Yao Junliang, Wang Ze, Zhang Chunli, Hui Hui
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel). 2024 Aug 5;24(15):5061. doi: 10.3390/s24155061.
Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.
车与万物(V2X)通信被视为推动智能交通未来发展的关键因素,这需要在高速移动场景中实现高质量通信和车辆信息的快速感知。然而,高速移动会使无线信道迅速变化,这就要求车辆与路边单元(RSU)之间频繁进行信道估计和信道反馈,从而导致通信开销增加。同时,车辆的高机动性会导致通信波束频繁切换和失准,因此RSU必须具备更好的波束预测和跟踪能力。为解决这一问题,本文提出了一种用于感知辅助下行链路(DL)通信的自适应帧结构设计方案。该方案的基本思路是分析车辆移动过程中的通信模型。此分析旨在建立符号错误率(SER)与以下两个关键因素之间的理论关系:车辆的起始位置和行驶距离。随后,该方案利用RSU检测到的车辆位置数据来计算DL通信的实时SER。根据DL通信的要求设置SER阈值。如果实时SER低于此阈值,则无需进行信道估计。这降低了信道估计的频率,并释放了帧结构中原本会被信道估计过程占用的时间和频率资源。本文详细介绍了上述方案中的自适应帧结构设计。此外,还分析了所提方法的性能,并与传统通信协议帧结构和基于波束预测的帧结构进行了比较。仿真结果表明,与传统通信协议帧结构相比,所提方法的通信吞吐量可提高多达6%,同时保持SER性能。