AllSpark: Enabling Long-Range Backscatter for Vehicle-to-Infrastructure Communication

AllSpark: Enabling Long-Range Backscatter for Vehicle-to-Infrastructure Communication
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DOI:
10.1109/jiot.2022.3197596
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发表时间:
2022-12
影响因子:
10.6
通讯作者:
Xuan Wang;Xin Kou;Haoyu Li;Fuwei Wang;Dingyi Fang;Yunfei Ma;Xiaojiang Chen
Xuan Wang;Xin Kou;Haoyu Li;Fuwei Wang;Dingyi Fang;Yunfei Ma;Xiaojiang Chen
中科院分区:
计算机科学1区
文献类型:
--
作者:
Xuan Wang;Xin Kou;Haoyu Li;Fuwei Wang;Dingyi Fang;Yunfei Ma;Xiaojiang Chen

文献摘要

相似文献

远距离后向散射通信具有为车辆提供足够的时间和空间来检测交通信息的潜力,这是车辆到基础设施(V2 I)通信的有吸引力的解决方案。然而,现有的反向散射研究要么要求标签靠近载波源(环境反向散射),要么具有较差的接收器灵敏度(RFID),这使得满足V2 I通信的高范围要求具有挑战性。在这篇文章中,我们开发了AllSpark来研究远程通信的可行性,使反向散射能够应用于V2 I。具体来说,为了增加RSS以补偿反向散射系统中巨大的双路径损耗,我们首先重新设计标签的射频(RF)前端以放大入射信号,然后为标签和阅读器设计高增益定向天线。其次,我们提出了EC-Net,这是一种端到端的解调器,可以选择性地增强信号特征(去噪),并调整到较低的分类(解调)误差,以最大限度地提高解调精度。此外,我们采用卷积编码的标签数据和使用EC-Net的输出概率设计一个有效的软解码器,以抵抗偶然的干扰和噪声,提高通信的鲁棒性。我们的原型和户外实验验证了AllSpark的有效性,它可以提供700 m的通信范围。即使在移动场景中,它也能实现600米的通信范围,证明AllSpark有潜力应用于自动驾驶和低空飞行的无人机,以检测交通状况。
Long-range backscatter communication has the potential to provide enough time and space for vehicles to detect traffic information, which is an attractive solution for Vehicle-to-Infrastructure (V2I) communication. However, existing backscatter studies either require the tag to be close to the carrier source (ambient backscatter) or have poor receiver sensitivity (RFID), making it challenging to satisfy the high range requirements of V2I communication. In this article, we develop AllSpark to investigate the feasibility of long-range communication enabling backscatter to be applied to V2I. Specifically, to increase RSS to compensate for the enormous dual-path loss in backscatter systems, we first redesign the tag’s radio frequency (RF) front-end to amplify the incident signals, and then we design high-gain directional antennas for the tag and reader. Second, we present EC-Net, an end-to-end demodulator that selectively enhances signal features (denoising) and is tuned to lower classification (demodulation) error to maximize demodulation accuracy. Furthermore, we adopt convolutional encoding for tag data and use the output probability of EC-Net to design an effective soft decoder to resist occasional interference and noise, improving communication robustness. Our prototype and experiments outdoors verify the effectiveness of AllSpark which can provide a communication range of 700 m. Even in a moving scene, it can achieve a communication range of 600 m, demonstrating that AllSpark has the potential to be applied for autonomous driving and low-flying drones to detect traffic conditions.