Digital Communications and Networks最新文献_第6页

VSSTPM: Verifiable simulation-secure threshold public key encryption scheme from standard module-LWE for IoT gateway-based applications VSSTPM：来自标准模块lwe的可验证仿真安全阈值公钥加密方案，用于基于物联网网关的应用

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI: 10.1016/j.dcan.2025.06.015

Ye Bai , Debiao He , Zhichao Yang , Xiaoying Jia , Min Luo

The Internet of Things (IoT) has become an integral part of daily life, making the protection of user privacy increasingly important. In gateway-based IoT systems, user data is transmitted through gateways to platforms, pushing the data to various applications, widely used in smart cities, industrial IoT, smart farms, healthcare IoT, and other fields. Threshold Public Key Encryption (TPKE) provides a method to distribute private keys for decryption, enabling joint decryption by multiple parties, thus ensuring data security during gateway transmission, platform storage, and application access. However, existing TPKE schemes face several limitations, including vulnerability to quantum attacks, failure to meet Simulation-Security (SS) requirements, lack of verifiability, and inefficiency, which results in gateway-based IoT systems still being not secure and efficient enough. To address these challenges, we propose a Verifiable Simulation-Secure Threshold PKE scheme based on standard Module-LWE (VSSTPM). Our scheme resists quantum attacks, achieves SS, and incorporates Non-Interactive Zero-Knowledge (NIZK) proofs. Implementation and performance evaluations demonstrate that VSSTPM offers 112-bit quantum security and outperforms existing TPKE schemes in terms of efficiency. Compared to the ECC-based TPKE scheme, our scheme reduces the time cost for decryption participants by 72.66%, and the decryption verification of their scheme is 11 times slower than ours. Compared with the latest lattice-based TPKE scheme, our scheme reduces the time overhead by 90% and 48.9% in system user encryption and decryption verification, respectively, and their scheme is 13 times slower than ours in terms of decryption participants.

物联网（IoT）已经成为人们日常生活中不可或缺的一部分，保护用户隐私变得越来越重要。在基于网关的物联网系统中，用户数据通过网关传输到平台，将数据推送到各种应用中，广泛应用于智慧城市、工业物联网、智慧农场、医疗物联网等领域。TPKE （Threshold Public Key Encryption）提供了一种私钥分发解密的方法，可以实现多方联合解密，从而保证网关传输、平台存储和应用访问过程中的数据安全。然而，现有的TPKE方案面临着一些限制，包括易受量子攻击，无法满足模拟安全（SS）要求，缺乏可验证性和低效率，这导致基于网关的物联网系统仍然不够安全和高效。为了解决这些挑战，我们提出了一种基于标准模块lwe （VSSTPM）的可验证仿真安全阈值PKE方案。我们的方案可以抵抗量子攻击，实现SS，并结合非交互式零知识（NIZK）证明。实现和性能评估表明，VSSTPM提供112位量子安全性，并且在效率方面优于现有的TPKE方案。与基于ecc的TPKE方案相比，我们的方案将解密参与者的时间成本降低了72.66%，其方案的解密验证速度比我们的方案慢11倍。与最新的基于格子的TPKE方案相比，我们的方案在系统用户加解密验证方面的时间开销分别减少了90%和48.9%，在解密参与者方面，他们的方案比我们的方案慢13倍。

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引用次数: 0

DP-Fed6G: An adaptive differential privacy-empowered federated learning framework for 6G networks DP-Fed6G：用于6G网络的自适应差分隐私授权联邦学习框架

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-07-21 DOI: 10.1016/j.dcan.2025.07.006

Miao Du , Peng Yang , Yinqiu Liu , Xiaoming He , Mingkai Chen

The advent of 6G networks is poised to drive a new era of intelligent, privacy-preserving distributed learning by leveraging advanced communication and AI-driven edge intelligence. Federated Learning (FL) has emerged as a promising paradigm to enable collaborative model training without exposing raw data. However, its deployment in 6G networks faces significant obstacles, including vulnerabilities to inference attacks, the complexities of heterogeneous and dynamic network environments, and the inherent trade-off between privacy protection and model performance. In response to these challenges, we introduce DP-Fed6G, a novel FL framework that integrates differential privacy (DP) to fortify data security while ensuring high-quality learning outcomes. Specifically, DP-Fed6G employs an adaptive noise injection strategy that dynamically adjusts privacy protection levels based on real-time 6G network conditions and device heterogeneity, ensuring robust data security while maximizing model performance and optimizing the trade-off between privacy and utility. Extensive experiments on three real-world healthcare datasets demonstrate that DP-Fed6G consistently outperforms existing baselines (DP-FedSGD and DP-FedAvg), achieving up to 10.3% higher test accuracy under the same privacy budget. The proposed framework thus provides a practical solution for secure and privacy-preserving AI in 6G, supporting intelligent decision-making in privacy-sensitive applications.

6G网络的出现将通过利用先进的通信和人工智能驱动的边缘智能，推动智能、保护隐私的分布式学习的新时代。联邦学习（FL）已经成为一种很有前途的范例，可以在不暴露原始数据的情况下进行协作模型训练。然而，其在6G网络中的部署面临着重大障碍，包括易受推理攻击，异构和动态网络环境的复杂性，以及隐私保护和模型性能之间的内在权衡。为了应对这些挑战，我们引入了DP- fed6g，这是一种新颖的FL框架，它集成了差分隐私（DP），以加强数据安全，同时确保高质量的学习成果。具体而言，DP-Fed6G采用自适应噪声注入策略，根据实时6G网络条件和设备异构性动态调整隐私保护级别，在确保强大的数据安全性的同时，最大限度地提高模型性能，优化隐私与实用性之间的权衡。在三个真实医疗数据集上进行的大量实验表明，DP-Fed6G始终优于现有基线（DP-FedSGD和dp - fedag），在相同的隐私预算下，测试准确率最高可提高10.3%。因此，所提出的框架为6G中的安全和隐私保护人工智能提供了一个实用的解决方案，支持隐私敏感应用中的智能决策。

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引用次数: 0

An efficient downlink resource allocation scheme in power-limited GEO satellite systems 功率有限的地球同步轨道卫星系统中一种有效的下行链路资源分配方案

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-05-19 DOI: 10.1016/j.dcan.2025.05.007

Xin Tong, Nanxi Li, Jiaxiang Liu, Shuo Peng

The integration of Geostationary Earth Orbit (GEO) satellite constellations into Sixth Generation (6G) framework for cellular networks is essential to achieve global connectivity. Despite the major importance of this integration, current research often underestimates the limitations imposed by available satellite payload power, erroneously assuming a uniform maximum power density distribution across all communication beams. In this paper, we propose an Efficient Downlink Resource Allocation scheme (EDRA) that accounts for transmitting power resource limitations, variable service quality demands, and a heterogeneous number of users. Our approach relies on the thorough analysis of real-world demographic data, allowing us to optimize the allocation of downlink power and time–frequency resources in a practical and effective manner. Furthermore, we introduce an optimization model to maximize the total system revenue, using an iterative algorithm specifically designed to solve complex optimization problems. Numerical simulations demonstrated that the EDRA scheme improved the average network revenue by more than 66% relatively to standard methods, with performance gains increasingly large for an increasing diversity of service types, establishing the robustness and adaptability of the proposed EDRA scheme in the rapidly-evolving context of satellite-based communication systems.

将地球静止轨道（GEO）卫星星座整合到蜂窝网络第六代（6G）框架中对于实现全球连通性至关重要。尽管这种整合非常重要，但目前的研究往往低估了可用卫星有效载荷功率所施加的限制，错误地假设所有通信波束的最大功率密度分布是均匀的。在本文中，我们提出了一种有效的下行链路资源分配方案（EDRA），该方案考虑了传输功率资源的限制、服务质量需求的变化以及用户数量的异构。我们的方法依赖于对真实世界人口数据的深入分析，使我们能够以实际有效的方式优化下行功率和时频资源的分配。此外，我们引入了一个优化模型，以最大限度地提高系统的总收益，使用迭代算法专门设计来解决复杂的优化问题。数值模拟结果表明，与标准方法相比，EDRA方案的平均网络收益提高了66%以上，并且随着业务类型的增加，性能的提高幅度越来越大，从而证明了EDRA方案在快速发展的星基通信系统环境下的鲁棒性和适应性。

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引用次数: 0

EdgeAIGC: Model caching and resource allocation for edge artificial intelligence generated content EdgeAIGC：边缘人工智能生成内容的模型缓存和资源分配

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-07-04 DOI: 10.1016/j.dcan.2025.07.003

Wu Wen , Yibin Huang , Xinxin Zhao , Peiying Zhang , Kai Liu , Guowei Shi

With the rapid development of generative artificial intelligence technology, the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs, which restrict user-side in-situ Artificial Intelligence Generated Content (AIGC) service requests. To this end, we propose the Edge Artificial Intelligence Generated Content (EdgeAIGC) framework, which can effectively address the challenges of cloud computing by implementing in-situ processing of services close to the data source through edge computing. However, AIGC models usually have a large parameter scale and complex computing requirements, which poses a huge challenge to the storage and computing resources of edge devices. This paper focuses on the edge intelligence model caching and resource allocation problems in the EdgeAIGC framework, aiming to improve the cache hit rate and resource utilization of edge devices for models by optimizing the model caching strategy and resource allocation scheme, and realize in-situ AIGC service processing. With the optimization objectives of minimizing service request response time and execution cost in resource-constrained environments, we employ the Twin Delayed Deep Deterministic Policy Gradient algorithm for optimization. Experimental results show that, compared with other methods, our model caching and resource allocation strategies can effectively improve the cache hit rate by at least 41.06% and reduce the response cost as well.

随着生成式人工智能技术的快速发展，传统的基于云的集中式模型训练和推理由于传输延迟和成本高而面临明显的局限性，这限制了用户端原位人工智能生成内容（AIGC）服务请求。为此，我们提出了边缘人工智能生成内容（EdgeAIGC）框架，该框架通过边缘计算实现靠近数据源的服务的原位处理，可以有效应对云计算的挑战。然而，AIGC模型通常具有较大的参数规模和复杂的计算需求，这对边缘设备的存储和计算资源提出了巨大的挑战。本文主要研究EdgeAIGC框架下的边缘智能模型缓存和资源分配问题，通过优化模型缓存策略和资源分配方案，提高模型边缘设备的缓存命中率和资源利用率，实现原位AIGC服务处理。以资源受限环境下服务请求响应时间和执行成本最小化为优化目标，采用双延迟深度确定性策略梯度算法进行优化。实验结果表明，与其他方法相比，我们的模型缓存和资源分配策略可以有效地将缓存命中率提高至少41.06%，并降低响应成本。

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引用次数: 0

Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN 基于多通道BiWGAN-GTN和CEEMDAN的云数据中心群学习异常检测框架

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2024-08-10 DOI: 10.1016/j.dcan.2024.08.009

Lun Tang , Yuchen Zhao , Chengcheng Xue , Zhiwei Jiang , Wei Zou , Yanping Liang , Qianbin Chen

Anomaly detection is an important task for maintaining the performance of cloud data center. Traditional anomaly detection primarily examines individual Virtual Machine (VM) behavior, neglecting the impact of interactions among multiple VMs on Key Performance Indicator (KPI) data, e.g., memory utilization. Furthermore, the non-stationarity, high complexity, and uncertain periodicity of KPI data in VM also bring difficulties to deep learning-based anomaly detection tasks. To settle these challenges, this paper proposes MCBiWGAN-GTN, a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network (BiWGAN-GTN) and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). (a) The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data. (b) The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process. (c) MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components. (d) To adapt the proposed algorithm for the entire cloud data center, a cloud data center anomaly detection framework based on Swarm Learning (SL) is designed. The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline, with an F1-score of 0.96, an accuracy of 0.935, a precision of 0.954, a recall of 0.967, and an FPR of 0.203. The experiments also verify the stability of MCBiWGAN-GTN, the impact of parameter configurations, and the effectiveness of the proposed SL framework.

异常检测是维护云数据中心性能的一项重要任务。传统的异常检测主要检查单个虚拟机（VM）的行为，而忽略了多个虚拟机之间的交互对关键性能指标（KPI）数据的影响，例如内存利用率。此外，VM中KPI数据的非平稳性、高复杂性和不确定的周期性也给基于深度学习的异常检测任务带来了困难。为了解决这些问题，本文提出了一种基于双向Wasserstein生成对抗网络（BiWGAN-GTN）和带自适应噪声的完全集成经验模态分解（CEEMDAN）的多通道半监督时间序列异常检测算法MCBiWGAN-GTN。(a)提出BiWGAN-GTN算法从数据中提取时空信息。(b)重新设计BiWGAN-GTN的损失函数，解决训练过程中的异常数据入侵问题。(c) MCBiWGAN-GTN旨在通过CEEMDAN进行时间序列分解，降低数据复杂度，并利用BiWGAN-GTN训练不同分量。(d)为使所提算法适用于整个云数据中心，设计了基于Swarm Learning （SL）的云数据中心异常检测框架。在真实云数据中心数据集上的评价结果表明，MCBiWGAN-GTN优于基线，f1得分为0.96，准确度为0.935，精密度为0.954，召回率为0.967，FPR为0.203。实验还验证了MCBiWGAN-GTN的稳定性、参数配置的影响以及所提出的SL框架的有效性。

{"title":"Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN","authors":"Lun Tang , Yuchen Zhao , Chengcheng Xue , Zhiwei Jiang , Wei Zou , Yanping Liang , Qianbin Chen","doi":"10.1016/j.dcan.2024.08.009","DOIUrl":"10.1016/j.dcan.2024.08.009","url":null,"abstract":"<div><div>Anomaly detection is an important task for maintaining the performance of cloud data center. Traditional anomaly detection primarily examines individual Virtual Machine (VM) behavior, neglecting the impact of interactions among multiple VMs on Key Performance Indicator (KPI) data, e.g., memory utilization. Furthermore, the non-stationarity, high complexity, and uncertain periodicity of KPI data in VM also bring difficulties to deep learning-based anomaly detection tasks. To settle these challenges, this paper proposes MCBiWGAN-GTN, a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network (BiWGAN-GTN) and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). (a) The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data. (b) The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process. (c) MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components. (d) To adapt the proposed algorithm for the entire cloud data center, a cloud data center anomaly detection framework based on Swarm Learning (SL) is designed. The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline, with an F1-score of 0.96, an accuracy of 0.935, a precision of 0.954, a recall of 0.967, and an FPR of 0.203. The experiments also verify the stability of MCBiWGAN-GTN, the impact of parameter configurations, and the effectiveness of the proposed SL framework.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 6","pages":"Pages 1883-1896"},"PeriodicalIF":7.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing sustainability and security: A review of 5G and IoT in smart cities 平衡可持续性和安全性：智慧城市中的5G和物联网回顾

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-06-30 DOI: 10.1016/j.dcan.2025.06.007

Ghadah Aldehim , Sunawar khan , Tariq Shahzad , Muhammad Amir Khan , Yazeed Yasin Ghadi , Weiwei Jiang , Tehseen Mazhar , Habib Hamam

This century's rapid urbanization has disrupted urban governance, sustainability, and resource management. The Internet of Things (IoT) and 5G have the potential to transform smart cities through real-time data processing, enhanced connectivity, and sustainable urban design. This study investigates the potential of 5G connectivity with the IoT's hierarchical framework to enhance public service provision, mitigate environmental effects, and optimize urban resource management. The article asserts that these technologies can enhance urban operations by tackling scalability, interoperability, and security issues. The research employs case studies from Singapore and Barcelona. The document moreover analyzes AI-driven security systems, 6G networks, and the contributions of IoT and 5G to the advancement of a circular economy. The essay asserts that the growth of smart cities necessitates robust policy frameworks to guarantee equitable access, data protection, and ethical considerations. This study integrates prior research with practical experiences to tackle data-informed municipal governance and urban innovation. The importance of policy in fostering inclusive and sustainable urban futures is emphasized.

本世纪的快速城市化对城市治理、可持续性和资源管理造成了干扰。物联网（IoT）和5G有可能通过实时数据处理、增强连接和可持续城市设计来改变智慧城市。本研究探讨了5G连接与物联网分层框架在增强公共服务提供、减轻环境影响和优化城市资源管理方面的潜力。本文断言，这些技术可以通过解决可伸缩性、互操作性和安全性问题来增强城市运营。该研究采用了新加坡和巴塞罗那的案例研究。该文件还分析了人工智能驱动的安全系统、6G网络以及物联网和5G对推进循环经济的贡献。这篇文章断言，智慧城市的发展需要强有力的政策框架来保证公平获取、数据保护和道德考虑。本研究将前人的研究与实践经验相结合，以解决基于数据的城市治理和城市创新问题。强调政策在促进包容和可持续城市未来方面的重要性。

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引用次数: 0

Pre-filters design for weighted sum rate maximization in multiuser time reversal downlink systems 多用户时间反转下行系统中加权和速率最大化的预滤波器设计

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2024-08-12 DOI: 10.1016/j.dcan.2024.08.011

Mingyue Wang , Fangwei Li , Yingsong Li , Shengyuan Luo

In high-speed multiuser Time Reversal (TR) downlink systems, the transmission rate is degraded due to the presence of severe inter-user and inter-symbol interference. Moreover, maximizing the weighted sum rate in such systems is a critical objective, since the weighting factors represent the priority of different users in different applications. However, it faces significant challenges as it is an NP-hard and non-convex problem. In order to suppress these interferences and maximize the weighted sum rate, in this paper we present a novel approach for the joint design of the pre-filters. The proposed method applies successive convex approximation to transform the original problem into a Second-Order Cone Programming (SOCP) problem. Then, a low-complexity iterative algorithm is provided to effectively solve the resulting SOCP problem. According to the simulation results, the proposed method reaches a local optimum within a few iterations and demonstrates superior performance in terms of weighted sum rate compared to the current algorithm.

在高速多用户时间反转（TR）下行链路系统中，由于存在严重的用户间和符号间干扰，传输速率会降低。此外，在这样的系统中，最大化加权和率是一个关键的目标，因为加权因子代表了不同应用中不同用户的优先级。然而，由于它是一个NP-hard和非凸问题，它面临着重大的挑战。为了抑制这些干扰并使加权和率最大化，本文提出了一种联合设计预滤波器的新方法。该方法利用逐次凸逼近将原问题转化为二阶锥规划问题。在此基础上，提出了一种低复杂度的迭代算法，有效地解决了由此产生的SOCP问题。仿真结果表明，该方法在几次迭代内达到局部最优，在加权和速率方面优于现有算法。

引用次数: 0

Corrigendum to “DRL-based federated self-supervised learning for task offloading and resource allocation in ISAC-enabled vehicle edge computing” [Digit. Commun. Networks 11 (2025) 1614–1627] “基于drl的联邦自监督学习，用于isac支持的车辆边缘计算中的任务卸载和资源分配”的勘误表[数字]。Commun。网络11 (2025)1614-1627]

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-11-27 DOI: 10.1016/j.dcan.2025.11.005

Xueying Gu , Qiong Wu , Pingyi Fan , Nan Cheng , Wen Chen , Khaled B. Letaief

引用次数: 0

Privacy-preserving computation meets quantum computing: A scoping review 隐私保护计算与量子计算：范围回顾

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 Epub Date: 2025-05-21 DOI: 10.1016/j.dcan.2025.05.010

Aitor Gómez-Goiri, Iñaki Seco-Aguirre, Oscar Lage, Alejandra Ruiz

Privacy-Preserving Computation (PPC) comprises the techniques, schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis. Most of the current PPC techniques rely on the complexity of cryptographic operations, which are expected to be efficiently solved by quantum computers soon. This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats. We analyze quantum proposals for Secure Multi-party Computation, Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face. Our findings show a strong focus on purely theoretical works, but a rise on the experimental consideration of these techniques in the last 5 years. The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques.

隐私保护计算（PPC）包括在安全计算和数据分析的背景下确保隐私和机密性的技术、方案和协议。目前大多数PPC技术依赖于密码操作的复杂性，预计量子计算机将很快有效地解决这一问题。本文探讨了如何在量子计算本身的基础上构建PPC，以减轻这些未来的威胁。我们分析了近十年来安全多方计算、遗忘传输和同态加密的量子建议，重点分析了它们的成熟度和当前面临的挑战。我们的研究结果显示了对纯理论工作的强烈关注，但在过去5年中，对这些技术的实验考虑有所增加。这些技术对实际用例的适用性是一个未被充分探索的方面，这可能导致对这些技术的实际评估。

引用次数: 0

Jamming recognition method based on wavelet packet decomposition and improved deep learning 基于小波包分解和改进深度学习的干扰识别方法

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-10-01 Epub Date: 2025-05-12 DOI: 10.1016/j.dcan.2025.05.004

Qi Wu , Gang Li , Xiang Wang , Hao Luo , Lianghong Li , Qianbin Chen , Xiaorong Jing

To overcome the challenges of poor real-time performance, limited scalability, and low intelligence in conventional jamming pattern recognition methods, this paper proposes a method based on Wavelet Packet Decomposition (WPD) and enhanced deep learning techniques. In the proposed method, an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall (SW), which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network. The network employs a bilateral filter to preprocess the input SW, thereby enhancing the edge features of the jamming signals. To extract abstract features, depthwise separable convolution is utilized instead of traditional convolution, thereby reducing the network's parameter count and enhancing real-time performance. A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes, thus enhancing scalability. During network training, adaptive moment estimation is employed as the optimizer, allowing the network to dynamically adjust the learning rate and accelerate convergence. A comprehensive comparison between the proposed jamming recognition network and six other models is conducted, along with Ablation Experiments (AE) based on numerical simulations. Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy, network complexity, and prediction time.

针对传统干扰模式识别方法实时性差、可扩展性有限、智能程度低等问题，提出了一种基于小波包分解（WPD）和增强深度学习技术的干扰模式识别方法。在该方法中，接收端的代理使用WPD对接收信号进行处理，生成初始谱图瀑布（SW），随后使用滑动窗口对其进行分割，作为干扰识别网络的输入。该网络采用双边滤波器对输入的SW进行预处理，从而增强了干扰信号的边缘特征。为了提取抽象特征，采用深度可分离卷积代替传统的卷积，从而减少了网络的参数个数，提高了实时性。在全连接层之前集成了金字塔池层，使网络能够处理不同大小的输入SW，从而增强了可扩展性。在网络训练过程中，采用自适应矩估计作为优化器，使网络能够动态调整学习率，加快收敛速度。对所提出的干扰识别网络与其他六种模型进行了全面比较，并进行了基于数值模拟的消蚀实验。仿真结果表明，基于WPD和增强深度学习的方法在保持预测精度、网络复杂度和预测时间的良好平衡的同时，实现了对各种干扰模式的高精度识别。

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引用次数: 0