As a solution for city-range integrated sensing and communication and intelligent positioning, 5G high-precision positioning is flooding into reality. Nevertheless, the underlying positioning security concerns have been overlooked, posing threats to more than a billion emerging 5G localization applications. In this work, we first identify a novel and far-reaching security vulnerability affecting current 5G positioning systems. Correspondingly, we introduce a threat model, called the selective-PRS-spoofing attack (SPS), which can cause substantial localization errors or even fully-hijacked positioning results at victims. The attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel. Different from traditional communication-oriented 5G attacks, SPS targets the localization and exerts real-world threats. More seriously, we confirm that SPS attacks can evade multiple latest 3GPP R18 defense, and analyze its great stealthiness from its precise spoofing feature. To tackle this challenge, a Deep Learning-based defence method called in-phase quadrature intra-attention network (IQIA-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate the effectiveness of our method and its good robustness to noise.

Witnessing its large swaths of success in various fields, digital twins (DTs) are considered a promising scheme for 6th Generation (6G) cellular systems, showing a leading edge in networking and communication modelling. However, another 6G core property ofhigh-precision positioningcan hardly be supported by existing 6G DT solutions due to the lack ofenvironmental modellingandsignal interactions with physical scenes. This shortcoming yields a series of challenges in 6G DT-enabled positioning, including positioning data acquisition, accuracy enhancement, and continuous optimization. In this regard, we propose a novel paradigm of localization-oriented DT (LocDT) with a compound architecture of 7 sub-DT layers to characterize the 6G integrated-localization-and-communication (ILAC) feature. LocDT starts from a physical environment sublayer to mirror 6G signal interactions within a real-world scenario, along with an ILAC baseband sublayer and a channel frequency Polar-coordinate (CFP) image construction method to provide finer-grained fingerprints. Furthermore, insight from LocDT reveals an interesting phenomenon: the channel features of Line-of-Sight (LoS) / None-Los (NLoS) gNodeBs makedifferentiated-contributionsto positioning accuracy, especially in wide-existingpartial-LoS-coveragescenarios. Benefiting from this, a DT-driven Artificial Intelligence (AI) positioning model, SSI-Net, is designed with a device-attention mechanism, achieving complementary improvements in accuracy. Evaluation results show LocDT and SSI-Net’s advantages from a position-of-strength in accuracy and time overhead, outperforming state-of-the-art models.

5G positioning systems, as a solution for city-range integrated-sensing-and-communication (ISAC), are flooding into reality. However, the positioning security aspects of such an ISAC system have been overlooked, bringing threats to more than a billion 5G users. In this paper, we propose a new threat model for 5G positioning scenarios, namely the selective-PRS-spoofing attack (SPS), disabling the latest security enhancement method reported in 3GPP R18. In our attack pattern, the attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel, which can introduce substantial localization errors at victims or even completely control the positioning results. Worse, such attacks are transparent to both the UE-end and the network-end due to their stealthiness and easily bypass the current 3GPP defense mechanisms. To solve this problem, a DL-based defense method called in-phase quadrature Network (IQ-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate that our method has 98% defense accuracy and good robustness to noise.

Unmanned aerial vehicles (UAVs) facilitate services in civilian and industrial fields but suffer from a limited direct link operating range and unreliable satellite positioning in urban canyons. Fortunately, cellular-connected UAVs (CCUAVs) overcome these shortcomings, benefitting from the beyond 5th generation (B5G) network’scity-level coverageandhigh-precision positioning capabilities, and are considered a paradigm of 5G-advanced and beyond. However, in a challenging airspace (e.g., urban canyon), the CCUAV localization accuracy deteriorates due tolow signal-to-interference-plus-noise (SINR) air-ground channelsandstrong multipath effects. To solve these problems, we first construct channel amplitude-phase response (CAPR) images to characterize the cellular channel in a challenging airspace for CCUAV positioning. In particular, the effect of down-tilted antennas and high-dimensional channel features are embedded into CAPR images, to meet the relevant cellular communication criteria. Subsequently, a deep learning (DL) model, the scale-shared quarter network (SSQ-Net), is devised for CAPR image-based positioning, along with a robustness enhancement method. With this method, the multipath effects and interference in challenging environments are exploited to improve positioning accuracy and robustness, instead of being treated as detriments. Finally, the experimental results in a typical urban canyon show that our method outperforms state-of-the-art methods in terms of accuracy and robustness.

Location-based services (LBSs) provide necessary infrastructure for daily life, from bicycle sharing to nursing care. In contrast to traditional positioning methods such as Wi-Fi, Bluetooth, and ultra-wideband (UWB), fifth-generation (5G) networking is defined as a paradigm ofintegrated sensing and communication(ISAC). With its advantages of wide-range coverage and indoor-outdoor integration, 5G is promising for high-precision positioning in indoor and urban canyon environments. However, 5G location studies face great obstacles due to the lack of commercialized 5G ISAC base stations that support positioning functions as well as publicly available datasets. In this paper, we first propose a dataset generation method, the Multilevel Feature Synthesis Method (Multilevel-FSM), to obtain positioning features. In particular, the features of a multiple-input multiple-output (MIMO) channel are flattened into a single image to increase the information density and improve feature expression, and data augmentation is performed to provide stronger robustness to noise. Subsequently, we devise a specially designed deep learning positioning method, Multipath Res-Inception (MPRI), trained on the proposed dataset to enhance positioning accuracy. Finally, the results of extensive experiments conducted in two typical 5G scenarios (indoors and urban canyon) show that Multilevel-FSM and MPRI outperform state-of-the-art works in accuracy, time overhead and robustness to noise.