Integrated sensing and communication (ISAC) is expected to play a crucial role in the sixth-generation (6G) mobile communication systems, offering potential applications in the scenarios of intelligent transportation, smart factories, etc. The performance of radar sensing in ISAC systems is closely related to the characteristics of radar sensing and communication channels. Therefore, ISAC channel modeling serves as a fundamental cornerstone for evaluating and optimizing ISAC systems. This article provides a comprehensive survey on the ISAC channel modeling methods. Furthermore, the methods of target radar cross section (RCS) modeling and clutter RCS modeling are summarized. Finally, we discuss the future research trends related to ISAC channel modeling in various scenarios.

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance.

To realize higher-level autonomous driving and advanced transportation applications, the introduction of the integrated sensing and communication (ISAC) technique in vehicular networks is indispensable. Different from the existing works, this paper investigates the power allocation problem for onboard ISAC systems of vehicles, during the vehicle-to-infrastructure communication, vehicle-to-vehicle communication and sensing progress, in case of the time-varying communication channel gains, the time-varying impulse responses of sensed targets, and the stochastic traffic. Note that both the inter-beam interference of a single vehicle and the inter-vehicle interference are important considerations. Specifically, we formulate a stochastic programming problem, which optimizes the sensing performance, subject to constraints on the network stability, power limits and quality-of-service requirements. Leveraging the Lyapunov optimization technique, this stochastic programming problem is transformed into a single-time slot non-convex problem. Taking advantages of genetic algorithm and particle swarm optimization (PSO), a hybrid meta-heuristic algorithm is designed to solve the non-convex problem. Typically, we improve the traditional PSO to balance the global search ability and local search ability of particles. Finally, a dynamic power allocation strategy is proposed. The theoretical analysis and simulation results show that this strategy achieves a communication performance-sensing performance tradeoff of [$ {\mathrm {O(}}1/V{\mathrm {)}} $,$ {\mathrm {O(}}V{\mathrm {)}} $] with$ V $being a control parameter.

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

As one of the promising technologies of the future 6G network, Integrated Sensing and Communication (ISAC) is able to provide tremendous benefits such as performance improvement and cost reduction through integrating the two systems as a whole. The use of ISAC technology will introduce new sensing abilities and enhance information processing capabilities at base stations, and makes the interaction between base stations and vehicles more frequent, which brings huge benefits to connected automated vehicles(CAV). If the BS only transmits the single beam at a given period, the sensing functions will not be guaranteed as there are blind zone and other issues. Therefore, we propose a multi-beam model for the ISAC downlink transmission. Furthermore, considering the uneven distribution of resources among different users, an ISAC multi-beam power allocation algorithm is proposed. Such convex optimization problem is solved using CVX toolbox and optimal solutions are obtained. The simulation results show that compared with the traditional average power allocation and water filling algorithms, proposed algorithm will improve the total communication rate for multi-user scenario, and provide a satisfied sensing accuracy of less than 1m sensing error.

Flying ad hoc networks (FANETs) play a crucial role in numerous military and civil applications since it shortens mission duration and enhances coverage significantly compared with a single unmanned aerial vehicle (UAV). Whereas, designing an energy-efficient FANETs routing protocol with a high packet delivery rate (PDR) and low delay is challenging owing to the dynamic topology changes. In this article, we propose a topology-aware resilient routing strategy based on adaptive$Q$-learning (TARRAQ) to accurately capture topology changes with low overhead and make routing decisions in a distributed and autonomous way. First, we analyze the dynamic behavior of UAVs nodes via the queuing theory, and then the closed-form solutions of neighbors’ change rate (NCR) and neighbors’ change interarrival time (NCIT) distribution are derived. Based on the real-time NCR and NCIT, a resilient sensing interval (SI) is determined by defining the expected sensing delay of network events. Besides, we also present an adaptive$Q$-learning approach that enables UAVs to make distributed, autonomous, and adaptive routing decisions, where the above SI ensures that the action space can be updated in time with low cost. The simulation results verify the accuracy of the topology dynamic analysis model, and also prove that our TARRAQ outperforms the$Q$-learning-based topology-aware routing (QTAR), mobility prediction-based virtual routing (MPVR), and greedy perimeter stateless routing based on energy-efficient hello (EE-Hello) in terms of 25.23%, 20.24%, and 13.73% lower overhead, 9.41%, 14.77%, and 16.70% higher PDR, and 5.12%, 15.65%, and 11.31% lower energy consumption, respectively.

Neighbor discovery (ND) is a key initial step of network configuration and prerequisite of vehicularad hocnetwork (VANET). However, the convergence efficiency of ND is facing the requirements of multivehicle fast networking of VANET with frequent topology changes. This article proposes the gossip-based information dissemination and sensing information-assisted ND with multipacket reception (GSIM-ND) algorithm for VANET. The GSIM-ND algorithm leverages efficient gossip-based information dissemination in the case of multipacket reception (MPR). Besides, through the multitarget detection function of multiple sensors installed in roadside unit (RSU), RSU can sense the distribution of vehicles and help vehicles to obtain the distribution of their neighbors. Thus, the GSIM-ND algorithm leverages the dissemination of sensing information as well. The expected number of discovered neighbors within a given period is theoretically derived and used as the critical metric to evaluate the performance of the GSIM-ND algorithm. The expected bounds of the number of time slots when a given number of neighbors needs to be discovered are derived as well. The simulation results verify the correctness of theoretical derivation. It is discovered that GSIM-ND algorithm proposed in this article can always reach the short-term convergence quickly. Moreover, the GSIM-ND algorithm is more efficient and stable compared with the completely random algorithm (CRA), scan-based algorithm (SBA), and gossip-based algorithm. The convergence time of the GSIM-ND algorithm is 40%–90% lower than that of these existing algorithms for both low density and high density networks. Thus, GSIM-ND can improve the efficiency of ND algorithm.