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Regularised Hyper Parameter Bi Level Optimisation With Continual Learning Based Deep Neural Network for Beamforming in Ultra-Wide Band System 基于持续学习深度神经网络的超宽带系统波束形成正则化超参数双级优化
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-28 DOI: 10.1049/cmu2.70137
Pradeep Kumar Siddanna, Bidare Divakarachari Parameshachari, Dharmanna Shivappa Lamani

Ultra-Wideband (UWB) is a wireless communication technology that uses Radio Frequency (RF) to transmit and receive signals between devices. Beamforming in UWB is a technique that uses multiple antennas simultaneously to focus on specific directions. In beamforming, Deep Learning (DL) techniques are applied to enhance signal processing and optimise beam pattern generation by utilising neural networks for efficient and accurate spatial filtering. However, existing DL techniques suffer from catastrophic forgetting, in which the testing data forgets previously learnt data due to the lack of knowledge distillation in other layers. Therefore, this research proposes a Regularised Hyperparameter Bilevel Optimisation with Continual Learning-based Deep Neural Network (RHBO-CLDNN) for beamforming in UWB systems. RHBO optimises hyperparameter efficiency at both the upper and lower levels, thereby enabling the DNN to accurately capture UWB channel characteristics, which improves channel estimation and enhances the Signal-to-Noise Ratio (SNR). CL is applied to dynamically adapt to changing environmental conditions without requiring complete retraining, making it suitable for real-time applications. Elastic Weight Consolidation (EWC) regularisation is also applied, which mitigates catastrophic forgetting by preserving weights from learnt tasks and enables the model to adapt to channel conditions without losing previous knowledge. Experiments on the DeepMIMO dataset show that RHBO-CLDNN enhances the sum-rate by up to 18% and achieves an inference time of 0.025 s over Convolutional Neural Network (CNN), thereby demonstrating its suitability for real-time beamforming.

超宽带(UWB)是一种使用射频(RF)在设备之间传输和接收信号的无线通信技术。超宽带波束形成是一种使用多个天线同时聚焦特定方向的技术。在波束形成中,深度学习(DL)技术被应用于增强信号处理,并通过利用神经网络进行有效和准确的空间滤波来优化波束模式生成。然而,现有的深度学习技术存在灾难性遗忘,即由于缺乏其他层的知识蒸馏,测试数据忘记了先前学习的数据。因此,本研究提出了一种基于持续学习的深度神经网络(RHBO-CLDNN)用于超宽带系统波束形成的正则化超参数双电平优化方法。RHBO优化了上下两层的超参数效率,从而使DNN能够准确捕获UWB信道特性,从而改进信道估计并提高信噪比(SNR)。CL应用于动态适应不断变化的环境条件,而不需要完全的再培训,使其适合实时应用。弹性权重巩固(EWC)正则化也被应用,它通过保留学习任务的权重来减轻灾难性遗忘,并使模型能够在不丢失先前知识的情况下适应信道条件。在DeepMIMO数据集上的实验表明,与卷积神经网络(CNN)相比,RHBO-CLDNN的求和速率提高了18%,推理时间为0.025 s,从而证明了其适合于实时波束形成。
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引用次数: 0
Hyperparameter-Free Maximum Versoria Criterion Based Channel-State Acquisition for mmWave Hybrid MIMO With Hardware Impairments 基于超无参数最大Versoria准则的毫米波混合MIMO信道状态采集
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-27 DOI: 10.1049/cmu2.70135
Rangeet Mitra, Sandesh Jain, K. Venkateswaran, Rajat Kumar, Vimal Bhatia, Ondrej Krejcar

Millimetre wave (mmWave) multiple-input multiple-output (MIMO) has emerged as a promising physical layer solution to address traffic demands in beyond 5G wireless communication systems. However, modern signal processing techniques for mmWave hybrid MIMO systems fall short of addressing the performance degradations due to residual transceiver hardware impairments (HIs). This paper thus considers a mmWave hybrid MIMO system with residual HIs. Using Bussgang decomposition, the residual transceiver HIs are modelled as an additive non-Gaussian noise that severely affects the received pilot and information signals, which makes channel state acquisition challenging. In this context of channel-estimation over non-Gaussian noise due to HIs, this work presents a hyperparameter-free maximum Versoria criterion (MVC)-based channel estimation technique. In details, the MVC-based channel-estimator is rendered hyperparameter-free by proposing a sampling rule for its spread parameter τ$tau$ and a gradient descent-based optimisation for its shape parameter p$p$. Finally, simulations are presented to show the generalisation and scenario-invariance of the proposed MVC-based channel-estimator. The analytical expression for steady-state misadjustment is also derived and validated by simulations.

毫米波(mmWave)多输入多输出(MIMO)已成为一种有前途的物理层解决方案,可满足5G以外无线通信系统的流量需求。然而,用于毫米波混合MIMO系统的现代信号处理技术无法解决由于剩余收发器硬件损伤(HIs)而导致的性能下降。因此,本文考虑了一种带有残余HIs的毫米波混合MIMO系统。利用Bussgang分解方法,将收发器的剩余HIs建模为严重影响接收导频信号和信息信号的加性非高斯噪声,使信道状态采集具有挑战性。在这种基于非高斯噪声的信道估计的背景下,这项工作提出了一种基于超参数无最大Versoria准则(MVC)的信道估计技术。详细地说,基于mvc的信道估计器通过对其扩展参数τ $tau$提出采样规则和对其形状参数p$ p$提出基于梯度下降的优化来实现超无参数化。最后,通过仿真验证了所提出的基于mvc的信道估计器的泛化和场景不变性。导出了稳态失调的解析表达式,并通过仿真进行了验证。
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引用次数: 0
Communication-Security Co-Design for Federated Learning in Grant-Free NOMA IoT Networks 无授权NOMA物联网网络中联邦学习的通信安全协同设计
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-27 DOI: 10.1049/cmu2.70138
Emmanuel Atebawone, Kwame Opuni-Boachie Obour Agyekum, James Dzisi Gadze, Kwasi Adu-Boahen Opare, Owusu Agyeman Antwi, Robert Akromond

The rapid growth of 6G Internet of Things (IoT) networks demands scalable and secure learning systems that can support massive device connectivity with minimal coordination overhead. Federated learning (FL) over grant-free non-orthogonal multiple access (GF-NOMA) offers a promising approach by enabling distributed model training with asynchronous uplink access and low signalling cost. However, this setup introduces coupled vulnerabilities: The uncoordinated nature of GF-NOMA leads to random collisions and residual interference, while the decentralised nature of FL exposes the system to poisoning, Sybil and jamming attacks. These cross-layer threats jointly degrade model convergence and communication reliability. To address this, we propose Security-Aware Proximal Policy Optimisation (SA-PPO), a reinforcement learning framework that co-designs communication security for FL over GF-NOMA. SA-PPO jointly embeds physical-layer features (e.g., SINR and interference) and learning-layer signals (e.g., anomaly scores and trust values) into its state, action and reward spaces. This enables the base station to optimise admission control, resource allocation and trust-weighted aggregation in a unified loop. Unlike prior methods that treat communication and security independently, SA-PPO learns coordinated strategies that attenuate adversarial impact while preserving update diversity. Simulation results show that SA-PPO achieves over 90% anomaly detection accuracy, sustains secure participation above 80% and reduces collision-induced decoding errors by 25% under scenarios with up to 40% compromised devices, while incurring only modest increases in energy and latency. These results demonstrate SA-PPO's effectiveness for secure, scalable and resilient edge intelligence in future 6G IoT environments.

6G物联网(IoT)网络的快速增长需要可扩展和安全的学习系统,这些系统可以以最小的协调开销支持大量设备连接。联邦学习(FL)优于无授权非正交多址访问(GF-NOMA),通过异步上行链路访问和低信令成本实现分布式模型训练,提供了一种很有前途的方法。然而,这种设置引入了耦合漏洞:GF-NOMA的不协调性质导致随机碰撞和残余干扰,而FL的分散性质使系统暴露于中毒,Sybil和干扰攻击。这些跨层威胁共同降低了模型的收敛性和通信可靠性。为了解决这个问题,我们提出了安全感知的近端策略优化(SA-PPO),这是一种强化学习框架,可在GF-NOMA上共同设计FL的通信安全性。SA-PPO将物理层特征(如SINR和干扰)和学习层信号(如异常分数和信任值)共同嵌入到其状态、动作和奖励空间中。这使得基站能够在一个统一的环路中优化准入控制、资源分配和信任加权聚合。与之前独立处理通信和安全的方法不同,SA-PPO学习协调策略,在保持更新多样性的同时减少对抗性影响。仿真结果表明,SA-PPO实现了超过90%的异常检测准确率,保持了80%以上的安全参与,在高达40%的设备受损的情况下,将碰撞引起的解码错误减少了25%,而能量和延迟只会适度增加。这些结果证明了SA-PPO在未来6G物联网环境中安全、可扩展和弹性边缘智能的有效性。
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引用次数: 0
Construction-Free Polar Coding For Practical Entropy Coding Tasks 实用熵编码任务的无构造极性编码
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-21 DOI: 10.1049/cmu2.70130
Zichang Ren, Cheng Zhang, Yuping Zhao

This paper explores the practical application of source polar codes to entropy coding tasks in modern transform coding pipelines. Transform coding remains the predominant and rapidly evolving framework for compressing complex real-world data. Despite the strong theoretical guarantees of polar codes, conventional polarization-based compression techniques follow a “construct-then-use” paradigm, which proves inefficient and inaccurate when applied to transform coding scenarios characterized by highly dynamic entropy models. To overcome this limitation, we propose a construction-free, plug-and-play polar compression scheme. Rather than relying on precomputed polarized entropies, our method selects output symbols based on probability vectors generated by a conditional entropy model. These vectors can be computed with low complexity and exact numerical precision, enabling efficient adaptation across diverse entropy coding tasks. The proposed approach offers greater flexibility than classical methods and achieves superior performance in the finite-length regime.

本文探讨了源极码在现代变换编码管道中熵编码任务中的实际应用。转换编码仍然是压缩复杂现实世界数据的主要和快速发展的框架。尽管极性编码有很强的理论保证,但传统的基于极化的压缩技术遵循“先构造后使用”的范式,当应用于以高度动态熵模型为特征的转换编码场景时,这种方法被证明是低效和不准确的。为了克服这一限制,我们提出了一种免施工,即插即用的极性压缩方案。我们的方法不是依赖于预先计算的极化熵,而是根据条件熵模型生成的概率向量选择输出符号。这些向量可以以低复杂度和精确的数值精度计算,从而能够有效地适应不同的熵编码任务。与传统方法相比,该方法具有更大的灵活性,并且在有限长度区域内具有更好的性能。
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引用次数: 0
A Contrastive GAN-Based Framework for Full-Body Visual Privacy Protection in Open World Scenarios 一种基于对比gan的开放场景全身视觉隐私保护框架
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-19 DOI: 10.1049/cmu2.70128
Haolong Fu, Xuan He

Generative adversarial networks (GANs) with their strong generative capabilities have shown significant promise in visual privacy protection. However, when applied to image full-body visual privacy protection in open-world scenarios, where abnormal visual privacy data may exist in the training data, issues such as mode collapse and instability in GANs can be severely exacerbated. This leads to a significant reduction in both image quality and utility preservation. In this paper, we propose an end-to-end, contrastive GANs-based framework, FBPPGAN, for image full-body visual privacy protection, specifically designed to address these challenges. First, we introduce the architecture of FBPPGAN, which is tailored for full-body visual privacy protection. Second, we propose a novel adversarial loss function aimed at mitigating mode collapse and instability, particularly in the presence of abnormal images in open-world environments. We also design a content mapping network and a content loss function based on contrastive learning to address the issue of insufficient color gamut in generated images. Furthermore, a stylized loss function is introduced to more accurately measure the difference between the generated and target domains. Experimental results across four public datasets demonstrate that FBPPGAN effectively overcomes mode collapse and instability, delivering superior image quality and utility preservation. Compared to the existing state-of-the-art methods, FBPPGAN outperforms in terms of convergence, stability, computational complexity, processing speed, and effectiveness. To the best of our knowledge, this is the first GAN-based framework for image full-body visual privacy protection in open-world scenarios.

生成对抗网络(GANs)以其强大的生成能力在视觉隐私保护方面显示出重要的应用前景。然而,当应用于开放世界场景下的图像全身视觉隐私保护时,训练数据中可能存在异常的视觉隐私数据,会严重加剧gan的模态崩溃和不稳定等问题。这将导致图像质量和效用保存的显著降低。在本文中,我们提出了一个端到端、基于对比gan的框架,FBPPGAN,用于图像全身视觉隐私保护,专门设计用于解决这些挑战。首先,我们介绍了专为全身视觉隐私保护而设计的FBPPGAN结构。其次,我们提出了一种新的对抗损失函数,旨在减轻模式崩溃和不稳定性,特别是在开放世界环境中存在异常图像的情况下。我们还设计了一个内容映射网络和一个基于对比学习的内容损失函数来解决生成图像的色域不足的问题。此外,还引入了一种程式化的损失函数,以更准确地测量生成域与目标域之间的差异。在四个公共数据集上的实验结果表明,FBPPGAN有效地克服了模式崩溃和不稳定性,提供了卓越的图像质量和效用保存。与现有的最先进的方法相比,FBPPGAN在收敛性、稳定性、计算复杂性、处理速度和有效性方面都优于现有的方法。据我们所知,这是开放世界场景中第一个基于gan的图像全身视觉隐私保护框架。
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引用次数: 0
Exploring Missed Spectrum Access Opportunities in Wi-Fi 6 and 5G NR-U Coexistence for Enhancing 6 GHz Spectrum Utilization 探索Wi-Fi 6和5G NR-U共存中错失的频谱接入机会,提高6ghz频谱利用率
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-16 DOI: 10.1049/cmu2.70125
Md Toufiqur Rahman, Jiang Xie, Xingya Liu

Since the opening of the 6 GHz bands for unlicensed radio access technologies (RATs), new coexistence mechanisms leveraging the currently uninhabited 6 GHz bands have been investigated, aiming for fair coexistence of Wi-Fi 6 and 5G new radio unlicensed (NR-U). However, our study shows that the utilization of the highly attractive 6 GHz bands can be significantly enhanced by exploring additional spectrum access opportunities, which remain unrealized in the existing channel contention mechanisms. We propose a novel two-stage channel contention mechanism for the coexistence of Wi-Fi 6 and 5G NR-U in the 6 GHz bands to explore these missed spectrum access opportunities. We formulate the probability of interference to ongoing transmissions and utilize this probability to enhance the utilization of radio resources by allowing simultaneous transmissions on a channel. We incorporate cross-technology communication (CTC) to compute this probability and formulate an optimization problem to derive the optimal CTC information required for the computation. Extensive simulation results show that the proposed framework significantly outperforms legacy channel contention mechanisms in terms of spectrum utilization while ensuring the ongoing transmissions unharmed.

自6 GHz频段开放给无牌无线接入技术(rat)以来,已经研究了利用目前无人使用的6 GHz频段的新共存机制,旨在实现Wi-Fi 6和5G新无牌无线电(NR-U)的公平共存。然而,我们的研究表明,通过探索额外的频谱接入机会,可以显著提高具有高度吸引力的6 GHz频段的利用率,这在现有的信道竞争机制中仍未实现。我们提出了一种新的两阶段信道争用机制,使Wi-Fi 6和5G NR-U在6ghz频段共存,以探索这些错过的频谱接入机会。我们制定了对正在进行的传输的干扰概率,并利用该概率通过允许在信道上同时传输来提高无线电资源的利用率。我们结合跨技术通信(CTC)来计算这一概率,并制定了一个优化问题来获得计算所需的最优CTC信息。大量的仿真结果表明,该框架在确保正在进行的传输不受损害的同时,在频谱利用率方面显著优于传统的信道争用机制。
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引用次数: 0
A Spectral-Spatial Dual Encoder With Bio-Inspired Swarm Adaptation for Cognitive Spectrum Sensing Using Real-World RF Signal Data 一种基于生物启发群适应的频谱空间双编码器,用于使用真实射频信号数据的认知频谱感知
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-16 DOI: 10.1049/cmu2.70133
P. Ramakrishnan, C. Kumar, R. Saravana Kumar, Sourav Barua

Cognitive Spectrum Sensing (CSS) stands as a foundational component in 5G and emerging 6G wireless communication systems, enabling intelligent identification and dynamic utilisation of underutilised spectrum bands. However, the implementation of CSS in dense and heterogeneous 5G/6G environments presents significant challenges, including high spectral dynamics, multi-protocol interference, and the requirement for real-time decision-making across diverse frequency bands. Existing methods such as deep belief networks, CNN-PSO hybrids, and DQN-based models suffer from limited adaptability, insufficient spatial-temporal learning, and poor generalisation in real-world RF environments. The proposed model includes a dual-stream deep learning architecture which has a 1D convolutional neural network (CNN)-based spectral encoder and a graph convolutional network (GCN)-based spatial encoder for extracting the frequency-domain and node-topology features. Experimental analysis of proposed model is performed using the Real-World Wireless Communication Dataset containing Wi-Fi, LTE, and 5G RF signals. The dataset is pre-processed using Fast Fourier Transformation (FFT) transformation and labelled through a signal-power-based thresholding mechanism. Results indicate the Spectral-Spatial Dual Encoder with Bio-Inspired Swarm Adaptation (SSDE-BSA) achieves an accuracy of 96.1%, an F1-score of 96.1%, and a spectral efficiency of 91. These results confirm the model's superiority in adapting to real-world spectrum dynamics, offering a robust and scalable solution for cognitive spectrum sensing in next-generation wireless networks.

认知频谱感知(CSS)是5G和新兴6G无线通信系统的基础组件,可实现未充分利用的频段的智能识别和动态利用。然而,在密集和异构的5G/6G环境中实现CSS面临着重大挑战,包括高频谱动态、多协议干扰以及跨不同频段的实时决策要求。现有的方法,如深度信念网络、CNN-PSO混合模型和基于dqn的模型,在现实射频环境中存在适应性有限、时空学习不足和泛化能力差的问题。该模型包括一个双流深度学习架构,该架构具有一个基于一维卷积神经网络(CNN)的频谱编码器和一个基于图卷积网络(GCN)的空间编码器,用于提取频域和节点拓扑特征。使用包含Wi-Fi, LTE和5G RF信号的真实世界无线通信数据集对所提出的模型进行了实验分析。该数据集使用快速傅里叶变换(FFT)进行预处理,并通过基于信号功率的阈值机制进行标记。结果表明,基于生物启发群适应的光谱-空间双编码器(SSDE-BSA)的精度为96.1%,f1得分为96.1%,光谱效率为91。这些结果证实了该模型在适应现实世界频谱动态方面的优势,为下一代无线网络中的认知频谱传感提供了一个鲁棒和可扩展的解决方案。
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引用次数: 0
Enhancing CNN Path Loss Estimation Performance using Satellite Image and Distance Feature 利用卫星图像和距离特征增强CNN路径损失估计性能
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-15 DOI: 10.1049/cmu2.70132
Renata Nur Afifah, Irma Zakia

We employ convolutional neural networks (CNNs) with distance feature and satellite image for path loss (PL) estimation at sub-6 GHz and millimetre wave (mmWave) frequencies. In order to avoid complex preprocessing of embedding distance feature into the image, we append this feature at the earliest, after the convolutional blocks of a CNN-based VGG-16 architecture. This is intuitive since the following fully-connected (FC) layer performs feature aggregation, thus, it combines the injected distance feature with the extracted features from the image. We propose three VGG-16 structures which vary in how the distance information is included. Performance is then evaluated in terms of training and prediction times, root mean square error (RMSE) and correlation coefficient, while performance without appended distance serves as benchmark. We observe that the inclusion of distance parameter gives more accurate estimation in terms of RMSE and a very strong correlation between the predicted and estimated PL values. Moreover, the proposed structures typically converge more quickly. Among the proposed structures, the one aided by a logarithm-of-distance model, is the most computationally efficient, leading to 50%$50%$ and 27%$27%$ reduction of training time and prediction time, respectively. Additionally, the VGG-16-based PL predictors yield lower RMSE by up to 2.4 dB and 21%$21%$ higher correlation compared to the 3GPP 38.901 urban macro cell (UMa) empirical model.

我们使用具有距离特征和卫星图像的卷积神经网络(cnn)在sub-6 GHz和毫米波(mmWave)频率下进行路径损耗(PL)估计。为了避免在图像中嵌入距离特征的复杂预处理,我们最早在基于cnn的VGG-16架构的卷积块之后附加该特征。这是直观的,因为下面的全连接(FC)层执行特征聚合,因此,它将注入的距离特征与从图像中提取的特征结合起来。我们提出了三种VGG-16结构,它们在包含距离信息的方式上有所不同。然后根据训练和预测时间、均方根误差(RMSE)和相关系数来评估性能,而不附加距离的性能作为基准。我们观察到,距离参数的包含在RMSE方面给出了更准确的估计,并且预测和估计的PL值之间具有很强的相关性。此外,所提出的结构通常收敛得更快。在提出的结构中,由距离对数模型辅助的结构是计算效率最高的,其训练时间和预测时间分别减少了50%和27%。此外,与3GPP 38.901城市宏细胞(UMa)经验模型相比,基于vgg -16的PL预测因子的RMSE降低了2.4 dB,相关性提高了21%。
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引用次数: 0
Joint Optimization Algorithm for UAV Trajectory and Beamforming for NOMA-Based Integrated Sensing and Communication (ISAC) Systems 基于noma的集成传感与通信(ISAC)系统无人机轨迹与波束形成联合优化算法
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-13 DOI: 10.1049/cmu2.70131
Dongsheng Han, Zian Wang, Yan Wang

Non-orthogonal multiple access (NOMA) combined with integrated sensing and communication (ISAC) presents a promising solution to 6G spectrum scarcity. However, non-line-of-sight channels caused by obstacles significantly degrade performance. Unmanned aerial vehicles (UAVs) can establish line-of-sight links, thereby improving communication rates. Existing research primarily focuses on stationary targets, while studies on moving targets rely on single-UAV sensing, leading to substantial estimation errors in dynamic environments. To address this, we propose a UAV-enabled NOMA-ISAC network that employs an extended Kalman filter for accurate target tracking. We aim to jointly optimize the UAV trajectory and beamforming vector using a block coordinate descent framework, where the subproblems are efficiently solved via successive convex approximation and semidefinite programming. Numerical results demonstrate that the proposed scheme achieves an 18.47% higher sum rate compared to benchmark schemes.

非正交多址(NOMA)与集成传感与通信(ISAC)相结合,为解决6G频谱短缺问题提供了一种很有前景的解决方案。然而,障碍物造成的非视线通道会显著降低性能。无人驾驶飞行器(uav)可以建立视距链接,从而提高通信速率。现有的研究主要集中在静止目标上,而对运动目标的研究依赖于单无人机传感,导致在动态环境下估计误差很大。为了解决这个问题,我们提出了一种支持无人机的NOMA-ISAC网络,该网络采用扩展卡尔曼滤波器进行精确的目标跟踪。采用分块坐标下降框架对无人机轨迹和波束形成矢量进行联合优化,其中子问题通过逐次凸逼近和半定规划有效求解。数值结果表明,与基准方案相比,该方案的和速率提高了18.47%。
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引用次数: 0
A Microstrip Monopole Antenna Design for 5G Sub-6 GHz Applications Using Deep Learning 基于深度学习的5G sub - 6ghz微带单极天线设计
IF 1.6 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-05 DOI: 10.1049/cmu2.70127
Berker Çolak, Mehmet Ali Belen, Farzad Kiani, Ozlem Tari, Peyman Mahouti, Oguzhan Akgol

This study presents the design and optimization of a microstrip monopole antenna for 5G sub-6 GHz applications, employing a deep learning-based surrogate model combined with honeybee mating optimization (HBMO). The studied antenna structure employs air via arrays, intended to enhance antenna performance, including improved impedance matching and increased bandwidth. It is important to note that, unlike conventional antennas, the proposed design does not include a fully enclosed metallic cavity similar to a substrate integrated waveguide (SIW) antenna designs. A sensitivity analysis was conducted to assess the impact of these parameters, emphasizing the need for optimal tuning. To generate training and test datasets efficiently, Latin hypercube sampling (LHS) was used. A convolutional neural network (CNN) surrogate model was trained, outperforming other machine learning (ML) algorithms in predictive accuracy and generalization. The proposed CNN-HBMO framework reduced computational costs by minimizing the need for expensive electromagnetic (EM) simulations, enabling rapid design space exploration. The optimized antenna was fabricated and validated through experimental measurements, achieving 2–3 dBi gain and 𝑆11 < −10 dB across the 2.7–5.2 GHz band. Compared to existing designs, the proposed antenna offers a compact size (34 × 34 mm) with competitive performance, making it suitable for multi-band 5G applications.

本研究采用基于深度学习的代理模型结合蜜蜂交配优化(HBMO),设计和优化了一种用于5G sub-6 GHz应用的微带单极天线。所研究的天线结构采用空气通孔阵列,旨在提高天线性能,包括改善阻抗匹配和增加带宽。值得注意的是,与传统天线不同,所提出的设计不包括类似于基板集成波导(SIW)天线设计的全封闭金属腔。进行了敏感性分析,以评估这些参数的影响,强调需要进行最佳调整。为了有效地生成训练和测试数据集,采用拉丁超立方体采样(LHS)。训练卷积神经网络(CNN)代理模型,在预测准确性和泛化方面优于其他机器学习(ML)算法。提出的CNN-HBMO框架通过最大限度地减少昂贵的电磁(EM)模拟需求来降低计算成本,从而实现快速设计空间探索。优化后的天线制作完成,并通过实验测量进行了验证,在2.7-5.2 GHz频段内实现了2-3 dBi增益和𝑆11 <;−10 dB。与现有设计相比,拟议的天线具有紧凑的尺寸(34 × 34毫米),具有竞争力的性能,适合多频段5G应用。
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引用次数: 0
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