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An Energy-Efficient Multipath Routing Protocol for Secure Video-Packet Transmission Across MANETs Using a Blockchain Framework 一种基于区块链框架的安全视频包跨manet传输的节能多径路由协议
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-19 DOI: 10.1002/dac.70363
C. Selvan, M. A. Gunavathie, Sini Anna Alex, Shaik Jaffar Hussain

Appropriate routing strategies are necessary for mobile ad hoc networks (MANETs) in order to facilitate effective data transfer. In order to counter the prevailing problems, the correct routing schemes will need to be selected as the default configurations are used. In this paper, a special optimal link state routing (OLSR) protocol is proposed to incorporate a deep learning methodology to facilitate efficient video streaming in MANETs. This study presents a new improved variant of the OLSR protocol, which is specially tailored to achieve efficient video streaming in MANETs. It is a radical approach that combines a deep-learning model with blockchain technology to overcome security and reliability issues. It starts with the gathering of video content that is available publicly. In order to detect black-hole nodes, a special twin-attention-based Elman spiking neural network model is applied. The reliability of the neighboring nodes is then measured by means of trust values. The pufferfish optimization algorithm, or the accuracy-aware energy-efficient multipath routing algorithm (AEMRAP), which takes into account node- and link-stability degrees, is used in making routing decisions. Interplanetary file system (IPFS) technology is used to store the data on blockchain and increase its security. The authentication of the blockchain architecture is conducted via the delegated proof-of-stake (DPoS) method that also delivers an extra protection of MANETs against unauthorized access. The study demonstrates superior performance in securing and optimizing video transmission, confirming that the extended OLSR protocol is highly effective for MANET video streaming applications. The proposed model exceeds the current approaches with a throughput of 2100 Kbps, an average end latency of 20.2 s, and a packet-delivery ratio of 92.3%.

适当的路由策略对于移动自组织网络(manet)是必要的,以促进有效的数据传输。为了解决普遍存在的问题,需要在使用缺省配置的情况下选择正确的路由方案。本文提出了一种特殊的最优链路状态路由(OLSR)协议,该协议结合了深度学习方法,以促进manet中高效的视频流。本研究提出了一种新的改进的OLSR协议变体,该协议是专门为在manet中实现高效视频流而量身定制的。这是一种将深度学习模型与区块链技术相结合,克服安全性和可靠性问题的激进方法。它从收集公开的视频内容开始。为了检测黑洞节点,采用了一种特殊的基于双注意的Elman尖峰神经网络模型。然后通过信任值度量相邻节点的可靠性。采用河豚优化算法或精度感知节能多路径路由算法(AEMRAP)进行路由决策,该算法考虑了节点和链路的稳定性。采用IPFS (Interplanetary file system)技术将数据存储在区块链上,提高了区块链的安全性。区块链架构的身份验证是通过委托权益证明(DPoS)方法进行的,该方法还为manet提供了额外的保护,防止未经授权的访问。该研究证明了在保护和优化视频传输方面的卓越性能,证实了扩展的OLSR协议对于MANET视频流应用是非常有效的。该模型以2100 Kbps的吞吐量、20.2 s的平均端延迟和92.3%的数据包传送率超越了现有的方法。
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引用次数: 0
Leveraging Levy Flight and Lotus Effect for Secure Routing and Anomaly Detection in Wireless Sensor Network 利用Levy飞行和Lotus效应在无线传感器网络中的安全路由和异常检测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-17 DOI: 10.1002/dac.70348
A. Sarumathi, S. Sivanesh

The rapid increase in the integration of wireless sensor networks within the Internet of Things (IoT) ecosystem has led to crucial difficulties in providing reliable, energy-efficient, and secure communication. Most of the traditional intrusion detection models face few struggles in mitigating these challenges due to limited scalability and centralized processing. Therefore, this paper proposes a lightweight federated learning–based energy-aware (LF-LEA) model to overcome all the existing issues. The proposed model is an integration of federated learning, bio-inspired optimization, and energy-aware routing for decentralized environments. For local intrusion detection at edge nodes, the proposed model uses a lightweight convolutional neural network to transmit only the model updates rather than raw data, and this ensures the user's data privacy. The integration of the Levy flight and lotus effect mechanisms enhances the exploration and exploitation balance for improved intrusion detection accuracy. Furthermore, the S-LEACH–based routing protocol is incorporated to ensure secure and energy-efficient communication between nodes and base stations. Two benchmark datasets are used to validate the proposed model. The experimental results demonstrated that the proposed model achieves a higher accuracy of 98.60%, a precision of 98.32%, and a packet delivery ratio of 92.7%. In addition, the proposed model achieves a minimum communication delay and false alarm rate. Furthermore, the statistical Wilcoxon rank-sum test is conducted to confirm the effectiveness and consistency of the proposed model across diverse evaluation metrics. The overall result demonstrates that the proposed model ensures privacy preservation, scalability, and energy efficiency, making it a robust model for real-time intrusion detection in IoT-enabled applications, including smart cold storage monitoring systems, industrial automation, and environmental sensing networks.

物联网(IoT)生态系统中无线传感器网络集成的快速增长导致了提供可靠、节能和安全通信的关键困难。由于有限的可伸缩性和集中处理,大多数传统的入侵检测模型在缓解这些挑战方面几乎没有什么困难。因此,本文提出了一种轻量级的基于联邦学习的能量感知(LF-LEA)模型来克服这些问题。所提出的模型集成了联邦学习、生物启发优化和分散环境的能源感知路由。对于边缘节点的局部入侵检测,该模型使用轻量级卷积神经网络只传输模型更新而不传输原始数据,保证了用户数据的隐私性。Levy飞行和lotus效应机制的融合增强了探测和利用的平衡性,提高了入侵检测的准确性。此外,还结合了基于s - leach的路由协议,以确保节点和基站之间的安全节能通信。使用两个基准数据集来验证所提出的模型。实验结果表明,该模型的准确率为98.60%,准确率为98.32%,数据包投递率为92.7%。此外,该模型实现了最小的通信延迟和虚警率。此外,进行了统计Wilcoxon秩和检验,以确认所提出的模型在不同评价指标中的有效性和一致性。总体结果表明,所提出的模型确保了隐私保护、可扩展性和能源效率,使其成为支持物联网应用(包括智能冷库监控系统、工业自动化和环境传感网络)的实时入侵检测的鲁棒模型。
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引用次数: 0
QoS-Temperature-Aware Energy-Efficient Adaptive Routing Protocol for Wireless Body Area Networks 无线体域网络qos -温度感知节能自适应路由协议
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-17 DOI: 10.1002/dac.70321
Maytham Mohammed Tuaama AL-Tulibawi, Mohammadreza Soltanaghaei

Severe resource constraints, multi-hop connections, variable topology, and high sensitivity of transmitted data have raised the issue of quality of service (QoS) as one of the serious issues for wireless body area networks (WBANs). Therefore, many papers have been introduced in this field to improve the important aspects of the QoS field. However, studies indicate that some important issues have not been well considered, the most important of which are the lack of measures to consider the dynamic conditions of nodes and the effective support for varying traffic QoS. In addition, the performance of most methods leads to a sharp increase in control overhead. In this paper, a QoS-Temperature-Aware energy-efficient Adaptive Routing (QTAEEAR) based on the development of the Q-learning algorithm has been introduced. QTAEEAR is a three-step method. The routing table is created and updated in the first step. In the second step, routing and selecting the next-hop node are performed based on the Q learning. In the third step, learning is updated in response to new network conditions. Simulation results using NS-2 indicated the optimal performance and superiority of QTAEEAR compared to other similar methods.

严重的资源约束、多跳连接、多变的拓扑结构和传输数据的高灵敏度使得服务质量(QoS)问题成为无线体域网络(wban)面临的严重问题之一。因此,该领域的许多论文被引入,以改进QoS领域的重要方面。然而,研究表明,一些重要的问题没有得到很好的考虑,其中最重要的是缺乏考虑节点动态条件的措施和对不同流量QoS的有效支持。此外,大多数方法的性能会导致控制开销的急剧增加。本文介绍了一种基于q学习算法发展的QoS-Temperature-Aware节能自适应路由(QTAEEAR)。QTAEEAR是一个三步法。在第一步中创建并更新路由表。第二步,基于Q学习进行路由和下一跳节点的选择。第三步,学习根据新的网络条件进行更新。NS-2仿真结果表明,与其他同类方法相比,QTAEEAR具有最佳性能和优越性。
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引用次数: 0
Network Traffic Detection in Software-Defined Network Using Optimized Rotation-Invariant Coordinate Convolutional Neural Network 基于优化旋转不变坐标卷积神经网络的软件定义网络流量检测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-15 DOI: 10.1002/dac.70360
V. Sujatha, S. Prabakeran

software-defined networking (SDN) offers flexible traffic management but remains vulnerable to sophisticated cyberattacks, necessitating accurate and efficient network traffic detection. Existing SDN-based intrusion detection systems often suffer from high computational cost, poor scalability, and reduced accuracy in high-throughput or encrypted environments. To address these limitations, the NTD-SDN-RICCNN framework is proposed, which integrates fast robust iterative filtering (FRIF) for noise removal with spectral graph fast Fourier transform (SGFFT) for discriminative feature extraction. Rotation-invariant coordinate convolutional neural network (RICCNN) optimized with weighted velocity-guided gray wolf optimizer (WVGWO) for parameter tuning. The proposed method reduces redundant feature processing while improving detection accuracy and inference speed. Experiments on the SDN intrusion detection dataset show that NTD-SDN-RICCNN attains 99.7% accuracy, 99.6% precision, 99.5% recall, and reduces computational time by up to 32.5% compared to the state-of-the-art baselines. These results demonstrate the method's effectiveness and scalability for real-time SDN intrusion detection in diverse network conditions.

软件定义网络(SDN)提供了灵活的流量管理,但仍然容易受到复杂的网络攻击,因此需要准确高效的网络流量检测。现有的基于sdn的入侵检测系统在高吞吐量或加密环境下存在计算成本高、可扩展性差、准确性低等问题。为了解决这些限制,提出了NTD-SDN-RICCNN框架,该框架将快速鲁棒迭代滤波(FRIF)用于噪声去除和谱图快速傅里叶变换(SGFFT)用于判别特征提取相结合。采用加权速度导向灰狼优化器(WVGWO)优化旋转不变坐标卷积神经网络(RICCNN)进行参数整定。该方法减少了冗余特征处理,提高了检测精度和推理速度。在SDN入侵检测数据集上的实验表明,与最先进的基线相比,NTD-SDN-RICCNN达到了99.7%的准确率、99.6%的精密度、99.5%的召回率,并将计算时间减少了32.5%。实验结果证明了该方法在多种网络条件下实时检测SDN入侵的有效性和可扩展性。
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引用次数: 0
Multimetric-Based Optimization of Cluster Selection and Data Redundancy Elimination Through Copula Variational LSTM in Wireless Sensor Networks 基于多度量的无线传感器网络聚类选择和数据冗余消除的Copula变分LSTM优化
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-14 DOI: 10.1002/dac.70355
Anantha Pandi S, B. Sathyasri

In wireless sensor networks (WSN), the sensor nodes are deployed randomly where the sensor nodes are not positioned away from each other. The intersection of sensing ranges creates an overlapping area. Every sharing node perceives the same event and generates redundant and associated data if it takes place inside the overlapping area. In this paper, a multimetric-based optimization of cluster selection and data redundancy elimination through copula variational LSTM in wireless sensor networks (CS-DRE-CVLSTM-WSN) is proposed. Initially, cluster formation using semantic invariant multiview clustering (SIMVC) for data aggregation in WSN by leveraging data characteristics and connectivity patterns is discussed. Then, the clusters formed are given to the Wader Hunt Optimization Algorithm (WHOA) for cluster head selection. For this, a novel method to enhance data redundancy elimination efficiency, considering factors, like trust degree computation, energy efficiency, link quality, path loss, distance from the target node to the base station, and aggregation delay is proposed. The selected clusters are fed into copula variational LSTM (CV-LSTM) to optimize cluster selection and predict temporal trends in key network metrics. The offline meta RL (OMRL) framework is proposed to suppress redundant data streams and decide which data to transmit or aggregate. The reward system assigns positive values for efficient aggregation and reduced communication costs, while penalizing data loss or unnecessary transmissions. The output is a set of policies for effective data aggregation and redundancy elimination. The performance of the proposed CS-DRE-CVLSTM-WSN method is evaluated with existing methods like the reliable cluster dependent data aggregation scheme for IoT with hybrid deep learning methods (RC-DAS-IoT-HDL), new machine learning-driven data aggregation for predicting data redundancy in IoT connected WSN (ML-PDR-IoT-WSN), and two vector data prediction techniques for energy-efficient data aggregation in WSN (TVDP-EEDA-WSN), respectively.

在无线传感器网络(WSN)中,传感器节点被随机地部署在彼此不远离的位置。传感范围的交集创建了一个重叠区域。每个共享节点感知相同的事件,如果事件发生在重叠区域内,则生成冗余和相关的数据。本文提出了一种基于多度量的无线传感器网络(cs - re - cvlstm - wsn)聚类选择和数据冗余消除的耦合变分LSTM优化方法。首先,讨论了利用语义不变多视图聚类(SIMVC)在WSN中利用数据特征和连接模式进行数据聚合的聚类形成。然后,将形成的聚类交给Wader Hunt优化算法(wow)进行簇头选择。为此,提出了一种综合考虑信誉度计算、能量效率、链路质量、路径损耗、目标节点到基站距离、聚合时延等因素提高数据冗余消除效率的新方法。将选择的聚类输入到copula变分LSTM (CV-LSTM)中,以优化聚类选择并预测关键网络指标的时间趋势。提出了离线元RL (OMRL)框架来抑制冗余数据流,并决定传输或聚合哪些数据。奖励系统对有效聚合和降低通信成本给予积极的评价,同时惩罚数据丢失或不必要的传输。输出是一组有效的数据聚合和冗余消除策略。采用现有方法对cs - re - cvlstm -WSN方法的性能进行了评估,这些方法包括基于混合深度学习方法的物联网可靠集群相关数据聚合方案(RC-DAS-IoT-HDL)、用于预测物联网WSN数据冗余的新型机器学习驱动数据聚合方案(ML-PDR-IoT-WSN)和两种用于WSN节能数据聚合的矢量数据预测技术(TVDP-EEDA-WSN)。
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引用次数: 0
Performance Analysis of an Energy-Limited Multi-User Mixed RF/FSO System 一种能量有限的多用户射频/FSO混合系统的性能分析
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-14 DOI: 10.1002/dac.70361
Zhuo Wang

This paper investigates an energy-limited multi-user mixed radio frequency/free space optical (RF/FSO) system, where the RF links and FSO link undergo Rayleigh fading and Gamma-Gamma turbulence models incorporating the effect of pointing errors, respectively. Simultaneous wireless information and power transfer (SWIPT) based on the power splitting protocol is employed to maintain communication continuity of the system operating in an energy-limited scenario. Moreover, an energy-based user selection strategy is adopted to enhance the overall performance. Closed-form expressions for outage probability (OP) and approximate bit error rate (BER) are provided by using Meijer-G functions. The accuracy of theoretical derivations is validated by Monte-Carlo simulations. Results demonstrate that, under the user selection strategy, merely increasing the number of users cannot significantly boost system performance. Furthermore, influenced by multiple factors, the prudent design of the power splitting proportion enables system performance optimization.

本文研究了一种能量有限的多用户混合射频/自由空间光学(RF/FSO)系统,其中RF链路和FSO链路分别经历了包含指向误差影响的瑞利衰落和Gamma-Gamma湍流模型。采用基于功率分割协议的同步无线信息与功率传输(SWIPT)来保持系统在能量受限情况下的通信连续性。此外,采用基于能量的用户选择策略来提高整体性能。利用Meijer-G函数给出了中断概率(OP)和近似误码率(BER)的封闭表达式。通过蒙特卡罗仿真验证了理论推导的准确性。结果表明,在用户选择策略下,仅仅增加用户数量并不能显著提高系统性能。此外,在多种因素的影响下,合理设计功率分配比例可以优化系统性能。
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引用次数: 0
Cyber Threat Detection in 6G Internet of Things Using Deep Learning and Privacy Preservation via Blockchain 基于b区块链的深度学习和隐私保护的6G物联网网络威胁检测
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-14 DOI: 10.1002/dac.70356
C. Nandagopal, R. Rajesh Kanna, K. Sangeetha, Pushpalatha Naveenkumar

The quick propagation of Internet of Things (IoT) devices in 6th Generation (6G) networks intensifies security challenges due to high-dimensional and diverse nature of IoT data, which complicates feature selection and increases computational overhead. Dynamic and evolving attack patterns, including various intrusion types, malware, denial-of-service attempts, and coordinated botnet attacks, further reduce detection reliability. To address these challenges, Optimized Periodic Implicit Generative Adversarial networks with advanced Transformer (OPIGAT) proposes an end-to-end framework for robust and scalable IoT security. The framework begins with a preprocessing stage that handles missing values, outliers, and normalization for clean and consistent data. Feature optimization occurs through a Hybrid Tuna Particle Swarm Optimization Algorithm (HTPSO), which Merges Particle Swarm Optimization (PSO) global search capability with Tuna Swarm Optimization (TSO) spiral foraging-inspired local refinement, enabling precise selection of compact and highly discriminative features while reducing dimensionality. OPIGAT classifier detects diverse attacks, with the generative component synthesizing realistic traffic patterns and the transformer module capturing contextual relationships, enhancing anomaly detection and reducing false positives. Finally, a lightweight blockchain integrated with InterPlanetary File System (IPFS) ensures secure and scalable data management, employing proof of authority, Elliptic Curve Cryptography (ECC) with Ring signature encryption, and smart contract–based revocation to maintain privacy, integrity, and efficiency. Extensive experiments on CICIoT-2023 and ROUT-4-2023 datasets demonstrate superior accuracy (98.78% and 97%), high detection efficiency (98.21%), and a low false alarm rate (20%), while IPFS-enabled storage supports seamless scalability. These results establish OPIGAT as a secure, efficient, and highly effective solution for 6G IoT intrusion detection.

第六代(6G)网络中物联网(IoT)设备的快速传播,由于物联网数据的高维和多样性,加剧了安全挑战,使特征选择复杂化并增加了计算开销。动态和不断发展的攻击模式,包括各种入侵类型、恶意软件、拒绝服务尝试和协调的僵尸网络攻击,进一步降低了检测的可靠性。为了应对这些挑战,优化周期隐式生成对抗网络与先进的变压器(OPIGAT)提出了一个端到端框架,用于强大和可扩展的物联网安全。该框架从预处理阶段开始,该阶段处理缺失值、异常值,并对干净一致的数据进行规范化处理。特征优化通过混合金枪鱼粒子群优化算法(HTPSO)进行,该算法将粒子群优化(PSO)的全局搜索能力与金枪鱼群优化(TSO)的螺旋觅食启发的局部细化相结合,能够在降低维数的同时精确选择紧凑且高度判别的特征。OPIGAT分类器检测各种攻击,生成组件综合真实流量模式,转换模块捕获上下文关系,增强异常检测并减少误报。最后,与星际文件系统(IPFS)集成的轻量级区块链确保了安全和可扩展的数据管理,采用权威证明、带环签名加密的椭圆曲线加密(ECC)和基于智能合约的撤销来维护隐私、完整性和效率。在CICIoT-2023和route -4-2023数据集上进行的大量实验表明,该方法具有卓越的准确率(98.78%和97%)、高检测效率(98.21%)和低误报率(20%),同时支持ipfs的存储支持无缝扩展。这些结果表明OPIGAT是一种安全、高效、高效的6G物联网入侵检测解决方案。
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引用次数: 0
IRS-Assisted Cognitive Radio Network for Spectrum Sensing Under NPA Effects NPA效应下irs辅助认知无线电网络的频谱感知
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-09 DOI: 10.1002/dac.70352
Anmol Shalom Rathore, Alok Kumar, Amit Kumar

The nonlinear power amplifier (NPA) in a cognitive radio (CR) network can degrade sensing performance. While spectrum sensing primarily focuses on the receiver, modeling the transmitter's NPA is crucial to understand its impact on detection. This paper investigates spectrum sensing performance in CR networks assisted by intelligent reconfigurable surfaces (IRS), considering the impact of NPA distortions. A novel multiple-input single-output (MISO) IRS-aided CR system, termed MICR, is proposed. The sensing performance of the proposed MICR system is analyzed in terms of the probabilities of false alarm and detection, considering the effect of IRS elements. The results are presented using receiver operating characteristic (ROC) curves. Furthermore, the impact of various system parameters such as the number of samples, number of IRS elements, IRS position, number of transmitting antennas, and phase shifts is analyzed. Additionally, the effects of line-of-sight (LoS) and non–line-of-sight (NLoS) propagation are evaluated. The ROC performance using different signal detectors is analyzed. The impact of interference, IRS overhead, and sensing energy on the proposed MICR system is also investigated. Extensive simulations demonstrate that IRS significantly improves the sensing performance of the CR network, both with and without NPA.

认知无线电(CR)网络中的非线性功率放大器(NPA)会降低感知性能。虽然频谱传感主要关注接收器,但对发射器的NPA进行建模对于了解其对检测的影响至关重要。在考虑NPA畸变影响的情况下,研究了智能可重构曲面(IRS)辅助下CR网络的频谱感知性能。提出了一种新的多输入单输出(MISO) irs辅助CR系统,称为MICR。从虚警概率和检测概率两方面分析了所提出的MICR系统的传感性能,并考虑了IRS元素的影响。结果用受试者工作特征(ROC)曲线表示。此外,还分析了样品数量、IRS元件数量、IRS位置、发射天线数量和相移等系统参数的影响。此外,还评估了视距(LoS)和非视距(NLoS)传播的影响。分析了不同信号检测器的ROC性能。研究了干扰、IRS开销和传感能量对所提出的MICR系统的影响。大量的仿真表明,无论有无NPA, IRS都能显著提高CR网络的感知性能。
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引用次数: 0
Hybrid Spectrum Sensing Framework for Cognitive Radio Networks in Dynamic Environments 动态环境下认知无线电网络的混合频谱感知框架
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-09 DOI: 10.1002/dac.70347
Jaspreet Kaur, Neelam Srivastava

Performance issues with spectrum sensing are caused by multipath fading and shadowing, and obstacle blocking. This paper presents a novel Hybrid spectrum sensing framework that cooperatively combines energy detection (ED), matched filter detection (MFD), and cyclostationary feature detection (CFD) to detection reliability in cognitive radio networks. To mitigate fading and shadowing effects, cooperative spectrum sensing (CSS) is incorporated, leveraging multiple secondary users (SUs) for spatial diversity, achieving Pd = 0.80 at SNR = −20 dB using the OR fusion rule The key novelty lies in a dynamic switching mechanism that adaptively selects among ED, MFD, and CFD based on real-time signal and PU information availability, enabling operation under full, limited, or absent PU knowledge. Monte Carlo simulations in the low SNR region (−20 to −5 dB) demonstrate that the proposed hybrid detector achieves Pd values of 1, 0.98, and 0.95 for CFD, ED, and MFD, respectively, at Pf = 0.1. Moreover, it achieves energy efficiencies of 1.86 bits/J (non-CSS) and 2.05 bits/J (CSS), emphasizing the importance of energy-efficient spectrum access in power-constrained CR-IoT applications. The framework also minimizes average detection delay to 1.16 ms, ensuring faster PU identification. Owing to its adaptability and energy-aware operation, the proposed method is suitable for TV white spaces, CR-IoT, 5G-and-beyond networks, VANETs, and disaster-recovery communications.

频谱感知的性能问题是由多径衰落和阴影以及障碍物阻塞引起的。提出了一种新的混合频谱感知框架,将能量检测(ED)、匹配滤波器检测(MFD)和循环平稳特征检测(CFD)协同结合,提高认知无线电网络的检测可靠性。为了减轻衰落和阴影效应,采用协同频谱感知(CSS),利用多个辅助用户(su)实现空间分集,利用OR融合规则在信噪比=−20 dB时实现Pd = 0.80。关键新颖之处在于动态切换机制,该机制可根据实时信号和PU信息的可用性自适应选择ED、MFD和CFD,从而实现在完全、有限或缺乏PU知识的情况下运行。在低信噪比区域(- 20至- 5 dB)的蒙特卡罗模拟表明,在Pf = 0.1时,所提出的混合检测器在CFD、ED和MFD下的Pd值分别为1、0.98和0.95。此外,它还实现了1.86 bits/J(非CSS)和2.05 bits/J (CSS)的能量效率,强调了在功率受限的CR-IoT应用中节能频谱接入的重要性。该框架还将平均检测延迟降至1.16 ms,确保更快地识别PU。该方法具有自适应性和能量感知特性,适用于电视空白空间、CR-IoT、5g及以上网络、vanet和容灾通信。
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引用次数: 0
Optimized Call Admission Control for LTE Networks Based on Multilevel Bandwidth Allocation Framework 基于多级带宽分配框架的LTE网络呼叫接纳控制优化
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-08 DOI: 10.1002/dac.70303
Vaishali Satish Jadhav, Pallavi Vasant Sapkale, Moresh M. Mukhedkar

Efficient resource allocation is vital for ensuring quality of service (QoS) in Long-Term Evolution (LTE) networks, especially for call admission control (CAC). This study introduces a multilevel bandwidth (BW) allocation model that dynamically assigns BW based on call requirements and computes the minimum necessary BW when availability is limited. Calls are classified as new calls (NC) or handoff calls (HC) based on network conditions. To enhance CAC decision-making, a cascade feedforward network (Cascade FFN), integration of a cascade neuro-fuzzy network and a deep feedforward neural network (DFNN), is employed. The proposed model achieves high BW utilization of 0.911, power efficiency of 65.098 dBm, and mean throughput of 516,830.584 bps. Also, it minimizes call blocking probabilities for NC, which is 0.591, and for HC, which is 0.605; the call dropping probabilities for NC, which is 0.606, and for HC, which is 0.592; the mean delay of 0.066 s; and the number of dropped users of 820.620. These results demonstrate significant improvements in LTE performance through intelligent BW allocation and CAC.

在LTE (Long-Term Evolution)网络中,有效的资源分配是保证服务质量(QoS)的关键,尤其是在CAC (call admission control)网络中。本文提出了一种基于呼叫需求动态分配带宽的多级带宽分配模型,并在可用性有限的情况下计算出最小所需带宽。呼叫根据网络情况分为NC (new call)呼叫和HC (handoff call)呼叫。为了提高CAC的决策能力,采用级联前馈网络(cascade FFN),将级联神经模糊网络和深度前馈神经网络(DFNN)相结合。该模型的BW利用率为0.911,功率效率为65.098 dBm,平均吞吐量为516,830.584 bps。此外,它最小化了NC的呼叫阻塞概率(0.591)和HC的呼叫阻塞概率(0.605);NC和HC的通话中断概率分别为0.606和0.592;平均延时0.066 s;而流失用户数量为820.620。这些结果表明,通过智能BW分配和CAC, LTE性能得到了显著改善。
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International Journal of Communication Systems
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