Integrated sensing and communication (ISAC) is a potential technology of the sixth-generation (6G) mobile communication system, which enables communication base station (BS) with sensing capability. However, the performance of single-BS sensing is limited, which can be overcome by multi-BS cooperative sensing. There are three types of multi-BS cooperative sensing, including cooperative active sensing, cooperative passive sensing, and cooperative active and passive sensing, where the multi-BS cooperative passive sensing has the advantages of low hardware modification cost and large sensing coverage. However, multi-BS cooperative passive sensing faces the challenges of synchronization offset mitigation and sensing information fusion. To address these challenges, a non-line of sight (NLoS) and line of sight (LoS) signal cross-correlation (NLCC) method is proposed to mitigate carrier frequency offset (CFO) and time offset (TO). Besides, a symbol-level fusion method of multi-BS sensing information is proposed. The discrete samplings of echo signals from multiple BSs are matched independently and coherently accumulated to improve sensing accuracy. Moreover, a low-complexity joint angle-of-arrival (AoA) and angle-of-departure (AoD) estimation method is proposed to reduce the computational complexity. Simulation results show that symbol-level multi-BS cooperative passive sensing scheme has an order of magnitude higher sensing accuracy than single-BS passive sensing. This work provides a reference for the research on multi-BS cooperative passive sensing.

Intelligent machines (IMs), including industrial machines, unmanned aerial vehicles (UAVs), and unmanned vehicles, etc., could perform effective cooperation in complex environment when they form IM network. The efficient environment sensing and communication are crucial for IM network, enabling the real-time and stable control of IMs. With the emergence of integrated sensing and communication (ISAC) technology, IM network is empowered with ubiquitous sensing capabilities, which is helpful in improving the efficiency of communication and sensing with the mutual benefit of them. However, the massive amount of sensing information brings challenges for the processing, storage and application of sensing information. In this article, ISAC driven digital twin (DT) is proposed for IM network, and the architecture and enabling technologies are revealed. ISAC driven DT structurally stores the sensing information, which is further applied to optimize communication, networking and control schemes of IMs, promoting the widespread applications of IMs.

With the support of integrated sensing and communication (ISAC) technology, mobile communication system will integrate the function of wireless sensing, thereby facilitating new intelligent applications such as smart city and intelligent transportation. Due to the limited sensing accuracy and sensing range of single base station (BS), multi-BS cooperative sensing can be applied to realize high-accurate, long-range and continuous sensing, exploiting the specific advantages of large-scale networked mobile communication system. This article proposes a cooperative sensing method suitable to mobile communication systems, which applies symbol-level sensing information fusion to estimate the location and velocity of target. With the demodulation symbols obtained from the echo signals of multiple BSs, the phase features contained in the demodulation symbols are used in the fusion procedure, which realizes cooperative sensing with the synchronization level of mobile communication system. Compared with the signal-level fusion in the area of distributed aperture coherence-synthetic radars, the requirement of synchronization is much lower. When signal-to-noise ratio (SNR) is −5 dB, it is evaluated that symbol-level multi-BS cooperative sensing effectively improves the accuracy of distance and velocity estimation of target. Compared with single-BS sensing, the accuracy of distance and velocity estimation is improved by 40% and 72%, respectively. Compared with data-level multi-BS cooperative sensing based on maximum likelihood (ML) estimation, the accuracy of location and velocity estimation is improved by 12% and 63%, respectively. This work may provide a guideline for the design of multi-BS cooperative sensing system to exploit the widely deployed networked mobile communication system.

The future mobile communication systems will support intelligent applications such as Internet of Vehicles (IoV) and Extended Reality (XR). Integrated Sensing and Communication (ISAC) is regarded as one of the key technologies satisfying the high data rate communication and highly accurate sensing for these intelligent applications in future mobile communication systems. With the explosive growth of wireless devices and services, the shortage of spectrum resources leads to the fragmentation of available frequency bands for ISAC systems, which degrades sensing performance. Facing the above challenges, this paper proposes a Carrier Aggregation (CA)-based ISAC signal aggregating high and low-frequency bands to improve the sensing performance, where the CA-based ISAC signal can use four different aggregated pilot structures for sensing. Then, an ISAC signal processing algorithm with Compressed Sensing (CS) is proposed and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is used to solve the reconfiguration convex optimization problem. Finally, the Cram'er-Rao Lower Bounds (CRLBs) are derived for the CA-based ISAC signal. Simulation results show that CA efficiently improves the accuracy of range and velocity estimation.

Wireless communication with the enormous demands of sensing ability have given rise to the integrated passive sensing and communication (IPSAC) technology. The main challenge of IPSAC is how to achieve high sensing and communication performance by integrating the passive sensing and communication demodulation. In this paper, we propose an integrated sensing and communication (ISAC) signal processing optimization scheme by jointly processing the pilot and data signals. To solve the optimization problem, we propose an ISAC signal processing algorithm based on iterative optimization, which alternates the passive sensing and channel reconstruction to realize target sensing. However, the hyper-parameter configuration of the iterative optimization algorithm influences the performance of target detection and communication demodulation. Recognizing this fact, we propose a model-driven ISAC network (ISAC-NET) that adopts the block-by-block signal processing method to improve the communication and sensing performance. The proposed ISAC-NET obtains suitable hyper-parameters by deep learning to guarantee the performance and convergence of communication and sensing signal processing. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for the IPSAC systems.

Perceptive mobile network (PMN) is an emerging concept for next-generation wireless networks capable of conducting integrated sensing and communication (ISAC). A major challenge for realizing high performance sensing in PMNs is how to deal with spatially separated asynchronous transceivers. Asynchronicity results in timing offsets (TOs) and carrier frequency offsets (CFOs), which further cause ambiguity in ranging and velocity sensing. Most existing algorithms mitigate TOs and CFOs based on the line-of-sight (LOS) propagation path between sensing transceivers. However, LOS paths may not exist in realistic scenarios. In this paper, we propose a cooperation based joint active and passive sensing scheme for the non-LOS (NLOS) scenarios having asynchronous transceivers. This scheme relies on the cross-correlation cooperative sensing (CCCS) algorithm, which regards active sensing as a reference and mitigates TOs and CFOs by correlating active and passive sensing information. Another major challenge for realizing high performance sensing in PMNs is how to realize high accuracy angle-of-arrival (AoA) estimation with low complexity. Correspondingly, we propose a low complexity AoA algorithm based on cooperative sensing, which comprises coarse AoA estimation and fine AoA estimation. Analytical and numerical simulation results verify the performance advantages of the proposed CCCS algorithm and the low complexity AoA estimation algorithm.

Integrated sensing and communication (ISAC) base stations can provide communication and wide range sensing for vehicles via downlink (DL) transmission, thus enhancing the driving safety. One major challenge for achieving the high performance of communication and sensing is how to deal with the DL mutual interference among adjacent ISAC base stations, which includes not only communication-related interference but also sensing-related interference. In this article, we establish a DL mutual interference model of adjacent ISAC base stations, and analyze the relationship between the communication and sensing mutual interference channels. To mitigate the mutual interference, we propose a collaborative precoding design for adjacent base stations under the transmit power constraint and constant modulus constraint. To solve the nonconvex collaborative precoding design problem, we first relax the problem into a convex programming by omitting the rank constraint, and propose a joint optimization algorithm to solve the problem. To reduce computational complexity, We further propose a sequential optimization algorithm, which divides the collaborative precoding design problem into four subproblems and finds the optimum via a gradient descent algorithm. Finally, we evaluate the collaborative precoding design algorithms by considering sensing and communication performance via numerical results.

The high mobility of unmanned aerial vehicles (UAVs) enables them to be used in various civilian fields, such as rescue and cargo transport. Path-following is a crucial way to perform these tasks while sensing and collision avoidance are essential for safe flight. In this paper, we investigate how to efficiently and accurately achieve path-following, obstacle sensing and avoidance subtasks, as well as their conflict-free fusion scheduling. Firstly, a high precision deep reinforcement learning (DRL)-based UAV formation path-following model is developed, and the reward function with adaptive weights is designed from the perspective of distance and velocity errors. Then, we use integrated sensing and communication (ISAC) signals to detect the obstacle and derive the Cramér-Rao lower bound (CRLB) for obstacle sensing by information-level fusion, based on which we propose the variable formation enhanced obstacle position estimation (VFEO) algorithm. In addition, an online obstacle avoidance scheme without pretraining is designed to solve the sparse reward. Finally, with the aid of null space based (NSB) behavioral method, we present a hierarchical subtasks fusion strategy. Simulation results demonstrate the effectiveness and superiority of the subtask algorithms and the hierarchical fusion strategy.

Driven by the intelligent applications of sixth-generation (6G) mobile communication systems such as smart city and autonomous driving, which connect the physical and cyber space, the integrated sensing and communication (ISAC) brings a revolutionary change to the base stations (BSs) of 6G by integrating radar sensing and communication in the same hardware and wireless resource. However, with the requirements of long-range and accurate sensing in the applications of smart city and autonomous driving, the ISAC enabled single BS still has a limitation in the sensing range and accuracy. With the networked infrastructures of mobile communication systems, multi-BS cooperative sensing is a natural choice satisfying the requirement of long-range and accurate sensing. In this article, the framework of multi-BS cooperative sensing is proposed, breaking through the limitation of single-BS sensing. The enabling technologies, including unified ISAC performance metrics, ISAC signal design and optimization, interference management, cooperative sensing algorithms, are introduced in details. The performance evaluation results are provided to verify the effectiveness of multi-BS cooperative sensing schemes. With ISAC enabled multi-BS cooperative sensing (ISAC-MCS), the intelligent infrastructures connecting physical and cyber space can be established, ushering the era of 6G promoting the intelligence of everything.

Due to the high mobility and easy deployment, unmanned aerial vehicles (UAVs) have attracted much attention in the field of wireless communication and positioning. To meet the challenges of lack of infrastructure coverage, uncertain sensor position and large amount of sensing data collection in the wireless sensor network (WSN), this article presents an efficient joint data collection and sensor positioning scheme for WSN supported by multiple UAVs. Specifically, a UAV is set as the main UAV to collect data, and other UAVs are used as auxiliary UAVs for sensor positioning using time difference of arrival (TDoA). A mixed-integer nonconvex optimization problem with uncertain sensor position is established. The goal is to minimize the average positioning error of all sensors by jointly optimizing the UAV trajectories, sensor transmission schedule, and positioning observation points (POPs). To solve this optimization model, the original problem is decomposed into two subproblems based on the path discrete method. First, the block coordinate descent (BCD) and successive convex approximation (SCA) techniques are applied to iteratively optimize the trajectory of the main UAV and the sensor transmission schedule, to maximize the minimum amount of data uploaded by the sensor. Then, based on the trajectory of the main UAV, a particle swarm optimization (PSO)-based algorithm is designed to optimize the POPs of UAVs. Finally, the spline curve is applied to generate the trajectories of auxiliary UAVs. The simulation results show that the proposed scheme can meet the requirements of data collection and has a good positioning performance.

Integrated sensing and communication (ISAC), with sensing and communication sharing the same wireless resources and hardware, has the advantages of high spectrum efficiency and low hardware cost, which is regarded as one of the key technologies of the fifth generation advanced (5G-A) and sixth generation (6G) mobile communication systems. ISAC has the potential to be applied in the intelligent applications requiring both communication and high accurate sensing capabilities. The fundamental challenges of ISAC system are the ISAC signal design and ISAC signal processing. However, the existing ISAC signal has low anti-noise capability. And the existing ISAC signal processing algorithms have the disadvantages of quantization errors and high complexity, resulting in large energy consumption. In this paper, phase coding is applied in ISAC signal design to improve the anti-noise performance of ISAC signal. Then, the effect of phase coding method on improving the sensing accuracy is analyzed. In order to improve the sensing accuracy with low-complexity algorithm, the iterative ISAC signal processing methods are proposed. The proposed methods improve the sensing accuracy with low computational complexity, realizing energy efficient ISAC signal processing. Taking the scenarios of short distance and long distance sensing into account, the iterative two-dimensional (2D) fast Fourier transform (FFT) and iterative cyclic cross-correlation (CC) methods are proposed, respectively, realizing high sensing accuracy and low computational complexity. Finally, the feasibility of the proposed ISAC signal processing methods are verified by simulation results.

This paper studies the sensing base station (SBS) that has great potential to improve the safety of vehicles and pedestrians on roads. SBS can detect the targets on the road with communication signals using the integrated sensing and communication (ISAC) technique. Compared with vehicle-mounted radar, SBS has a better sensing field due to its higher deployment position, which can help solve the problem of sensing blind areas. In this paper, key technologies of SBS are studied, including the beamforming algorithm, beam scanning scheme, and interference cancellation algorithm. To transmit and receive ISAC signals simultaneously, a double-coupling antenna array is applied. The free detection beam and directional communication beam are proposed for joint communication and sensing to meet the requirements of beamwidth and pointing directions. The joint time-space-frequency domain division multiple access algorithm is proposed to cancel the interference of SBS, including multiuser interference and duplex interference between sensing and communication. Finally, the sensing and communication performance of SBS under the industrial scientific medical power limitation is analyzed and simulated. Simulation results show that the communication rate of SBS can reach over 100 Mbps and the range of sensing and communication can reach about 500 m.

5th Generation (5G) mobile communication systems operating at around 28 GHz have the potential to be applied to simultaneous localization and mapping (SLAM). Most existing 5G SLAM studies estimate environment as many point targets, instead of extended targets. In this paper, we focus on the performance analysis of 5G SLAM for multiple extended targets. To evaluate the mapping performance of multiple extended targets, a new mapping error metric, named extended targets generalized optimal sub-pattern assignment (ET-GOPSA), is proposed in this paper. Compared with the existing metrics, ET-GOPSA not only considers the accuracy error of target estimation, the cost of missing detection, the cost of false detection, but also the cost of matching the estimated point with the extended target. To evaluate the performance of 5G signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB, 5G signal sensing can barely meet to meet the requirements of SLAM for multiple extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23 GHz, and the antenna size of 32.

Roadside units (RSUs) can help vehicles sense the traffic environment, so as to improve traffic safety. Since the sensing capability of single RSU is limited, we propose a multiple RSUs cooperative radar sensing network (RSU-CRSN) with signal-level fusion technique. Spatial registration is an essential prerequisite and foundation for RSU-CRSN with signal-level fusion. In this paper, we present an adjustable beam enabled spatial registration algorithm (AB-SRA) that makes the sensing area of each RSU coincide by adjusting the sensing beam width of RSU. To adjust the width of sensing beam flexibly, a beamwidth adjustable beamforming algorithm (BABA) is proposed in this paper. Simulation results show that the performance of AB-SRA is close to perfect spatial registration.

In this paper, a novel multiple access method is proposed and evaluated for integrated sensing and communication (ISAC) enabled UAV ad hoc network, in which the UAVs can perform sensing and communicating simultaneously. With integrated signal, a novel spatial division method is proposed based on a multi-beam framework with tunable analog antenna arrays for ISAC system. With the implementation of such spatial division method, we design a new time-frequency resource allocation scheme by dividing the integrated signal into Radar (R) mode and Radar Communication (RC) mode. Moreover, according to the packet arrival rate, to make full use of spectrum resources, a novel procedure to assign channels is proposed. The performance of medium access method is analyzed by using Markov model. Simulation results shows that the multiple access method proposed in this paper has improved the throughput of UAV nodes with the assistance of sensing information.

The collaborative sensing of multiple Integrated sensing and communication (ISAC) base stations is one of the important technologies to achieve intelligent transportation. Interference elimination between ISAC base stations is the prerequisite for realizing collaborative sensing. In this paper, we focus on the mutual interference elimination problem in collaborative sensing of multiple ISAC base stations that can communicate and radar sense simultaneously by transmitting ISAC signals. We establish a mutual interference model of multiple ISAC base stations, which consists of communication and radar sensing related interference. Moreover, we propose a joint optimization algorithm (JOA) to solve the collaborative precoding problem with total power constraint (TPC) and perantenna power constraint (PPC). The optimal precoding design can be obtained by using JOA to set appropriate tradeoff coefficient between sensing and communication performance. The proposed collaborative precoding design algorithm is evaluated by considering sensing and communication performance via numerical results. The complexity of JOA for collaborative precoding under TPC and PPC is also compared and simulated in this paper.

The unmanned aerial vehicle (UAV) needs to sense the environment to ensure safe flight, and the sensing accuracy and communication delay performance are two important indicators of safe flight. The strategy of using integrated sensing and communication (ISAC) technology to improve the sensing and communication performance is proposed in this paper. On the one hand, the extended kalman filter (EKF) algorithm is adopted to achieve the fusion of communication location information and sensing information to improve the accuracy of target sensing. On the other hand, a Identification Friend or Foe (IFF) method based on ISAC is proposed to reduce communication delay. Compared with the traditional IFF method, the integrated technology used for IFF can realize the radar sensing and communication interrogating functions in parallel, greatly shortening the sensing time. Simulation results show that using ISAC technology, the sensing performance of UAV has been greatly improved, the communication delay can be reduced by up to 50%, the accuracy of target sensing can be improved by 24.2 % when communication location information and radar sensing information have the same sensing accuracy.