Signal area estimation based on deep learning

In many practical application scenarios, radio communication signals are commonly represented as a spectrogram, which represents the signal strength measured at multiple discrete time instants and frequency points within a specific time interval and frequency band, respectively. In the context of spectrum occupancy measurements, the notion of Signal Area (SA) is defined as the rectangular region in the time–frequency domain where a signal is assumed to be present. Signal Area Estimation (SAE) is an important functionality in spectrum-aware wireless systems where spectrum usage monitoring is required. However, the conventional approaches to SAE have a limited estimation accuracy, in particular at low SNR. In this work, a novel technique for SAE is proposed using Deep Learning based on Artificial Neural Network (DL-ANN) for enhanced extraction of SA information from radio spectrograms. The performance of the proposed DL-ANN method is evaluated both with software simulations and hardware experiments, and the results are compared with several conventional methods from the literature, showing significant performance improvements. A key feature of the proposed method is the improvement in the SAE accuracy compared to other existing methods (in particular in the low SNR regime) and the capability to extract the location of the detected SAs automatically. Overall, the proposed technique is a promising solution for the automatic processing of radio spectrograms in spectrum-aware wireless systems.     

Efficient detection of refugees and migrants in Turkey using convolutional neural network

At the start of the Syrian Civil War in 2011, NGOs played a big part in giving refugees access to aid and distributing that aid so that people could go to school, get a job, or get medical care. Within the last few years, when tensions rose between Syrian refugees and the Turkish community, many non-governmental organizations switched their attention to fostering community among refugees in Turkey. Over the past two decades, family displacement has become a big problem in various countries due to a rise in the frequency with which natural catastrophes, military conflicts, and terrorist strikes occur. It poses severe difficulties for governing bodies and the organizations that oversee them. This research aims to identify and track refugees in surveillance zones by utilizing artificial intelligence. Refugees are vulnerable to acts of nature and human aggression, which makes their random relocation or encampments challenging to manage. To overcome these challenges, a convolutional neural network deep learning model has been proposed to identify and track refugees in surveillance zones. The proposed solution is integrated with Internet of Things (IoT) technology by equipping the system with IoT sensors to capture real-time data on the location and movements of refugees. This combination of AI and IoT has the potential to improve the efficiency and effectiveness of refugee management efforts. The suggested solution uses a convolutional neural network deep learning model, which can quickly identify a refugee’s face. To assist the government in locating a specific refugee, the system simultaneously connects with the refugees and requests that they regularly update their location. The system alerts security to identify the missing immigrant since the refugee does not update their whereabouts. Without human intervention, the deep learning algorithm makes it simple to recognize immigrants and keep an eye on them.     

Relay selection scheme based on deep reinforcement learning in wireless sensor networks

Cooperative communication technology has realized the enhancement in the wireless communication system’s spectrum utilization rate without resorting to any additional equipment; additionally, it ensures system reliability in transmission, increasingly becoming a research focus within the sphere of wireless sensor networks (WSNs). Since the selection of relay is crucial to cooperative communication technology, this paper proposes two different relay selection schemes subject to deep reinforcement learning (DRL), in response to the issues in WSNs with relay selection in cooperative communications, which can be summarized as the Deep-Q-Network Based Relay Selection Scheme (DQN-RSS), as well as the Proximal Policy Optimization Based Relay Selection Scheme (PPO-RSS); it further compared the commonly used Q-learning relay selection scheme (Q-RSS) with random relay selection scheme. First, the cooperative communication process in WSNs is modeled as a Markov decision process, and DRL algorithm is trained in accordance with the outage probability, as well as mutual information (MI). Under the condition of unknown instantaneous channel state information (CSI), the best relay is adaptively selected from multiple candidate relays. Thereafter, in view of the slow convergence speed of Q-RSS in high-dimensional state space, the DRL algorithm is used to accelerate the convergence. In particular, we employ DRL algorithm to deal with high-dimensional state space while speeding up learning. The experimental results reveal that under the same conditions, the random relay selection scheme always has the worst performance. And compared to Q-RSS, the two relay selection schemes designed in this paper greatly reduce the number of iterations and speed up the convergence speed, thereby reducing the computational complexity and overhead of the source node selecting the best relay strategy. In addition, the two relay selection schemes designed and raised in this paper are featured by lower-level outage probability with lower-level energy consumption and larger system capacity. In particular, PPO-RSS has higher reliability and practicability.     

基于深度卷积网络的多传感器信号故障诊断方法研究

针对传统故障诊断方法中多传感器数据融合技术难度大、特征提取困难等问题,提出了一种基于深度卷积网络的多传感器信号故障诊断方法,通过构建测量数据帧进行卷积计算实现多通道数据的自然融合,利用深度网络结构实现高层特征的自动提取和分类,从而高效地实现了故障分类诊断;经分别采用小规模数据集REF和大规模故障数据集BI02进行实验验证,均取得了较高的故障识别准确率,具有很强的工程应用价值。     

基于深度学习的船舶辐射噪声识别研究

为了改善船舶辐射噪声识别系统的性能,进一步提高船舶辐射噪声识别的正确率,该文提出采用一种基于深度学习的船舶辐射噪声识别方法。该方法首先提取了船舶辐射噪声的频谱、梅尔倒谱系数等特征,将提取特征后的图像样本分别用于训练卷积神经网络和深度置信网络,再对船舶辐射噪声进行识别。通过文中所给实例,将深度学习和支持向量机两种识别方法的性能进行比较,得出深度学习方法可以有效地提高船舶辐射噪声识别正确率的初步结论。     

基于深度学习的ELM实时识别研究

基于深度学习的方法,在HL-2A装置上开发出了一套边缘局域模(ELM)实时识别算法。算法使用5200次放电数据(约24.19万数据切片)进行学习,得到一个深度为22层的卷积神经网络。为衡量算法的识别能力,识别了HL-2A装置自2009年实现稳定ELMyH模放电以来所有历史数据(约26000次放电数据),共识别出1665次H模放电,其中误识别35次,误报率为2.10%。在实际的1634次H模放电中,漏识别4次,漏识别率为0.24%。该误报率和漏报率可以满足ELM实时识别的精度要求。识别算法在实时控制环境下,对单个时间点的平均计算时间为0.46ms,可以满足实时控制的计算速度要求。     

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.     

UAV path design with connectivity constraint based on deep reinforcement learning

Cellular networks are expected to communicate effectively with unmanned aerial vehicles (UAVs) and support various applications. However, existing cellular networks are primarily designed to cover users on the ground; thus, coverage holes in the sky will exist. In this paper, we investigate the problem of path design for cellular-connected UAVs, taking into account the interruption performance throughout the UAV mission to minimize the completion time. Two types of connectivity constraints requirements are assumed to be available. The first is defined as the maximum continuous time interval that the UAV loses connection with base stations (BSs) below a predefined threshold. For the second, we consider the sum outage of UAV is limited during the entire UAV mission. The UAV is tasked with flying from a starting location to a final destination while minimization the mission time, satisfying the two constraints, separately. The formulated path design problem which involves continues variables and a dynamic radio environment, is not convex and thus is extremely difficult to solve directly. To tackle this challenge, a deep reinforcement learning (DRL) based trajectory design algorithm is proposed, where the Dueling Double Deep Q Network(Dueling DDQN) with multi-steps learning method is applied. Simulation results demonstrate the effectiveness of the proposed DRL algorithm and achieve a trade-off between the trajectory length of the UAV and connection quality.     

基于深度神经网络的水声信号恢复方法研究*

针对干扰或噪声环境下水声目标信号难以获取的问题,该文提出研究基于深度神经网络的自适应水声被动信号波形恢复方法。在单阵元情况下,该方法提取对数功率谱特征作为输入,采用深度神经网络回归模型自适应学习目标信号的自身特征,输出降噪后的对数功率谱特征并还原时域波形。在多阵元情况下,提出阵列深度神经网络降噪方法,将部分或全部阵元特征拼接为长向量作为输入,从而利用空域信息。为全面利用阵列丰富的时频域信息,该文提出一种两阶段特征融合深度神经网络,在第一阶段将阵列分为若干个子阵,将每个子阵分别用阵列深度神经网络进行处理,在第二阶段将第一阶段的各子阵处理结果与阵列接收信号同时输入一个深度神经网络进行融合学习。实验表明,所提出的单阵元和两阶段融合深度神经网络取得了显著优于常规波束形成的恢复结果,能够准确估计目标信号波形和功率并显著提高输出信噪比。     

采用性别相关的深度神经网络及非负矩阵分解模型用于单通道语音增强

为了从带噪信号中得到纯净的语音信号,提出了一种采用性别相关模型的单通道语音增强算法。具体而言,在训练阶段,分别训练了与性别相关的深度神经网络-非负矩阵分解模型用于估计非负矩阵分解中的权重参数;在测试阶段,提出了一种基于非负矩阵分解和组稀疏惩罚的算法用于判断测试语音中说话人的性别信息,然后再采用对应的模型估计权重,并结合已训练好的字典进行语音增强。实验结果表明所提算法在噪声抑制量及语音质量上,均优于一些基于非负矩阵分解的算法和基于深度神经网络的算法。     

应用深度学习识别法兰螺栓连接状态

针对常规诊断方法对螺栓的连接状态识别效果差、鲁棒性和抗噪性弱等问题,提出了基于深度学习理论的螺栓检测新方法.首先以4种预紧力状态下的法兰螺栓结构产生的声发射信号为研究对象,借助于自适应噪声的完整集成经验模态分解理论以及梅尔频率倒谱系数特征提取方式,实现了声发射信号的自适应消噪和最优模态函数分量组的选取,提取到了可以较好...     

一种深度学习的立体阵波达方向估计方法

针对常规波束形成主瓣宽且目标分辨能力低的问题,提出一种基于深度卷积神经网络的波达方向估计方法。算法使用常规波束形成计算二维空间功率谱,将预处理后的空间功率谱图输入深度卷积神经网络。该文利用神经网络学习解卷积映射关系,输出主瓣宽度更窄的空间功率谱图,从而实现高分辨率二维波达方向估计。该算法对阵列结构没有限制,适用于立体阵。仿真结果表明该文方法在不同目标个数、快拍数及信噪比参数下均能准确估计目标方向。该文方法目标分辨能力优于常规波束形成方法。在低快拍情况下,目标方向估计误差低于自适应波束形成方法。     

非高斯环境下的深度学习脉冲信号去噪与重构*

侧扫声呐进行沉底小目标探测时,底混响是主要背景干扰。底混响通常是一种非平稳、非高斯的带限噪声,它使得白噪声条件下的滤波器性能受到限制。在混响背景下常利用自回归模型对接收信号进预行白化处理,但对于实际侧扫声呐应用,白化后直接匹配滤波的处理效果不甚理想。针对此问题,在自回归模型预白化的基础上,提出采用一种次最佳检测与多分辨二分奇异值分解相结合的改进方法。该方法首先对接收信号进行分段处理,利用改进Burg算法估计每段数据自回归模型的系数及阶数;然后构造白化滤波器对分段数据预白化,并对白化后的数据进行多分辨二分奇异值分解;最后应用ostu方法对原始声图和处理后的声图进行目标检测。仿真与实验结果表明,该方法明显提高了信混比,改善了侧扫声呐沉底静态小目标的成图质量,有利于后期实现基于图像的目标自动检测。     

Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

The key principle of physical layer security (PLS) is to permit the secure transmission of confidential data using efficient signal-processing techniques. Also, deep learning (DL) has emerged as a viable option to address various security concerns and enhance the performance of conventional PLS techniques in wireless networks. DL is a strong data exploration technique which can be used to learn normal and abnormal behavior of 5G and beyond wireless networks in an insecure channel paradigm. Also, since DL techniques can successfully predict future new instances by learning from existing ones, they can successfully predict new attacks, which frequently involve mutations of earlier attacks. Thus, motivated by the benefits of DL and PLS, this survey provides a comprehensive review that overviews how DL-based PLS techniques can be employed for solving various security concerns in 5G and beyond networks. The survey begins with an overview of physical layer threats and security concerns in 5G and beyond networks. Then, we present a detailed analysis of various DL and deep reinforcement learning (DRL) techniques that are applicable to PLS applications. We present the specific use-cases of PLS design for each type of technique, including attack detection, physical layer authentication (PLA), and other PLS techniques. Then, we present an in-depth overview of the key areas of PLS where DL can be used to enhance the security of wireless networks, such as automatic modulation classification (AMC), secure beamforming, PLA, etc. Performance evaluation metrics for DL-based PLS design are subsequently covered. Finally, we provide insights to the readers about various challenges and future research trends in the design of DL-based PLS for terrestrial communications in 5G and beyond networks.     

Resource optimization for UAV-assisted mobile edge computing system based on deep reinforcement learning

Computational efficiency is a direction worth considering in moving edge computing (MEC) systems. However, the computational efficiency of UAV-assisted MEC systems is rarely studied. In this paper, we maximize the computational efficiency of the MEC network by optimizing offloading decisions, UAV flight paths, and allocating users’ charging and offloading time reasonably. The method of deep reinforcement learning is used to optimize the resources of UAV-assisted MEC system in complex urban environment, and the user’s computation-intensive tasks are offloaded to the UAV-mounted MEC server, so that the overloaded tasks in the whole system can be alleviated. We study and design a framework algorithm that can quickly adapt to task offload decision making and resource allocation under changing wireless channel conditions in complex urban environments. The optimal offloading decisions from state space to action space is generated through deep reinforcement learning, and then the user’s own charging time and offloading time are rationally allocated to maximize the weighted sum computation rate. Finally, combined with the radio map to optimize the UAC trajectory to improve the overall weighted sum computation rate of the system. Simulation results show that the proposed DRL+TO framework algorithm can significantly improve the weighted sum computation rate of the whole MEC system and save time. It can be seen that the MEC system resource optimization scheme proposed in this paper is feasible and has better performance than other benchmark schemes.     

DRL-M4MR: An intelligent multicast routing approach based on DQN deep reinforcement learning in SDN

Traditional multicast routing methods have some problems in constructing a multicast tree. These problems include limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multiobjective optimization problem, and DRL-M4MR, an intelligent multicast routing algorithm based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in a software-defined network. First, combining the characteristics of SDN global network-aware information, the multicast tree state matrix, link bandwidth matrix, link delay matrix and link packet loss rate matrix are designed as the state space of the reinforcement learning agent to solve the problem in that the original method cannot make full use of network status information. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree in four cases. Third, single-step and final reward function forms are designed to guide the agent to make decisions to construct the optimal multicast tree. The double network architectures, dueling network architectures and prioritized experience replay are adopted to improve the learning efficiency and convergence of the agent. Finally, after the DRL-M4MR agent is trained, the SDN controller installs the multicast flow entries by reversely traversing the multicast tree to the SDN switches to implement intelligent multicast routing. The experimental results show that, compared with existing algorithms, the multicast tree constructed by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Code and DRL model are available at https://github.com/GuetYe/DRL-M4MR.     

NIRS feature extraction based on deep auto-encoder neural network

As a secondary analysis method, Near Infrared Spectroscopy (NIRS) needs an effective feature extraction method to improve the model performance. Deep auto-encoder (DAE) can build up an adaptive multilayer encoder network to transform the high-dimensional data into a low-dimensional code with both linear and nonlinear feature combinations. To evaluate its capability, we experimented on the spectra data obtained from different categories of cigarette with the method of DAE, and compared with the principal component analysis (PCA). The results showed that the DAE can extract more nonlinear features to characterize cigarette quality. In addition, the DAE also got the linear distribution of cigarette quality by its nonlinear transformation of features. Finally, we employed k-Nearest Neighbor (kNN) to classify different categories of cigarette with the features extracted by the linear transformation methods as PCA and wavelet transform-principal component analysis (WT-PCA), and the nonlinear transformation methods as DAE and isometric mapping (ISOMAP). The results showed that the pattern recognition mode built on features extracted by DAE was provided with more validity.     

A multi-channel and multi-user dynamic spectrum access algorithm based on deep reinforcement learning in Cognitive Vehicular Networks with sensing error

In this paper, a spectrum access problem is proposed to improve the spectrum access rates of secondary vehicles in Cognitive Vehicular Networks, where the channel capacity mathematic model is established under the conditions of spectrum sensing errors rates and the dynamic occupancy spectrum rates. Meanwhile, an improved Q-learning method is proposed to conform the dynamic communication under the different conditions of the reward functions. In this function, a Deep Q Network method with a modified reward function (IDQN) is proposed to deal with the situation of multi-vehicle in multi-channel. In order to verify the effectiveness of the IDQN method, the Myopic method, the improved Q-learning method, and the traditional DQN method are compared on Python. The simulation results shown that the proposed IDQN method not only outperforms the compared methods in terms of channel utilization and channel capacity but also improves the ability that the vehicle adapts to the dynamic communication environment.     

基于远场信息和卷积神经网络的波前重构方法

探测波前相位信息是实现自适应光学波前补偿的关键,使用卷积神经网络（CNN）代替波前传感器进行波前重构,系统简单易于实现,同时重构过程不依赖迭代运算,快速实时。为准确提取远场中的波前特征,CNN需要事先使用大量样本进行训练。研究中根据4～30阶大气湍流泽尼克像差系数与其远场强度的对应关系,仿真制作样本数据集,训练CNN从输入的一帧远场图像中预测出畸变波前的泽尼克像差系数,重构原始波前。验证结果表明,该方法能快速实时地还原出波前相位信息,重构波前较原始波前具有极高的波面吻合度和较小的残差剩余量,有望实现实际自适应光学系统中的闭环校正。