Bi-directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL-C model with Rayleigh fading distribution

In this work, a deep learning (DL)-based massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL-C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi-directional long short-term memory (Bi-LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi-LSTM-based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function-trained Bi-LSTM-based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi-LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short-term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.     

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security. Traditional approaches are based on vision or wearables, which only work under the line of sight (LOS) or require the targets to carry dedicated devices. As human bodies and their movements have influences on WiFi propagation, this paper proposes the recognition of human activities by analyzing the channel state information (CSI) from the WiFi physical layer. The method requires only the commodity: WiFi transmitters and receivers that can operate through a wall, under LOS and non-line of sight (NLOS), while the targets are not required to carry dedicated devices. After collecting CSI, the discrete wavelet transform is applied to reduce the noise, followed by outlier detection based on the local outlier factor to extract the activity segment. Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration. Experiments in through-the-wall environments achieve recognition accuracy >95% for six common activities, such as standing up, squatting down, walking, running, jumping, and falling, outperforming existing work in this field.     

New method of text representation model based on neural network

Method of text representation model was proposed to extract word-embedding from text feature.Firstly,the word-embedding of the dual word-embedding list based on dictionary index and the corresponding part of speech index was created.Then,feature vectors was obtained further from these extracted word-embeddings by using Bi-LSTM recurrent neural network.Finally,the sentence vectors were processed by mean-pooling layer and text categorization was classified by softmax layer.The training effects and extraction performance of the combination model of Bi-LSTM and double word-embedding neural network were verified.The experimental results show that this model not only performs well in dealing with the high-quality text feature vector and the expression sequence,but also significantly outperforms other three kinds of neural networks,which includes LSTM,LSTM+context window and Bi-LSTM.     

融入多特征的汉韩双语自动句对齐方法

为解决汉韩双语平行语料库资源匮乏以及传统句对齐算法面向跨语系语言准确率较低的问题,提出了融合特征的汉韩双语句对齐方法.首先将Bi-LSTM融入孪生神经网络构建句对齐模型,用以分别提取汉语和韩语句子的特征并进行对齐.之后基于语料的特点提取句对齐特征融入输入层.通过与传统Bi-LSTM和不同特征组合的孪生Bi-LSTM的对...     

基于双向LSTM的图书分类系统的设计与实现

进入信息化时代,图书数量、更新换代速度飞速提高,加大了图书馆管理人员的工作难度,并给数字化建设带来不便。为此,文中设计并实现了一种图书分类系统,将双向LSTM模型引入到图书分类任务中,并通过在模型的嵌入层使用字符向量化编码的方法避免了中文分词的困难性。系统包含前端交互、书籍信息录入、特征识别、分类器管理等功能模块,实现了图书馆文献建设的自动化管理。实验结果表明,不管是针对于传统的图书情报管理还是针对于图书馆数字化平台的建设,使用该设计实现的图书分类系统能够有效提高相关人员的工作效率。     

基于Bi-LSTM的维吾尔语人称代词指代消解

针对维吾尔语人称代词指代现象,提出利用双向长短时记忆网络（Bi-directional long short term memory,Bi-LSTM）的深度学习机制进行基于深层语义信息的维吾尔语人称代词指代消解.首先将富含语义和句法信息的word embedding向量作为Bi-LSTM的输入,挖掘维吾尔语隐含的上下文语义层面特征;其次对维吾尔语人称代词指代现象进行探索,提取针对人称代词指代研究的24个hand-crafted特征;然后利用多层感知器（multilayer perception,MLP）融合Bi-LSTM学习到的上下文语义层面特征与hand-crafted特征;最后使用融合的两类特征训练softmax分类器完成维吾尔语人称代词指代消解任务.实验结果表明,充分利用两类特征的优势,维吾尔语人称代词指代消解的F1值达到76.86%.实验验证了Bi-LSTM与单向LSTM、浅层机器学习算法的SVM和ANN相比更具备挖掘隐含上下文深层语义信息的能力,而hand-crafted层面特征的引入,则有效提高指代消解性能.     

基于双流特征融合的FMCW雷达人体连续动作识别

本文提出了一种基于双流特征融合的FMCW雷达人体连续动作识别方法。首先对人体动作雷达回波信号进行预处理得到距离时间域图与微多普勒时频谱图,之后分别对两个不同维度的图像进行主成分分析提取对应特征并选取相同时间段的主成分分析结果进行融合得到双流融合特征,最后将双流融合特征输入到Bi-LSTM网络中训练与测试,网络对每个时间段的输入特征产生与之对应的动作类别输出从而实现连续人体动作识别。实验结果表明,当采用双流融合特征作为Bi-LSTM网络的输入时平均识别准确率要高于只采用距离时间特征或微多普勒特征作为网络输入时的平均识别准确率。     

结合改进Bi-LSTM和CNN的文本情感分析

针对目前用于文本情感分析神经网络非常缺乏的问题,提出了一种级联RNN的体系结构.该体系结构首先将RNN放在全局平均池化层上,用于捕获与CNN之间的长期依赖关系,然后通过GloVe嵌入方法对词向量进行处理,最终作为输入数据,进行训练.该方法与Twitter语料库中的基线模型相比,实验表现出更好的情感分类效果,该方法在Tw...