ID 原文 译文
17705 无线通信中的抗干扰技术对通信的稳定性和安全性都具有重要意义，干扰识别作为抗干扰技术的重要环节一直是研究的热点。 The anti-interference technology in wireless communication is great significance to the stability andsecurity of communication. As an important part of anti-interference technology, interference recognition is a research hotspot.
17706 该文提出一种基于奇异值分解与神经网络的干扰识别方法，该方法只计算信号矩阵的奇异值即完成特征提取，与传统方法相比节省了多个谱特性的计算量。 An interference recognition method based on singular value decomposition and neural networkis proposed. This method only calculates the singular value of the signal matrix as the feature. Compared withthe traditional method, it saves the computational complexity of multiple spectral features.
17707 仿真结果表明：基于奇异值分解与神经网络的干扰识别方法与传统方法相比在干信比为0 dB左右的条件下识别准确率有10%～25%的提高。 The simulation results show that the recognition accuracy based on singular value decomposition and neural network is10%~25% higher than the traditional method under the condition of jamming-signal ratio at 0 dB.
17708 针对5G网络切片环境下由于业务请求的随机性和未知性导致的资源分配不合理从而引起的系统高时延问题，该文提出了一种基于迁移演员-评论家(A-C)学习的服务功能链(SFC)部署算法(TACA)。 To solve the problem of high system delay caused by unreasonable resource allocation because ofrandomness and unpredictability of service requests in 5G network slicing, this paper proposes a deploymentscheme of Service Function Chain (SFC) based on Transfer Actor-Critic (A-C) Algorithm (TACA).
17709 首先，该算法建立基于虚拟网络功能放置、计算资源、链路带宽资源和前传网络资源联合分配的端到端时延最小化模型，并将其转化为离散时间马尔可夫决策过程(MDP)。 Firstly, anend-to-end delay minimization model is built based on Virtual Network Function (VNF) placement, and jointallocation of computing resources, link resources and fronthaul bandwidth resources, then the model istransformed into a discrete-time Markov Decision Process (MDP).
17710 而后，在该MDP中采用A-C学习算法与环境进行不断交互动态调整SFC部署策略，优化端到端时延。 Next, A-C learning algorithm is adopted inthe MDP to adjust dynamically SFC deployment scheme by interacting with environment, so as to optimizethe end-to-end delay.
17711 进一步，为了实现并加速该A-C算法在其他相似目标任务中(如业务请求到达率普遍更高)的收敛过程，采用迁移A-C学习算法实现利用源任务学习的SFC部署知识快速寻找目标任务中的部署策略。 Furthermore, in order to realize and accelerate the convergence of the A-C algorithm insimilar target tasks (such as the arrival rate of service requests is generally higher), the transfer A-C algorithmis adopted to utilize the SFC deployment knowledge learned from source tasks to find quickly the deploymentstrategy in target tasks.
17712 仿真结果表明，该文所提算法能够减小且稳定SFC业务数据包的队列积压，优化系统端到端时延，并提高资源利用率。 Simulation results show that the proposed algorithm can reduce and stabilize thequeuing length of SFC packets, optimize the system end-to-end delay, and improve resource utilization.
17713 在军用和民用领域，Morse电报一直是一种重要的短波通信手段，但目前的自动译码算法仍然存在准确率低、无法适应低信噪比和不稳定的信号等问题。 In the military and civilian fields, the Morse telegraph is always as an important means of short-wave communication, but the current automatic decoding algorithms still have problems such as low accuracy, inability to adapt to low signal-to-noise ratio and unstable signals.
17714 该文引入深度学习方法构建了一个Morse码自动识别系统，神经网络模型由卷积神经网络、双向长短时记忆网络和连接时序分类层组成，结构简单，且能够实现端到端的训练。 A deep learning method is introduced toconstruct a Morse code automatic recognition system. The neural network model consists of convolutionalneural network, bidirectional long short-term memory network and connectionist temporal classification layer.The structure is simple and can implement end-to-end training.