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A Rapid Planning Repair Method of Three-Dimensional Path for AUV 自动潜航器三维路径快速规划修复方法
Pub Date : 2024-04-18 DOI: 10.1007/s11036-024-02307-x
Changting Shi, Dongdong Tao, Haibo Liu, Jinlong Bai

In response to the local path planning issue encountered by Autonomous Underwater Vehicle (AUV) during autonomous navigation when facing sudden threats or obstacles, a rapid path planning repair solution based on the IRRT*-VSRP method is proposed in this paper. This method combines an enhanced RRT* algorithm with a threat-based variable step-size receding horizon predictive strategy, effectively reducing the search space in three-dimensional environments. Its notable features include rapid local path repair and generation, thereby improving the success rate and efficiency of planning. Simulation results demonstrate that the IRRT*-VSRP algorithm significantly reduces the time required for planning repair and enhances the directionality of tree expansion, rendering it suitable for complex underwater three-dimensional environments and enhancing the efficiency of AUV planning repair.

针对自主水下航行器(AUV)在自主导航过程中面临突发威胁或障碍物时遇到的局部路径规划问题,本文提出了一种基于 IRRT*-VSRP 方法的快速路径规划修复方案。该方法将增强型 RRT* 算法与基于威胁的可变步长后退地平线预测策略相结合,有效地缩小了三维环境下的搜索空间。其显著特点包括快速修复和生成局部路径,从而提高了规划的成功率和效率。仿真结果表明,IRRT*-VSRP 算法大大缩短了规划修复所需的时间,并增强了树扩展的方向性,使其适用于复杂的水下三维环境,提高了 AUV 规划修复的效率。
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引用次数: 0
Uplink Performance Analysis of Wireless Energy Harvesting-Enabled NOMA-based Networks 基于无线能量收集的 NOMA 网络的上行链路性能分析
Pub Date : 2024-04-18 DOI: 10.1007/s11036-024-02326-8
Dipen Bepari, Soumen Mondal, Prakash Pareek, Nishu Gupta

This article presents a performance analysis of wireless energy harvesting (WEH)-enabled sensor networks that extract energy from ambient radio frequency (RF) signals prior to uplink transmission. A time-switching (TS)-based protocol is utilized to alternate sensor nodes between energy harvesting (EH) and data transmission modes. Implementing the non-orthogonal multiple access (NOMA) technique aims to boost the sensor network’s performance regarding uplink sum rate and outage probability. To optimize resource allocation, we propose an unequal operating time frame (OTF) scheme that determines data transmission and energy harvesting intervals based on channel gain quality. Simulation results affirm the superiority of NOMA over orthogonal multiple access (OMA), with NOMA enabling higher sum rates by accommodating more signals within the same frequency band, though at the expense of slightly degraded outage performance.

本文对支持无线能量采集(WEH)的传感器网络进行了性能分析,该网络在上行链路传输之前从环境射频(RF)信号中提取能量。利用基于时间切换(TS)的协议在能量采集(EH)和数据传输模式之间交替使用传感器节点。采用非正交多址(NOMA)技术旨在提高传感器网络在上行链路总和速率和中断概率方面的性能。为了优化资源分配,我们提出了一种不等工作时间帧(OTF)方案,根据信道增益质量确定数据传输和能量收集间隔。仿真结果表明,NOMA 比正交多址接入(OMA)更具优势,NOMA 可在同一频段内容纳更多信号,从而实现更高的总和速率,但代价是中断性能略有下降。
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引用次数: 0
AI Assisted Energy Optimized Sustainable Model for Secured Routing in Mobile Wireless Sensor Network 移动无线传感器网络安全路由的人工智能辅助能源优化可持续模型
Pub Date : 2024-04-16 DOI: 10.1007/s11036-024-02327-7
Khalid Haseeb, Fahad F. Alruwaili, Atif Khan, Teg Alam, Abrar Wafa, Amjad R. Khan

With the rapid development of cognitive computing and the Internet of Things (IoT), sensing systems have produced a wide range of real-time communication applications. They use 5G/6G-enabled technologies to connect to the outside world to collect data and process different end-user requests. Wireless systems and artificial intelligence (AI) have led to significant development in the optimization process of network communication. Due to various constraints of wireless systems, many solutions have been presented to cope with routing and connectivity concerns. However, topology awareness and attaining management of quality of services are still demanding research challenges for sustainable development. This study proposes an AI-assisted routing model for mobile wireless sensor networks (MWSN) to optimize energy and detect communication link failures. Moreover, the proposed intelligent security approach increases the trustworthiness of the constraint devices on unpredictable routes. Firstly, it explores a genetic algorithm, a metaheuristic optimization technique to determine the feasible solutions, and based on independent metrics it generates an optimal set of routes. In the proposed model, the genetic algorithm provides a fault-tolerant solution for dynamic environments, specifically under unpredictable conditions. Second, new routes are established using dynamic decisions that satisfy the energy considerations. In the end, the proposed model performs regular auditing to detect malicious devices based on unexpected behavior. The proposed model is tested and it outperforms IMD-EACBR and AGRIC in terms of realistic performance metrics.

随着认知计算和物联网(IoT)的快速发展,传感系统产生了广泛的实时通信应用。它们利用支持 5G/6G 的技术与外界连接,收集数据并处理不同的终端用户请求。无线系统和人工智能(AI)在网络通信的优化过程中取得了重大发展。由于无线系统的各种限制,人们提出了许多解决方案来解决路由和连接问题。然而,拓扑感知和实现服务质量管理仍是可持续发展所面临的严峻研究挑战。本研究为移动无线传感器网络(MWSN)提出了一种人工智能辅助路由模型,以优化能源和检测通信链路故障。此外,所提出的智能安全方法提高了不可预测路由上约束设备的可信度。首先,它利用遗传算法这种元启发式优化技术来确定可行的解决方案,并根据独立指标生成一组最优路由。在所提出的模型中,遗传算法为动态环境,特别是不可预测条件下的动态环境提供了一种容错解决方案。其次,利用满足能源考虑的动态决策建立新的路线。最后,建议的模型会执行定期审核,根据意外行为检测恶意设备。对所提出的模型进行了测试,从实际性能指标来看,它优于 IMD-EACBR 和 AGRIC。
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引用次数: 0
RE-InCep-BT-:Resource-Efficient InCeptor Model for Brain Tumor Diagnostic Healthcare Applications in Computer Vision RE-InCep-BT-:计算机视觉中用于脑肿瘤诊断医疗应用的资源效率 InCeptor 模型
Pub Date : 2024-04-15 DOI: 10.1007/s11036-024-02320-0
Kamini Lamba, Shalli Rani, Muhammad Attique Khan, Mohammad Shabaz

The rising incidence of brain tumors in the medical field necessitates the development of precise and effective diagnostic tools to assist the medical experts especially neurosurgeons as well as radiologists in early diagnosis and treatment recommendations. This study introduces a unique resource-efficient inceptor model utilizing computer-vision techniques for diagnosing presence of abnormal tissues inside brain MRI scans. The proposed model utilizes strengths of the inception architecture and incorporate resource-efficient design principles for optimizing its performance for healthcare applications. The model has been trained on a distinct dataset with different sizes where it is further processed, trained and validated on InCeptor model. Features are extracted by transfer learning process namely InceptionV3 for leveraging prior knowledge learnt from imagenet which is further integrated with support vector machines for performing binary classification to have accurate and efficient outcomes for giving timely recommendation and treatment to patients suffering from such disorder. The architecture of the proposed model has been designed in such a way that model should be computationally efficient for making it suitable in healthcare especially for brain tumor diagnostic purpose with limited resources. Experimental results demonstrates accuracy of 98.31%, precision of 99.09%, recall of 98.91%, specificity of 95% and F1- Score of 99% over state of art techniques.

脑肿瘤在医学领域的发病率不断上升,因此有必要开发精确有效的诊断工具,以协助医学专家,尤其是神经外科医生和放射科医生进行早期诊断并提出治疗建议。本研究介绍了一种利用计算机视觉技术诊断脑部磁共振成像扫描中是否存在异常组织的独特的资源节约型感知器模型。所提出的模型利用了初始架构的优势,并结合了资源节约型设计原则,以优化其在医疗保健应用中的性能。该模型在不同大小的数据集上进行了训练,并在 InCeptor 模型上进行了进一步处理、训练和验证。通过迁移学习过程(即 InceptionV3)提取特征,以利用从图像网络中学到的先验知识,并进一步与支持向量机集成,执行二元分类,从而获得准确、高效的结果,为患有此类疾病的患者提供及时的建议和治疗。拟议模型的架构设计应具有计算效率,使其适用于医疗保健领域,尤其是资源有限的脑肿瘤诊断。实验结果表明,与现有技术相比,该模型的准确率为 98.31%,精确率为 99.09%,召回率为 98.91%,特异性为 95%,F1 分数为 99%。
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引用次数: 0
An Extended SDN Architecture for Video-on-Demand Caching 用于视频点播缓存的扩展 SDN 架构
Pub Date : 2024-04-13 DOI: 10.1007/s11036-024-02321-z
Wei-Kuo Chiang, Tsung-Ying Li

Owing to the variety of ways to view the Internet and the changes in user behavior on the Internet, network traffic has been explosively growing in recent years. Users can watch high-quality videos on the Internet; it is a critical issue to reduce network traffic and increase the user's quality of experience (QoE). Therefore, there have been in-network caching services that cache the content that had been fetched by the user in a proxy server. Meanwhile, the software-defined network (SDN) has been developed to implement the network function through the virtualization function. Programmers can implement customized network functions using the SDN architecture. In this paper, we proposed an Extended SDN Cache service architecture (ESC). The ESC decomposes the function of inspecting incoming traffic, making cache decisions, and caching content to three network entities. This design can reduce the load of a single network entity. To reduce the load of the SDN controller, we utilize an extended OpenFlow switch named the DPI (deep packet inspection) switch, which can inspect the incoming traffic. The ESC designed a mechanism that can cache the different parts of a video in distinct cache nodes. The distributed content storage mechanism can increase the cache capability and the system's flexibility. We use the M/M/1 queuing model to analyze the average queuing delay time and compare the ESC queuing delay time with the C-flow and the OpenCache. The numerical analysis results show that the ESC queuing delay is shorter than the other two.

由于上网方式的多样化和用户上网行为的变化,近年来网络流量呈爆炸式增长。用户可以在互联网上观看高质量的视频,如何减少网络流量并提高用户的体验质量(QoE)是一个关键问题。因此,出现了网内缓存服务,将用户获取的内容缓存在代理服务器中。同时,通过虚拟化功能实现网络功能的软件定义网络(SDN)也得到了发展。程序员可以利用 SDN 架构实现定制的网络功能。本文提出了一种扩展 SDN 缓存服务架构(ESC)。ESC 将检测传入流量、做出缓存决策和缓存内容的功能分解为三个网络实体。这种设计可以减少单个网络实体的负载。为了减轻 SDN 控制器的负载,我们使用了一个名为 DPI(深度数据包检测)的扩展 OpenFlow 交换机,它可以检测进入的流量。ESC 设计了一种机制,可将视频的不同部分缓存在不同的缓存节点中。分布式内容存储机制可以提高缓存能力和系统的灵活性。我们使用 M/M/1 队列模型分析平均队列延迟时间,并将 ESC 队列延迟时间与 C-flow 和 OpenCache 进行比较。数值分析结果表明,ESC 的排队延迟时间比其他两种都短。
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引用次数: 0
MPSO: An Optimization Algorithm for Task Offloading in Cloud-Edge Aggregated Computing Scenarios for Autonomous Driving MPSO:自动驾驶云边缘聚合计算场景中任务卸载的优化算法
Pub Date : 2024-04-05 DOI: 10.1007/s11036-024-02310-2
Xuanyan Liu, Rui Yan, Jung Yoon Kim, Xiaolong Xu

With the development of cloud computing and edge computing technologies, these technologies have come to play a crucial role in the field of autonomous driving. The autonomous driving sector faces unresolved issues, with one key problem being the handling of latency-sensitive applications within vehicles. Cloud computing and edge computing provide a solution by segmenting unresolved computing tasks and offloading them to different computing nodes, effectively addressing the challenges of high concurrency through distributed computing. While the academic literature addresses computation offloading issues, it often focuses on static scenarios and does not fully leverage the advantages of cloud computing and edge computing. To address these challenges, a multivariate particle swarm optimization (MPSO) algorithm tailored for the cloud-edge aggregated computing environment in the autonomous driving domain is proposed. The algorithm, grounded in real-world scenarios, considers factors that may impact computation latency, abstracts them into quantifiable attributes, and determines the priority of each task. Tasks are then assigned to optimal computing nodes to achieve a balance between computation time and waiting time, resulting in the shortest total average weighted computation latency time for all tasks. To validate the effectiveness of the algorithm, experiments were conducted using the self-designed CETO-Sim simulation platform. The algorithm’s results were compared with those of simulated annealing, traditional particle swarm optimization, purely local computation, and purely cloud-based computation. Additionally, comparisons with traditional algorithms were considered in terms of iteration count and result stability. The results indicate that the MPSO algorithm not only achieves optimal computation offloading strategies within specified time constraints when addressing computation offloading issues in the autonomous driving domain but also exhibits high stability. Furthermore, the algorithm determines the processing location for each computing task, demonstrating significant practical value.

随着云计算和边缘计算技术的发展,这些技术在自动驾驶领域发挥着至关重要的作用。自动驾驶领域面临着一些尚未解决的问题,其中一个关键问题是如何处理车内对延迟敏感的应用。云计算和边缘计算提供了一种解决方案,它们将未解决的计算任务分割并卸载到不同的计算节点,通过分布式计算有效解决了高并发性的挑战。虽然学术文献探讨了计算卸载问题,但往往侧重于静态场景,并没有充分利用云计算和边缘计算的优势。为了应对这些挑战,本文提出了一种为自动驾驶领域的云-边缘聚合计算环境量身定制的多变量粒子群优化(MPSO)算法。该算法以真实世界的场景为基础,考虑了可能影响计算延迟的因素,将其抽象为可量化的属性,并确定每个任务的优先级。然后将任务分配到最佳计算节点,以实现计算时间和等待时间之间的平衡,从而使所有任务的总平均加权计算延迟时间最短。为了验证该算法的有效性,我们使用自行设计的 CETO-Sim 仿真平台进行了实验。该算法的结果与模拟退火、传统粒子群优化、纯本地计算和纯云计算的结果进行了比较。此外,还考虑了与传统算法在迭代次数和结果稳定性方面的比较。结果表明,在解决自动驾驶领域的计算卸载问题时,MPSO 算法不仅能在规定时间内实现最优计算卸载策略,而且表现出很高的稳定性。此外,该算法还能确定每个计算任务的处理位置,具有重要的实用价值。
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引用次数: 0
Model-Free QoE-Aware Seamless Handoff in Heterogeneous Wireless Networks 异构无线网络中的无模型 QoE 感知无缝切换
Pub Date : 2024-04-04 DOI: 10.1007/s11036-024-02305-z
Kaustubh Ranjan Singh, Rashmi Chaudhry, Vinay Rishiwal, Mano Yadav

Next-generation wireless networks (NGWN) consist of the integration of various technologies, such as Mobile ad-hoc networks (MANET), Wi-Fi, WiMAX, and LTE which are connected to the internet. Switching off the nodes among networks with same or different technology is handled by mobile IP. The determination of hand-off is not solely reliant on received signal strength, as relying solely on this metric could result in unnecessary hand-offs. Various factors, such as power consumption in communication, delay, traffic load, and network bandwidth, also play crucial roles in ensuring successful transmission. This paper introduces a seamless hand-off technique based on Markov processes (S-MSH), which takes into account different network properties that impact the Quality of Experience (QoE) for mobile terminals (MT) during communication. The proposed approach focuses on creating a Markov Decision Process (MDP) model for the system, considering user traffic requirements. The Q-learning algorithm is applied to the model to predict whether a hand-off is beneficial. An integrated similarity index-based approach, termed S-MSH, has been introduced to expedite the convergence rate of MSH. Simulation and numerical results demonstrate that the proposed approach surpasses the performance of the Network Priority Multicriteria Vertical Handover Decision Algorithm (NPMH) and the Simple Additive Weighing Algorithm (SAW) in terms of total reward and the number of handoffs.

下一代无线网络(NGWN)由多种技术整合而成,如与互联网连接的移动 ad-hoc 网络(MANET)、Wi-Fi、WiMAX 和 LTE。采用相同或不同技术的网络之间的节点切换由移动 IP 处理。决定是否切换并不完全依赖于接收到的信号强度,因为仅仅依赖这一指标可能会导致不必要的切换。各种因素,如通信功耗、延迟、流量负载和网络带宽,也对确保成功传输起着至关重要的作用。本文介绍了一种基于马尔可夫过程(S-MSH)的无缝切换技术,它考虑到了通信过程中影响移动终端(MT)体验质量(QoE)的不同网络属性。所提出的方法侧重于为系统创建马尔可夫决策过程(MDP)模型,同时考虑用户流量需求。Q-learning 算法应用于该模型,以预测移交是否有益。为了加快 MSH 的收敛速度,引入了一种基于相似性指数的综合方法,称为 S-MSH。仿真和数值结果表明,所提出的方法在总回报和移交次数方面超过了网络优先级多标准垂直移交决策算法(NPMH)和简单加权算法(SAW)。
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引用次数: 0
Minimum cascade repair method for mobile network nodes failure under time–frequency feature fusion 时频特征融合下移动网络节点故障的最小级联修复方法
Pub Date : 2024-04-03 DOI: 10.1007/s11036-024-02312-0

Abstract

Aiming at the connectivity failure caused by the failure of mobile network nodes in complex application scenarios, a minimum cascade repair method for the failure of mobile network nodes under time–frequency feature fusion is proposed. The short-time energy and zero-crossing rate of time-domain features of mobile network nodes were extracted.The frequency domain features of network nodes are extracted by fully connected long and short time memory network. In the form of weighting and series, the time domain and frequency domain features are fused to obtain the time–frequency fusion features. The failure node is detected by using the adaptive mechanism of node clustering feature combined with time–frequency fusion feature. Judging whether the failed node is a cut point according to the rule; If the failed node is a cut point, the network needs to be repaired. Considering the impact of faulty nodes on network communication and connectivity in the real world, an innovative minimum cascade repair algorithm is introduced to establish communication links to repair the network topology without moving nodes, achieving optimization of repair rules. Calculate the energy consumption of the repair moving distance of the failed node, based on the optimized repair rules. If the energy consumption is less than or equal to the energy consumption of the communication radius of the neighboring node, the mobile network can be repaired statically by directly establishing a communication link between the neighboring nodes. Otherwise, the best candidate node is selected and its location to be moved is calculated to complete the mobile network mobile repair. Experimental results show that this method can effectively repair the network after node failure. This method reduces the energy loss of the repair node and improves the status indicator value of the mobile network.

摘要 针对复杂应用场景下移动网络节点故障导致的连接失效问题,提出了一种时频特征融合下的移动网络节点故障最小级联修复方法。提取移动网络节点时域特征的短时能量和零交叉率,通过全连接的长短时记忆网络提取网络节点的频域特征。通过加权和序列的形式,将时域和频域特征进行融合,得到时频融合特征。利用节点聚类特征与时频融合特征相结合的自适应机制来检测故障节点。根据规则判断故障节点是否为切点;如果故障节点为切点,则需要修复网络。考虑到现实世界中故障节点对网络通信和连通性的影响,引入创新的最小级联修复算法,在不移动节点的情况下建立通信链路修复网络拓扑,实现修复规则的优化。根据优化后的修复规则,计算故障节点修复移动距离的能耗。如果能耗小于或等于相邻节点通信半径的能耗,则可通过直接建立相邻节点间的通信链路,静态修复移动网络。否则,将选择最佳候选节点,并计算其需要移动的位置,完成移动网络的移动修复。实验结果表明,该方法能在节点故障后有效修复网络。该方法减少了修复节点的能量损耗,提高了移动网络的状态指示值。
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引用次数: 0
5G Integrated User Downlink Adaptive Transmission Scheme for Low Earth Orbit Satellite Internet Access Network 低地球轨道卫星互联网接入网络的 5G 综合用户下行链路自适应传输方案
Pub Date : 2024-04-03 DOI: 10.1007/s11036-024-02296-x
Dongdong Wang, Chenhua Sun, Xiujie Wang, Lizhe Liu, Bin Wang

After the low-earth orbit (LEO) satellite Internet has gone through the two stages of competing with the terrestrial network and supplementing the terrestrial network, it has begun to enter the third stage of constructing the satellite-ground integrated network with the terrestrial network to provide seamless global coverage. 5G New Radio (NR) is one of the core enabling technologies of the third stage of satellite Internet. This paper focuses on how to make full use of the power and bandwidth resources on the LEO satellite by using adaptive transmission scheme to maximize the throughput of the user downlink based on 5G NR. To solve the problem that the ultra-long propagation delay, outdated channel state information (CSI) and dynamic multi-scenario of LEO satellite will lead to the high implementation cost and greatly reduced performance when applied the 5G adaptive transmission scheme to LEO satellites, we optimized the adaptive transmission scheme of 5G NR based LEO satellite from multiple dimensions such as adaptive transmission process, signal to noise ratio (SNR) prediction and modulation and coding scheme (MCS) adaptive switching strategy. The simulation results show that compared with the fixed threshold switching strategy based adaptive transmission scheme, the proposed scheme can improve the average throughput of the system by 26.6% under the dynamic multi-scenario environment served by the LEO satellite.

低地轨道(LEO)卫星互联网在经历了与地面网络竞争和补充地面网络两个阶段后,开始进入第三阶段,即构建与地面网络无缝覆盖全球的星地一体化网络。5G 新无线电(NR)是卫星互联网第三阶段的核心使能技术之一。本文重点研究如何基于5G NR,采用自适应传输方案,充分利用低地轨道卫星上的功率和带宽资源,最大限度地提高用户下行链路的吞吐量。针对低地轨道卫星超长传播时延、信道状态信息(CSI)过时、多场景动态等特点,导致在低地轨道卫星上应用5G自适应传输方案时实施成本高、性能大打折扣的问题,我们从自适应传输过程、信噪比(SNR)预测、调制编码方案(MCS)自适应切换策略等多个维度对基于5G NR的低地轨道卫星自适应传输方案进行了优化。仿真结果表明,与基于固定门限切换策略的自适应传输方案相比,在低地轨道卫星服务的动态多场景环境下,所提出的方案可将系统的平均吞吐量提高26.6%。
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引用次数: 0
Single-Channel Speech Quality Enhancement in Mobile Networks Based on Generative Adversarial Networks 基于生成式对抗网络的移动网络中单信道语音质量增强技术
Pub Date : 2024-04-02 DOI: 10.1007/s11036-024-02300-4
Guifen Wu, Norbert Herencsar

A large amount of randomly generated noise in mobile networks leads to a lack of targeting and gaming processes in the speech enhancement process, and the enhancement process from the perspective of acoustic features alone suffers from major drawbacks. Propose a single-channel speech quality enhancement method based on generative adversarial networks in mobile networks. Explain the principle of generative adversarial network to realize single-channel speech quality enhancement in mobile networks and clarify its shortcomings. Design an improved Mel frequency cepstral coefficient extraction method to extract 12 base features as the enhancement basis. Use the relative average least squares loss instead of the traditional loss function to enhance the training efficiency, use the hybrid penalty term to enhance the generator's ability to generate single-channel speech, and optimize the discriminator through the multi-layer convolution and the addition of fully connected layers to enhance the speech quality enhancement ability of adversarial generative networks in various aspects, forming a relative average generative adversarial network (RaGAN) based on hybrid penalty term to realize single-channel speech quality enhancement processing. Through the experiment, when the discriminator is applied with the size of a 3*3 convolutional kernel, the best effect of speech quality enhancement is achieved in the mobile network. This method can complete the enhancement of single-channel speech quality in the mobile network, and the effect is significant, which can effectively reduce the noise in the original single-channel speech.

移动网络中大量随机产生的噪声导致语音增强过程缺乏针对性和博弈性,仅从声学特征角度出发的增强过程存在较大弊端。提出一种基于生成式对抗网络的移动网络单信道语音质量增强方法。解释生成式对抗网络在移动网络中实现单通道语音质量增强的原理,并阐明其缺点。设计一种改进的 Mel 频率倒频谱系数提取方法,提取 12 个基本特征作为增强基础。用相对平均最小二乘损失代替传统损失函数提高训练效率,用混合惩罚项提高生成器生成单通道语音的能力,通过多层卷积和增加全连接层优化鉴别器,多方面提高对抗生成网络的语音质量增强能力,形成基于混合惩罚项的相对平均对抗生成网络(RaGAN),实现单通道语音质量增强处理。通过实验,当判别器的大小为 3*3 卷积核时,移动网络中的语音质量增强效果最佳。该方法能完成移动网络中单信道语音质量的增强,且效果显著,能有效降低原始单信道语音中的噪声。
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引用次数: 0
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Mobile Networks and Applications
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