The Cellular Vehicle-to-Everything (C-V2X) networks can support direct vehicle-to-vehicle V2V communications via sidelink/PC5 interface without cellular infrastructure support. The sensing-based Semi-Persistent Scheduling (SPS) scheme is to allow vehicles to autonomously reserve and select radio resources. However, the common sensing nature of distributed SPS algorithm gives rise to that C-V2X distributed communications can be challenged by the resource selection collisions, especially in aperiodic traffic, which resulting in low reliability. In this paper, we propose a short term sensing based resource selection (STS-RS) scheme to reduce packet collisions due to resource contention, where a short term sensing duration is configured at the beginning of the resource unit right before resource selection, and whether the packet is ultimately transmitted on the selected resource depends on the sensing result. Furthermore, analysis models of the performance for the proposed STS-RS scheme and SPS scheme defined in C-V2X mode 4 are investigated. Finally, simulations and numerical results show that the STS-RS scheme significantly reduces the packet collisions and increase the C-V2X direct communication performance compared to SPS scheme.

The outliers remove, classification of effective measurements, and the weighted optimization method of corresponding measurement are the main factors that affects the positioning accuracy based on range-based multi-target tracking in wireless sensor networks (WSNs). In this paper, we develop an improved weighted least-square algorithm based on enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian (NNB), flexible naive Bayesian (FNB), the homologous model of FNB (FNBROT), support vector machine (SVM), k-means, fuzzy c-means (FCM), possibilistic c-means (PCM), possibilistic fuzzy c-means (PFCM), and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce root mean squared error (RMSE) of the position in compared with Extended Kalman Filter (EKF). In addition, the proposed algorithm has better robustness against large localization and tracking errors.

In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.

MmWave communication has emerged as a promising technology to fulfill requirements of gigabit-level data rates, so as to support the exchange of large amounts of sensor data in vehicle-to-vehicle (V2V) communications. However, inter-vehicle line-of-sight (LOS) blockages may significantly deteriorate the link quality and lead to data packet loss. Addationally, it is difficult to coordinate directional transmissions to broadcast massive sensor data to nearby vehicles timely for enhanced environmental awareness. To overcome these challenges, this article proposes a blockage avoidance based multi-hop sensor data dissemination (BAMD) scheme in a centralized manner. First, we propose a mmWave LOS link availability identification method based on the geometric relation among vehicles. Second, the problem of sensor data dissemination among all vehicles is formulated aiming at minimizing the overall transmission delay. To find an approximate solution, a novel transmission utility function based multi-hop data dissemination algorithm is designed jointly considering the distance between vehicles, packet queue, the number of LOS neighbors and concurrent transmissions. By theoretical analysis and extensive simulation, it is indicated that the proposed scheme can achieve a reliable and fast sensor data dissemination. In high-density case, BAMD can improve reliability by 20 $\%$ and reduce transmission delay by 82 $\%$ compared with baselines.

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization root mean square error (RMSE) by about 6 dB compared with the maximum likelihood esimation (MLE)-based benchmark method.

The New Radio Vehicle-to-Everything (NR-V2X) technology investigated by the Third Generation Partnership Project (3GPP) explores the high reliability requirement to support message dissemination by vehicle-to-vehicle (V2V) distributed communications for safety applications via a direct communication interface. However, the natural commonality of distributed resource selection may lead to severe resource collisions in a dense high mobility environment. In this paper, we propose a UE-scheduling resource allocation scheme to reduce distributed resource contentions, where vehicles are clustered autonomously based on orthogonal resource sets detection and the cluster size is determined by the cluster-joining threshold. In addition, the algorithms of clustering maintenance are designed to improve the stability of clusters by periodically checking the status of the resource sets and the movement of vehicles. Furthermore, a detailed frame structure design is described, and theoretical performance analysis models of the proposed scheme in different traffic scenarios are investigated. Finally, simulations and numerical results demonstrate that the proposed scheme can reduce packet collisions and increase the packet delivery ratio compared to the conventional distributed scheme, and outperforms some existing schemes in cluster stability.