高精度抗噪RNN:一种鲁棒无学习的波束形成方法

IF 8.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Cong Lin;Zhihui Jiang;Jingyu Cong;Lilan Zou
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引用次数: 0

摘要

递归神经网络(rnn)以其精度高、鲁棒性强而著称。然而,基于rnn的阵列信号波束形成方案的采用仍处于起步阶段,因为rnn对噪声非常敏感,不易克服环境噪声对方案的影响。为了解决这些限制,本研究提出了用于阵列信号波束形成的动态集成增强神经网络(DIENN),该网络包含误差积分反馈机制。该机制增强了模型的鲁棒性和抗噪声性,使其在动态噪声环境下仍能保持稳定的性能。与最先进的SOTA方法相比,该模型在波束形成任务中具有更高的稳定性,并且在三种干扰条件下提供了良好的结果,而其他比较算法都失败了。在时变扰动情况下获得的残差精度为10^{-15}$。通过实验数据验证了该模型的可行性。据我们所知,这是开发用于阵列信号处理的零复位RNN的第一项工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RNN With High Precision and Noise Immunity: A Robust and Learning-Free Method for Beamforming
Recurrent neural networks (RNNs), recognized for their high accuracy and strong robustness. However, the adoption of RNN-based solutions for array signal beamforming is still in its infancy, as RNNs are very sensitive to noise and cannot easily overcome the impact of environmental noise on the solution. To address these limitations, this study proposes the dynamic integrated enhanced neural network (DIENN) for array signal beamforming, which incorporates an error integral feedback mechanism. This mechanism enhances the robustness and noise immunity of the model, enabling it to maintain stable performance under dynamic noise environments. Compared with state-of-the-art (SOTA) methods, the proposed model has higher stability in beamforming tasks while providing excellent results under three interference conditions where the other algorithms of the comparison failed. The residual accuracy achieved in the case of time-varying disturbance was $10^{-15}$ . The feasibility of the model was verified by applying it to experimental data. To our knowledge, this is the first work to develop a zero-reset RNN for array signal processing.
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来源期刊
IEEE Internet of Things Journal
IEEE Internet of Things Journal Computer Science-Information Systems
CiteScore
17.60
自引率
13.20%
发文量
1982
期刊介绍: The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.
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