Reconstruction is the process of capturing the shape and appearance of real objects, which has been a hot topic in computer vision for decades. Thanks to the improved range and angle resolutions provided by the millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) techniques, this paper proposes to leverage the 6G technology for the reconstruction of extended targets. In this new field, we aim to explore a multi-view reconstruction scenario. Specifically, a movable user equipment (UE) merely equipped with the 6G transceiver moves to different known sites at different time slots to emit orthogonal frequency-division multiplexing (OFDM) communication signals so as to sense an extended target from multiple angles. Then, the echo signals observed from different sites are fused to recover the shape of the extended target with high resolution. To achieve this goal, we propose a grid-based method, where the whole region is divided into multiple grids, and the probability of the existence of a point target of the extended target at each grid is calculated based on range and angle estimations obtained at multiple time slots. Numerical results show that with echo signals observed from more angles, the probability density map is more accurate, leading to higher-quality target reconstruction.

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.

In the sixth-generation (6G) integrated sensing and communication (ISAC) cellular network, base stations (BSs) can collaborate with each other to reap not only the cooperative communication gain, but also the networked sensing gain. In contrast to cooperative communication where both line-of-sight (LOS) paths and non-line-of-sight (NLOS) paths are useful, networked sensing mainly relies on the LOS paths. However, in practice, the number of BSs possessing LOS paths to a target can be small. Because the density of user equipments (UEs) is much larger than that of the BSs, this paper considers a UE-assisted networked sensing architecture, where a BS transmits communication signals in the downlink, while the UEs that receive the echo signals scattered by a target can cooperate with the BS to localize it. However, the positions of the UEs are estimated by Global Positioning System (GPS) and subject to unknown errors. Based on the outlier detection technique, this paper proposes an efficient method to select a subset of UEs with accurate position information as anchors for localizing the target. Numerical results show that our scheme can select good UEs with very high probability, indicating that networked sensing can be realized in practice with the aid of UEs.

This paper considers networked device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular system with multipath environment, where the passive targets reflect the downlink signals to the base stations (BSs) via non-line-of-sight (NLOS) paths and/or line-of-sight (LOS) paths, and the BSs share the sensing information extracted from their received echoes to jointly localize the targets. A two-phase localization protocol is considered. In Phase I, we design an efficient method that is able to accurately estimate the range of any path from a transmitting BS to a receiving BS via a target, even if the transmitting and receiving BSs are separated and not perfectly synchronized. In Phase II, we propose an effective method that is able to jointly identify the ranges of the LOS paths between the targets and the BSs as well as associate the ranges of LOS paths with the right targets, such that the number and the locations of the targets both can be accurately estimated. Numerical results verify that our proposed two-phase protocol can achieve high performance of networked sensing in the multipath environment.

Recently, there is a growing interest in achieving integrated sensing and communication (ISAC) in the sixth-generation (6G) cellular network. Inspired by the success of cooperative communication in cloud radio access network, this paper considers a networked device-free sensing architecture based on base station (BS) cooperation to transform the cellular network into a huge sensor that can provide ubiquitous sensing services. Under this framework, the BSs first transmit the downlink communication signals to the mobile users and estimate the range information of the targets based on their e-choes. Next, a central processor collects the range information from all the BSs and localizes each target based on its distances to various BSs. To enable this strategy in 6G network, we will perform joint data association, non-line-of-sight (NLOS) mitigation, and clutter suppression, such that the central processor is able to find out the useful range estimations extracted from the line-of-sight (LOS) paths and match them to the right targets for localization. Numerical results show that our interested networked device-free sensing scheme for the 6G network can localize the targets with high accuracy in the challenging multi-path propagation environment.

This paper considers device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular network to enable integrated sensing and communication (ISAC). A novel two-phase sensing framework is proposed to localize the passive targets that cannot transmit/receive reference signals to/from the base stations (BSs), where the ranges of the targets are estimated based on their reflected OFDM signals to the BSs in Phase I, and the location of each target is estimated based on its ranges to different BSs in Phase II. Specifically, in Phase I, we design a model-free range estimation approach by leveraging the OFDM channel estimation technique for determining the delay values of all the two-way BS-target-BS paths, which does not rely on any BS-target channel model. In Phase II, we reveal that ghost targets may be falsely detected in some cases as all the targets reflect the same signals to the BSs, which thus do not know how to match each estimated range with the right target. Interestingly, we show that the above data association issue is not a fundamental limitation for device-free sensing: under the ideal case of perfect range estimation in Phase I, the probability for ghost targets to exist is proved to be negligible when the targets are randomly located. Moreover, under the practical case of imperfect range estimation in Phase I, we propose an efficient algorithm for joint data association and target localization in Phase II. Numerical results show that our proposed two-phase framework can achieve very high accuracy in the localization of passive targets, which increases with the system bandwidth.