To exploit the spectrum reuse potential and enhance the cooperative sensing ability of the cooperative sensing unmanned aerial vehicle network (CSUN), we propose a novel joint sensing-communication (JSC) CSUN that can simultaneously conduct downward-looking radar sensing and sensing data fusion communication with the unified spectrum and transceiver by adopting the beam sharing scheme. To achieve the beam sharing scheme, we design a novel antenna array equipped on each UAV, which can generate a sensing beam and a communication beam orthogonally for downward detection and data-fusion communication, respectively. To quantify the cooperative sensing performance of CSUN, we propose and formulate a novel upper-bound average cooperative sensing area (UB-ACSA) as the cooperative sensing performance metric, based on deriving the closed-form expressions for the average mutual sensing interference of CSUN and the communication capacity constraints. Finally, extensive simulations are conducted to validate the theoretical results. We also demonstrate that the cooperative sensing performance of the JSC CSUN can improve by 66.3 percent compared with the conventional CSUN in terms of UB-ACSA.

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar-sensing signals to improve the communication reliability and radar-sensing accuracy, we propose a novel code-division orthogonal frequency-division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancelation-based signal processing technique that obtains code-division multiplex gain, which is compatible with the prevalent orthogonal frequency-division multiplex (OFDM) communication system. To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system and the error propagation performance. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.

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.

With the widespread application of millimeter-wave (mmWave) radar technology in modern vehicles, vehicle-to-vehicle radar interference becomes a significant concern in exploiting the benefits of mmWave radar. This letter studies the radar mutual interference among vehicles on a practical bi-directional multi-lane road. Particularly, the fluctuation of the target radar cross-section (RCS) is modeled by Swerling I model and Chi-square model. Closed-form expression for successful ranging probability that works as the radar ranging performance metric is obtained by applying the stochastic geometry. Compared with constant RCS model in previous literature, the proposed performance metric based on random RCS models can provide more precise and intuitive results. The simulation results show that the increasing ranging distance and intensity of vehicles lead to a sharp decrease of the successful ranging probability.

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.

The joint communication and sensing (JCAS) technique has drawn great attention due to its high spectrum efficiency by using the same transmit signal for both communication and sensing. Exploiting the correlation between the uplink (UL) channel and the sensing results, we propose a sensing-aided Kalman filter (SAKF)-based channel state information (CSI) estimation method for UL JCAS, which exploits the angle-of-arrival (AoA) estimation to improve the CSI estimation accuracy. A Kalman filter (KF)-based CSI enhancement method is proposed to refine the least-square CSI estimation by exploiting the estimated AoA as the prior information. Simulation results show that the bit error rates (BER) of UL communication using the proposed SAKF-based CSI estimation method approach those using the minimum mean square error (MMSE) method, while at significantly reduced complexity.