China Communications最新文献

LDA-ID: An LDA-based framework for real-time network intrusion detection LDA-ID：基于 LDA 的实时网络入侵检测框架

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.ea.2021-0446.202302

Weidong Zhou, Shengwei Lei, Chunhe Xia, Tianbo Wang

引用次数: 0

Radio frequency fingerprinting identification using semi-supervised learning with meta labels 利用带元标签的半监督学习进行射频指纹识别

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.fa.2022-0609.202312

Tiantian Zhang, Pinyi Ren, Dongyang Xu, Zhanyi Ren

{"title":"Radio frequency fingerprinting identification using semi-supervised learning with meta labels","authors":"Tiantian Zhang, Pinyi Ren, Dongyang Xu, Zhanyi Ren","doi":"10.23919/JCC.fa.2022-0609.202312","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0609.202312","url":null,"abstract":"Radio frequency fingerprinting (RFF) is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things (IoT) systems. Deep learning (DL) is a critical enabler of RFF identification by leveraging the hardware-level features. However, traditional supervised learning methods require huge labeled training samples. Therefore, how to establish a highperformance supervised learning model with few labels under practical application is still challenging. To address this issue, we in this paper propose a novel RFF semi-supervised learning (RFFSSL) model which can obtain a better performance with few meta labels. Specifically, the proposed RFFSSL model is constituted by a teacher-student network, in which the student network learns from the pseudo label predicted by the teacher. Then, the output of the student model will be exploited to improve the performance of teacher among the labeled data. Furthermore, a comprehensive evaluation on the accuracy is conducted. We derive about 50 GB real long-term evolution (LTE) mobile phone's raw signal datasets, which is used to evaluate various models. Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97% experimental testing accuracy over a noisy environment only with 10% labeled samples when training samples equal to 2700.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"86 9","pages":"78-95"},"PeriodicalIF":4.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Deep reinforcement learning for IRS-assisted UAV covert communications 用于 IRS 辅助无人机隐蔽通信的深度强化学习

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.ea.2022-0336.202302

Songjiao Bi, Langtao Hu, QUAN LIU, Jianlan Wu, Rui Yang, L. Wu

引用次数: 0

An efficient federated learning framework deployed in resource-constrained IoV: User selection and learning time optimization schemes 在资源受限的物联网中部署高效的联合学习框架：用户选择和学习时间优化方案

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.fa.2022-0726.202312

Qiang Wang, Shaoyi Xu, Rongtao Xu, Dongji Li

{"title":"An efficient federated learning framework deployed in resource-constrained IoV: User selection and learning time optimization schemes","authors":"Qiang Wang, Shaoyi Xu, Rongtao Xu, Dongji Li","doi":"10.23919/JCC.fa.2022-0726.202312","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0726.202312","url":null,"abstract":"In this article, an efficient federated learning (FL) Framework in the Internet of Vehicles (IoV) is studied. In the considered model, vehicle users implement an FL algorithm by training their local FL models and sending their models to a base station (BS) that generates a global FL model through the model aggregation. Since each user owns data samples with diverse sizes and different quality, it is necessary for the BS to select the proper participating users to acquire a better global model. Meanwhile, considering the high computational overhead of existing selection methods based on the gradient, the lightweight user selection scheme based on the loss decay is proposed. Due to the limited wireless bandwidth, the BS needs to select an suitable subset of users to implement the FL algorithm. Moreover, the vehicle users' computing resource that can be used for FL training is usually limited in the IoV when other multiple tasks are required to be executed. The local model training and model parameter transmission of FL will have significant effects on the latency of FL. To address this issue, the joint communication and computing optimization problem is formulated whose objective is to minimize the FL delay in the resource-constrained system. To solve the complex nonconvex problem, an algorithm based on the concave-convex procedure (CCCP) is proposed, which can achieve superior performance in the small-scale and delay-insensitive FL system. Due to the fact that the convergence rate of CCCP method is too slow in a large-scale FL system, this method is not suitable for delay-sensitive applications. To solve this issue, a block coordinate descent algorithm based on the one-step projected gradient method is proposed to decrease the complexity of the solution at the cost of light performance degrading. Simulations are conducted and numerical results show the good performance of the proposed methods.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"372 2","pages":"111-130"},"PeriodicalIF":4.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139022303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-objective optimization for NOMA-based mobile edge computing offloading by maximizing system utility 通过系统效用最大化实现基于 NOMA 的移动边缘计算卸载的多目标优化

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.ea.2021-0252.202302

Hong Qin, Haitao Du, Huahua Wang, Li Su, Yunfeng Peng

引用次数: 0

IoV and blockchain-enabled driving guidance strategy in complex traffic environment 复杂交通环境下基于车联网和区块链的驾驶引导策略

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/jcc.ea.2020-0174.202302

Yuchuan Fu, Changle Li, T. Luan, Yao Zhang

引用次数: 0

Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization 基于高阶累积优化的PSO-BP神经网络水下多源DOA估计

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/jcc.ea.2021-0031.202302

Haihua Chen, Jingyao Zhang, Binbin Jiang, Xuerong Cui, Rongrong Zhou, Yucheng Zhang

{"title":"Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization","authors":"Haihua Chen, Jingyao Zhang, Binbin Jiang, Xuerong Cui, Rongrong Zhou, Yucheng Zhang","doi":"10.23919/jcc.ea.2021-0031.202302","DOIUrl":"https://doi.org/10.23919/jcc.ea.2021-0031.202302","url":null,"abstract":"Due to the complex and changeable environment under water, the performance of traditional DOA estimation algorithms based on mathematical model, such as MUSIC, ESPRIT, etc., degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model. In addition, the neural network has the ability of generalization and mapping, it can consider the noise, transmission channel inconsistency and other factors of the objective environment. Therefore, this paper utilizes Back Propagation (BP) neural network as the basic framework of underwater DOA estimation. Furthermore, in order to improve the performance of DOA estimation of BP neural network, the following three improvements are proposed. (1) Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process, PSO-BP-NN based on optimized particle swarm optimization (PSO) algorithm is proposed. (2) The Higher-order cumulant of the received signal is utilized to establish the training model. (3) A BP neural network training method for arbitrary number of sources is proposed. Finally, the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"1 1","pages":"212-229"},"PeriodicalIF":4.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68734795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

High-accuracy NLOS identification based on random forest and high-precision positioning on 60 GHz millimeter wave 基于随机森林和 60 GHz 毫米波高精度定位的高精度 NLOS 识别

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.fa.2021-0742.202312

Qiuna Niu, Wei Shi, Yongdao Xu, Weijun Wen

引用次数: 0

Deep learning based signal detector for OFDM systems 基于深度学习的 OFDM 系统信号检测器

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/JCC.fa.2021-0347.202312

Guangliang Pan, Wei Wang, Minglei Li

{"title":"Deep learning based signal detector for OFDM systems","authors":"Guangliang Pan, Wei Wang, Minglei Li","doi":"10.23919/JCC.fa.2021-0347.202312","DOIUrl":"https://doi.org/10.23919/JCC.fa.2021-0347.202312","url":null,"abstract":"In this paper, we propose a novel deep learning (DL)-based receiver design for orthogonal frequency division multiplexing (OFDM) systems. The entire process of channel estimation, equalization, and signal detection is replaced by a neural network (NN), and hence, the detector is called a NN detector (N2D). First, an OFDM signal model is established. We analyze both temporal and spectral characteristics of OFDM signals, which are the motivation for DL. Then, the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory (Bi-LSTM) NN. Especially, a discriminator (F) is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain (OCG), which can greatly improve the performance of the detector. Finally, the trained N2D is used for online recovery of OFDM symbols. The performance of the proposed N2D is analyzed theoretically in terms of bit error rate (BER) by Monte Carlo simulation under different parameter scenarios. The simulation results demonstrate that the BER of N2D is obviously lower than other algorithms, especially at high signal-to-noise ratios (SNRs). Meanwhile, the proposed N2D is robust to the fluctuation of parameter values.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"39 1","pages":"66-77"},"PeriodicalIF":4.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139016808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Secure short-packet transmission in uplink massive MU-MIMO assisted URLLC under imperfect CSI 不完全CSI下上行海量MU-MIMO辅助URLLC的安全短包传输

IF 4.1 3区计算机科学

China Communications Pub Date : 2023-12-01 DOI: 10.23919/jcc.ea.2021-0067.202302

Tianyue Yu, Xiaoli Sun, Yueming Cai, Z. Zhu

{"title":"Secure short-packet transmission in uplink massive MU-MIMO assisted URLLC under imperfect CSI","authors":"Tianyue Yu, Xiaoli Sun, Yueming Cai, Z. Zhu","doi":"10.23919/jcc.ea.2021-0067.202302","DOIUrl":"https://doi.org/10.23919/jcc.ea.2021-0067.202302","url":null,"abstract":"Ultra-reliable and low-latency communication (URLLC) is still in the early stage of research due to its two strict and conflicting requirements, i.e., ultra-low latency and ultra-high reliability, and its impact on security performance is still unclear. Specifically, short-packet communication is expected to meet the delay requirement of URLLC, while the degradation of reliability caused by it makes traditional physical-layer security metrics not applicable. In this paper, we investigate the secure short-packet transmission in uplink massive multiuser multiple-input-multiple-output (MU-MIMO) system under imperfect channel state information (CSI). We propose an artificial noise scheme to improve the security performance of the system and use the system average secrecy throughput (AST) as the analysis metric. We derive the approximate closed-form expression of the system AST and further analyze the system asymptotic performance in two regimes. Furthermore, a one-dimensional search method is used to optimize the maximum system AST for a given pilot length. Numerical results verify the correctness of theoretical analysis, and show that there are some parameters that affect the tradeoff between security and latency. Moreover, appropriately increasing the number of antennas at the base station (BS) and transmission power at user devices (UDs) can increase the system AST to achieve the required threshold.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"1 1","pages":"196-211"},"PeriodicalIF":4.1,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68734342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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