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.

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.

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.

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.

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 per-antenna 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.

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.