The radio channel modeling and location system are the focus of many research fields. However, their performance is largely limited by line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Currently, the most popular LOS/NLOS identification algorithm is based on the channel impulse response (CIR) feature. The decision threshold is applied to identify between LOS and NLOS conditions on this basis. However, the features around the threshold are usually very similar, which leads to low LOS/NLOS accuracy. In this work, the feature extracted from the CIR is used to fit its distribution with the Gaussian mixture function. Then, a soft decision algorithm is used to classify the signal. In the experimental simulations, the algorithm is tested according to the measured data. The results reveal that by choosing different feature combinations, the algorithm effectively improves the accuracy of LOS and NLOS identification.

Vehicular networks are becoming increasingly dense due to expanding wireless services and platooning has been regarded as a promising technology to improve road capacity and on-road safety. Constrained by limited resources, not all communication links in platoons can be allocated to the resources without suffering interference. To guarantee the quality of service, it is required to determine the set of served services at which the scale of demand exceeds the capability of the network. To increase the number of guaranteed services, the resource allocation has to be adjusted to adapt to the dynamic environment of the vehicular network. However, resource re-allocation results in additional costs, including signal overhead and latency. To increase the number of guaranteed services at a low-cost in a resource-limited vehicular network, we propose a time dynamic optimization method that constrains the network re-allocation rate. To decrease the computational complexity, the time dynamic optimization problem is converted into a deterministic optimization problem using the Lyapunov optimization theory. The simulation indicates that the analytical results do approximate the reality, and that the proposed scheme results in a higher number of guaranteed services as compared to the results of a similar algorithm.

The emergence of visible light communication technology has alleviated the pressure of wireless network communication and provided new ways for vehicle networking technology. Therefore, it is necessary to analyze the performance changes in visible light communication in vehicle networking communication. This article mainly considers the impact of vehicle speed on the quality of visible light communication technology services during the driving duration. Firstly, the impact of vehicle speed on signal-to-noise ratio changes was analyzed. Secondly, the channel states in different V2I/V2V scenarios were analyzed. Finally, the transformation in signal-to-noise ratio in V2I/V2V scenarios was analyzed through simulation.

Conventional peak-to-average power ratio (PAPR) reduction metrics including bit error rate (BER) and complementary cumulative distribution function (CCDF) can hardly reveal the overall effectiveness of a distortion-type PAPR technique. To remedy this drawback, a new overall performance index, BER at various power consumption of a given HPA and channel noise level, is proposed. Furthermore, a novel triangular companding (TC) technique is also introduced to improve the system performance. Simulations demonstrate that the TC scheme outperforms other schemes in case of the proposed overall performance index and the conventional performance metrics for PAPR reduction.

Information topology graph has been extensively used in modeling network management, where it is necessary to continually optimize the management of the dynamic network. However, frequent reconfiguration may lead to an increased overhead and a degraded latency. Since the mobility in a vehicular network is restricted by the road, there is an internal relatively stable part embedded in the entire dynamic network. Thus, in this study, this characteristic is first used to reduce reconfiguration, where the maximum stable subgraph is extracted and configuration only needs to be reconsidered for the rest of the network. However, there are three main challenges in the modeling and solving procedure, namely the evaluation of stability of part of the network, the assurance of the coherence in the set of extracted vehicles and the huge computation complexity of the subgraph enumeration. Therefore, in this study, first, the vehicular network is modeled by constructing the time-evolving information topology graph, in which a subgraph similarity is constructed to quantify the stability of part of network. Second, a maximum stable subgraph extraction optimization problem is presented, where the coherence constraint of vehicles is proposed. Finally, a gradual contraction extraction algorithm is proposed to solve the optimization problem with a low complexity. The simulation results reveal that the proposed algorithm can extract a stable subgraph with an approximate maximum size, and has a higher adaptability than the compared ones. Furthermore, it was found that the application of the proposed algorithm in resource allocation can effectively reduce the reconfiguration.