Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jun-Han Wang;He He;Kosuke Tamura;Shun Kojima;Jaesang Cha;Chang-Jun Ahn
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引用次数: 0

Abstract

With the development of autonomous vehicle operation, vehicle-to-vehicle (V2V) communication plays an increasingly important role. However, in high-speed mobile environments, the channel has fast time-varying, which significantly decreases the property of channel estimation. On the other hand, the frame structure of the IEEE 802.11p standard contains a few number of pilots and a large pilot interval, which is not sufficient to track the rapidly changing channel environment accurately. In recent years, deep learning has been widely used for channel estimation. However, these methods typically perform poorly in high-speed mobility scenarios or have excessively high computational complexity. To alleviate such issues, this study proposes a channel estimation method by combining the sparrow search algorithm (SSA) and gated recurrent unit (GRU). In addition, this paper adds the attention mechanism to GRU to improve the robustness of the model. The computer simulation results confirm that, compared to traditional schemes, the proposed estimator can achieve a lower bit error rate (BER) and normalized mean squared error (NMSE). At the same time, the computational complexity of the algorithm has been reduced to some extent, allowing the estimator to complete the channel estimation faster. This study provides a useful reference for optimizing neural networks and thus improving the performance of channel estimators.
基于门循环单元的车对车通信信道估计
随着自动驾驶汽车的发展,车对车(V2V)通信的作用越来越重要。然而,在高速移动环境下,信道具有快速时变特性,这大大降低了信道估计的性能。另一方面,IEEE 802.11p标准的帧结构包含少量导频和较大的导频间隔,不足以准确跟踪快速变化的信道环境。近年来,深度学习在信道估计中得到了广泛的应用。然而,这些方法通常在高速移动场景中表现不佳或具有过高的计算复杂度。为了解决这一问题,本研究提出了一种结合麻雀搜索算法(SSA)和门控循环单元(GRU)的信道估计方法。此外,本文还在GRU中加入了注意机制,提高了模型的鲁棒性。计算机仿真结果表明,与传统的估计方法相比,该估计方法具有较低的误码率和归一化均方误差(NMSE)。同时,在一定程度上降低了算法的计算复杂度,使得估计器能够更快地完成信道估计。该研究为优化神经网络从而提高信道估计器的性能提供了有益的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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