文献类型：期刊文献

英文题名：RNN with High Precision and Noise Immunity:A Robust and Learning-Free Method for Beamforming

作者：Lin, Cong[1]; Jiang, Zhihui[1]; Cong, Jingyu[2]; Zou, Lilan[1]

机构：[1] Guangdong Ocean University, School of Electronics and Information Engineering, Zhanjiang, 524088, China; [2] Sun Yat-sen University, School of Electronics and Communication Engineering, Shenzhen, 518107, China

年份：2025

外文期刊名：IEEE Internet of Things Journal

收录：EI(收录号：20250417766104)、Scopus(收录号：2-s2.0-85215952115)

语种：英文

外文关键词：Recurrent neural networks - Signal modulation

外文摘要：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 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. ? 2014 IEEE.