Integrated sensing and communication (ISAC) is a key enabling technology for sixth-generation (6G) mobile communication systems, achieving seamless integration of communication and sensing functions. Cooperative sensing, where the transmitter and receiver are not co-located, serves as a key enabler for ISAC, significantly enhancing the sensing performance while reducing the implementation complexity of the receiver. However, practical deployments of cooperative sensing still face numerous challenges, such as synchronization and interference. This paper presents a set of advanced beam management methods specifically designed for the cooperative sensing system, offering a comprehensive framework to address these challenges. Specifically, we first analyze the strong/weak path effect (SWPE), a critical phenomenon caused by diverse target reflectivities and propagation paths, which severely degrades both synchronization accuracy and target detection. To counteract this, we propose an adaptive path power allocation method compatible with both all-digital and hybrid beamforming architectures. This method intelligently allocates power across different paths to mitigate the SWPE, thereby ensuring reliable synchronization via the direct path while enhancing the detectability of weak targets. As a result, the proposed method improves the target detection probability by over 30%. Furthermore, an adaptive interference suppression method is designed to reduce interference while maintaining sensing/communication quality, which obtains the SINR gain of around 5 dB, compared to the traditional full nulling method. Experimental results validate the effectiveness of robust synchronization and our proposed power allocation. This study lays a solid foundation for beamforming optimization in cooperative sensing systems, facilitating high-accuracy sensing and communication in complex environments.

Abstract A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network.

The 6th generation mobile communication system (6G) will provide everything as a service (XaaS), where X includes communication, sensing, computing, artificial intelligence (AI), big data and security and more. Novel features such as sensing as a Service (SaaS) will contribute to further realising Internet of Everything (IoE). Integrated Sensing and Communication (ISAC) is identified as one of the six usage scenarios for 6G by the International Telecommunication Union Radiocommunication Sector (ITU-R), and the corresponding studies on the detailed technical performance requirements and evaluation methodologies have begun in 2024. Although ISAC has become a popular topic, there are no systematic performance requirements metrics and corresponding evaluation methodologies defined for SaaS in a mobile communication system, while conventional key performance indicators (KPI) for radar systems have been borrowed currently. Therefore, to fill this gap, this paper proposes SensCAP, a systematic CAPability performance metric composed of Sensing Capacity, Accuracy and Probability. The sensing capacity reflects the comprehensive sensing performance, which can be expressed as the number of targets that can be detected per unit area within unit time, given Sensing Quality of Service (QoS) requirements consisting of sensing accuracy and probability. Furthermore, the performance evaluation of the SensCAP is conducted through system simulation using proposed evaluation methodologies, and the KPI values are suggested as the guidelines for further study in ITU-R.

The combination of communication and sensing is envisioned as a novel feature in the forthcoming sixth-generation (6G) wireless communication. The conventional approach to the joint sensing and communication (JSAC) system is utilizing one base station (BS) as both a sensing transmitter and a sensing receiver, which is known as monostatic sensing. However, the resulting self-interference issue requires additional hardware promotion to achieve full-duplexing. To overcome this issue, in this paper, we focus on cooperative sensing where the transmitter and receivers are non-co-located, which includes the bistatic and multistatic sensing. Specifically, the system model of cooperative sensing based on mobile networks is established. To demonstrate the feasibility of cooperative sensing, the bistatic radar cross section (RCS) is provided. As for the sensing method, a refined orthogonal matching pursuit (R-OMP) method is proposed to estimate the channel parameters and data fusion is also provided to derive the objects’ positions and velocities. Considering the non-negligible interference in the cooperative JSAC networks, we also discuss interference management in this paper. Simulation results show that the proposed cooperative sensing system improves the position and velocity estimation accuracy by over 20% when compared with monostatic sensing. The preliminary experiment results also verify the feasibility of the proposed system.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) based integrated localization and communication (ILAC) signal transmission scheme, where communication and localization signals of different user equipments (UEs) are superimposed respectively. We analyze the performance of localization and communication in terms of position error bound (PEB) and effective data rate (EDR) theoretically. We further compare the proposed NOMA-ILAC method with current NOMA-orthogonal multiple access (OMA) method where different UEs’ communication signals are superposed while their localization signals are transmitted orthogonally, both from theoretical analysis and simulations. Performance trade-off is then carried out w.r.t. the time-domain resource allocation. Numerical results demonstrate that by adapting the time allocation ratio, the proposed method is able to improve the communication performance by up to 33%, when the PEB of the two methods are equal.

Utilizing the unmanned aerial vehicle (UAV) swarm to realize location awareness of ground users (GUs) is a promising technology in green aerial Internet of Things (IoT) systems. However, a typical UAV-based positioning system is frequently applied in dense urban areas, where the traditional satellite positioning systems are severely impaired. Due to the co-frequency interference and the inevitable defect of limited resources of the positioning anchors, the UAVs positioning ability and system resource utilization suffer serious challenges. In this article, we propose a novel ranging code design for UAV swarm self-positioning, which consists of the code truncation algorithm and Greedy-based code group optimization algorithm, aiming to obtain the code group with the optimal correlation characteristics and shorter code length based on the pseudo noise (PN) code. In particular, the design can flexibly change the truncated length, so as to ensure the self-positioning accuracy with less positioning resource. To evaluate the influence of the proposed design for the self-positioning of UAVs on the aerial IoT, the root-mean-square error (RMSE) of UAVs self-positioning is provided, and based on this, the Cramer–Rao lower bound (CRLB) of GUs location estimate is derived. Numerical results demonstrate that the ranging code design is a superior way for future green aerial IoT in both positioning and resource utilization, as compared with a state-of-the-art approach.

Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV’s mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEMs) of realistic terrain for reliability analysis. Subsequently, we propose a reliability-prediction method and derive the corresponding metric to evaluate the system’s ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte Carlo simulations show that in mountainous environments, the proposed reliability-prediction method could achieve a prediction accuracy that is at least 36.8$\%$higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability-enhancement method improves the service reliability of the proposed system by 23$\%$and 29$\%$, respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.

Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of Line-of-Sight (LoS) communication, as well as the diversity and number of reference stations, the application of ultradense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X-integrated positioning methodology in UDN, in which the V2I, V2V, and inertial navigation systems (INSs) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-LoS (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based${K}$-means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-the-art approach.

Nonorthogonal multiple access (NOMA) has been identified as a promising technology in millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) communication networks for Internet-of-Things (IoT) applications, which has the advantages of both massive connectivity and high spectral efficiency. However, few researchers have considered the probability of introducing NOMA to a positioning system. In this article, a novel mmWave MIMO-NOMA-based positioning system is proposed, which is capable of meeting the requirements of IoT applications. We establish a NOMA-based positioning model from the perspective of the system level, along with the design of a transmission strategy. To characterize the positioning performance, the position error bound (PEB) is selected as evaluation criteria and theoretical expressions of the PEB are provided. Simulations of comparing localization performance between NOMA and conventional orthogonal multiple access (OMA) are conducted by using the theoretical analysis. The numerical results show that the application of NOMA to localization is a viable way to reduce the PEB compared to OMA. This article further shows under what circumstances can NOMA outperform OMA in terms of localization performance and the corresponding parameter settings.