Integrated sensing and communication (ISAC) is regarded as a recent advanced technology, which is expected to realize the dual functions of sensing and communication simultaneously in one system. Nevertheless, it still faces the challenges of the information security and transmission robustness caused by the openness of wireless channel, especially under antagonistic environment. Hence, this article develops a generalized framework, named cognitive joint jamming, sensing and communication (cognitive $J^2$SAC), to empower the current sensing/communication/jamming system with a "brain" for realizing precise sensing, reliable communication and effective jamming under antagonistic environment. Three kinds of gains can be captured by cognitive $J^2$SAC, including integrated gain, cooperative gain and cognitive gain. Moreover, we highlight the enabling mechanism among jamming, sensing, and communication, as well as illustrating several typical use cases of cognitive $J^2$SAC. Furthermore, several key enabled technologies are analyzed and a typical sensing enhance integrated communication and jamming case study is discussed to verify the effectiveness of the proposed method. Last but not the least, the future directions are listed before concluding this article.

There is growing interest in the integrated sensing and communication (ISAC) to extend the 5G+/6G network capabilities by introducing sensing capability. While the solutions for mono-static or bi-static ISAC have shown feasibility and benefits based on existing 5G physical layer design, whether and how to coordinate multiple ISAC devices to better exert networking performance are rarely discussed. $3^{\text{rd}}$ Partnership Project (3GPP) has initiated the ISAC use cases study, and the follow-up studies for network architecture could be anticipated. In this article, we focus on gNB-based sensing mode and propose ISAC functional framework with given of high-level service procedures to enable cellular based ISAC services. In the proposed ISAC framework, three types of network functions for sensing service as Sensing Function (SF), lightweight-Edge Sensing Function (ESF) and full-version-ESF are designed with interaction with network nodes to fulfill the latency requirements of ISAC use cases. Finally, with simulation evaluations and hardware testbed results, we further verify the performance benefit and feasibility to enable ISAC in 5G for the gNB-based sensing mode with new design on SF and related signaling protocols.

Connected autonomous vehicles (CAVs) are a promising paradigm for implementing intelligent transportation systems. However, in CAVs scenarios, the sensing blind areas cause serious safety hazards. Existing vehicle-to-vehicle (V2V) technology is difficult to break through the sensing blind area and ensure reliable sensing information. To overcome these problems, considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication (ISAC) technology. The mmWave base station (mmBS) transmits multiple beams consisting of communication beams and sensing beams. The sensing beams are responsible for sensing objects within the CAVs blind area, while the communication beams are responsible for transmitting the sensed information to the CAVs. To reduce the impact of inter-beam interference, a joint multiple beamwidth and power allocation (JMBPA) algorithm is proposed. By maximizing the communication transmission rate under the sensing constraints. The proposed non-convex optimization problem is transformed into a standard difference of two convex functions (D.C.) problem. Finally, the superiority of the proposed JMBPA algorithm is verified by iterative solutions. The average transmission rate of communication beams remains over 3.4 Gbps, showcasing a significant improvement compared to other algorithms. Moreover, the satisfaction of sensing services remains steady.

By multiplexing information symbols in the delay-Doppler (DD) domain, orthogonal time frequency space (OTFS) is a promising candidate for future wireless communication in high-mobility scenarios. In addition to the superior communication performance, OTFS is also a natural choice for radar sensing since the primary parameters (range and velocity of targets) in radar signal processing can be inferred directly from the delay and Doppler shifts. Though there are several works on OTFS radar sensing, most of them consider the integer parameter estimation only, while the delay and Doppler shifts are usually fractional in the real world. In this paper, we propose a two-step method to estimate the fractional delay and Doppler shifts. We first perform the two-dimensional (2D) correlation between the received and transmitted DD domain symbols to obtain the integer parts of the parameters. Then a difference-based method is implemented to estimate the fractional parts of delay and Doppler indices. Meanwhile, we implement a target detection method based on a generalized likelihood ratio test since the number of potential targets in the sensing scenario is usually unknown. The simulation results show that the proposed method can obtain the delay and Doppler shifts accurately and get the number of sensing targets with a high detection probability.

Artificial intelligence (AI) models are promising to improve the accuracy of wireless positioning systems, particularly in indoor environments where unpredictable radio propagation channel is a great challenge. Although great efforts have been made to explore the effectiveness of different AI models, it is still an open problem whether these models, trained with the data collected from all base stations (BSs), could work when some BSs are unavailable. In this paper, we make the first effort to enhance the generalization ability of AI wireless positioning model to adapt to the scenario where only partial BSs work. Particularly, a Siamese Network based Wireless Positioning Model (SNWPM) is proposed to predict the location of mobile user equipment from channel state information (CSI) collected from 5G BSs. Furthermore, a Feature Aware Attention Module (FAAM) is introduced to reinforce the capability of feature extraction from CSI data. Experiments are conducted on the 2022 Wireless Communication AI Competition (WAIC) dataset. The proposed SNWPM achieves decimeter-level positioning accuracy even if the data of partial BSs are unavailable. Compared with other AI models, the proposed SNWPM can reduce the positioning error by nearly 50% to more than 60% while using less parameters and lower computation resources.

In this paper, joint location and velocity estimation (JLVE) of vehicular terminals for 6G integrated communication and sensing (ICAS) is studied. We aim to provide a unified performance analysis framework for ICAS-based JLVE, which is challenging due to random fading, multipath interference, and complexly coupled system models, and thus the impact of channel fading and multipath interference on JLVE performance is not fully understood. To address this challenge, we exploit structured information models of the JLVE problem to render tractable performance quantification. Firstly, an individual closed-form Cramer-Rao lower bound for vehicular localization, velocity detection and channel estimation, respectively, is established for gaining insights into performance limits of ICAS-based JLVE. Secondly, the impact of system resource factors and fading environments, e.g., system bandwidth, the number of subcarriers, carrier frequency, antenna array size, transmission distance, spatial channel correlation, channel covariance, the number of interference paths and noise power, on the JLVE performance is theoretically analyzed. The associated closed-form JLVE performance analysis can not only provide theoretical foundations for ICAS receiver design but also provide a performance benchmark for various JLVE methods.