPS estimates, followed by data association and multilateration to localize targets even under synchronization errors. The framework is extended to an active sensing mode, where uplink ranges provide additional constraints that reduce Phase II complexity and tighten data-association search spaces. The proposed algorithms demonstrate high localization accuracy in simulations, and the active mode consistently reduces computational effort while improving performance, highlighting the potential of UE-assisted sensing for ubiquitous 6G ISAC deployments.

Abstract

This paper investigates user equipment (UE) assisted device-free networked sensing in the sixth-generation (6G) integrated sensing and communication (ISAC) system, where one base station (BS) and multiple UEs, such as unmanned aerial vehicles (UAVs), serve as anchors to cooperatively localize multiple passive targets based on the range information. Three challenges arise from the above scheme. First, the UEs are not perfectly synchronized with the BSs. Second, the UE (anchor) positions are usually estimated by the Global Positioning System (GPS) and subject to unknown errors. Third, data association is challenging, since it is hard for each anchor to associate each rang estimation to the right target under device-free sensing. We first tackle the above three challenges under a passive UE based sensing mode, where UEs only passively hear the signals over the BS-target-UE paths. A two-phase UE assisted localization protocol is proposed. In Phase I, we design an efficient method to accurately estimate the ranges from the BS to the targets and those from the BS to the targets to the UEs in the presence of synchronization errors between the BS and the UEs. In Phase II, an efficient algorithm is proposed to localize the targets via jointly removing the UEs with quite inaccurate position information from the anchor set and matching the estimated ranges at the BS and the remaining UEs with the targets. Next, we also consider an active UE based sensing mode, where the UEs can actively emit signals to obtain additional range information from them to the targets. We show that this additional range information can be utilized to significantly reduce the complexity of Phase II in the aforementioned two-phase localization protocol. Numerical results show that our proposed UE assisted networked sensing scheme can achieve very high localization accuracy.

Figures (5)

• Figure 1: UE assisted ISAC network: Some UEs just communicate with the BS in the uplink/downlink. The other UEs can aid the BS to perform sensing. Specifically, some idle UEs can passively hear the echo signals reflected by the targets to perform passive sensing. On the other hand, some UEs can utilize their uplink signals to actively sense the targets. Because uplink/downlink communication techniques are quite mature, this paper mainly considers how the UEs can assist the BS to localize the targets.
• Figure 2: STO estimation error probability versus the number of UEs.
• Figure 3: Performance comparison of the benchmark scheme and the proposed scheme under the passive UE based and the active UE based sensing modes with single target.
• Figure 4: Performance comparison of the benchmark schemes and the proposed scheme under the passive UE based sensing mode with multiple targets.
• Figure 5: Performance comparison of the proposed scheme under the passive UE based and the active UE based sensing modes with multiple targets.