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  • REVIEW PAPER
    An Overview of Interactive Immersive Services
    Xiaozhi Yuan, Qingyang Wang, Linfeng Zhang, Li Peng, Xiaojie Zhu, Jinlan Ma, Zhan Liu, Yuxiang Jiang
    2023, 20(12): 1-18. https://doi.org/10.23919/JCC.ea.2022-0355.202302
    Abstract ( )   Knowledge map   Save
    Immersive services are the typical emerging services in current IMT-2020 network. With the development of network evolution, real-time interactive applications emerge one after another. This article provides an overview on immersive services which focus on real-time interaction. The scenarios, framework, requirements, key technologies, and issues of interactive immersive service are presented.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Joint Optimization Scheme for the Reconfigurable Intelligent Surface-Assisted Untrusted Relay Networks
    Lihua Gong, Wei Xu, Xiaoxiu Ding, Nanrun Zhou, Qibiao Zhu
    2023, 20(12): 19-29. https://doi.org/10.23919/JCC.ea.2021-0806.202302
    Abstract ( )   Knowledge map   Save
    To further improve the secrecy rate, a joint optimization scheme for the reconfigurable intelligent surface (RIS) phase shift and the power allocation is proposed in the untrusted relay (UR) networks assisted by the RIS. The eavesdropping on the UR is interfered by a source-based jamming strategy. Under the constraints of unit modulus and total power, the RIS phase shift, the power allocation between the confidential signal and the jamming signal, and the power allocation between the source node and the UR are jointly optimized to maximize the secrecy rate. The complex multivariable coupling problem is decomposed into three sub-problems, and the non-convexity of the objective function and the constraints is solved with semi-definite relaxation. Simulation results indicate that the secrecy rate is remarkably enhanced with the proposed scheme compared with the equal power allocation scheme, the random phase shift scheme, and the no-RIS scheme.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Single-Layer Symmetrical Full-Port Quasi-Absorptive Filtering Phase Shifter
    Qingxiang Dong, Yongle Wu, Weijuan Chen, Yuhao Yang, Weimin Wang
    2023, 20(12): 30-40. https://doi.org/10.23919/JCC.ea.2021-0764.202302
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    In this article, a single-layer symmetrical full-port quasi-absorptive filtering phase shifter is presented. The proposed phase shifter is composed of a main quasi-absorptive filtering branch, a reference quasi-absorptive filtering branch, and two delay lines. The proposed phase shifter achieves both phase controlling function and quasi-absorptive filtering function for the first time. Each quasi-absorptive filtering branch can realize the quasi-absorptive filtering function. Meanwhile, the constant phase shift can be obtained by combining the two quasi-absorptive filtering branches and the two delay lines. The design formulas can be derived from the even- and odd-mode network analysis, and then two quasi-absorptive filtering phase shifters can be devised easily and quickly. For verification, a 90° quasi-absorptive filtering phase shifter, which is critical for circularly polarized antenna systems, is simulated, manufactured, and measured.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Performance Analysis in SWIPT-Based Bidirectional D2D Communications in Cellular Networks
    Yang Zhang, Wei Wang, Xiangmo Zhao, Jun Hou
    2023, 20(12): 41-51. https://doi.org/10.23919/JCC.fa.2022-0554.202312
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    In this paper, a novel traffic-aware cooperative cognitive radio network that can enable device-to-device (D2D) communications in cellular system is proposed and investigated. By providing relay cooperation to cellular transmission, D2D users can realize their own two-way communication in the licensed spectrum. Unlike most existing works, in the proposed network, both wireless-powered D2D users can harvest energy via radio-frequency signals received from basic station (BS) through a hybrid protocol which can adaptively utilize both time-switching and power-splitting techniques. Specifically, D2D users perform decode-and-forward operation to transmit signals, and mobile user (MU) employs a selection combining technique. In addition, the performance of both D2D system and cellular system in the proposed network is evaluated by deriving the expressions of their exact outage probability and throughput. Numerical and simulation results validate correctness of derivations and reveal the influence of various system parameters of the proposed network.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Dynamic Spectrum Anti-Jamming with Distributed Learning and Transfer Learning
    Xinyu Zhu, Yang Huang, Delong Liu, Qihui Wu, Xiaohu Ge, Yuan Liu
    2023, 20(12): 52-65. https://doi.org/10.23919/JCC.fa.2022-0626.202312
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    Physical-layer security issues in wireless systems have attracted great attention. In this paper, we investigate the spectrum anti-jamming (AJ) problem for data transmissions between devices. Considering fast-changing physical-layer jamming attacks in the time/frequency domain, frequency resources have to be configured for devices in advance with unknown jamming patterns (i.e. the time-frequency distribution of the jamming signals) to avoid jamming signals emitted by malicious devices. This process can be formulated as a Markov decision process and solved by reinforcement learning (RL). Unfortunately, state-of-the-art RL methods may put pressure on the system which has limited computing resources. As a result, we propose a novel RL, by integrating the asynchronous advantage actor-critic (A3C) approach with the kernel method to learn a flexible frequency pre-configuration policy. Moreover, in the presence of time-varying jamming patterns, the traditional AJ strategy can not adapt to the dynamic interference strategy. To handle this issue, we design a kernel-based feature transfer learning method to adjust the structure of the policy function online. Simulation results reveal that our proposed approach can significantly outperform various baselines, in terms of the average normalized throughput and the convergence speed of policy learning.
    • SIGNAL PROCESSING
  • SIGNAL PROCESSING
    Deep Learning Based Signal Detector for OFDM Systems
    Guangliang Pan, Wei Wang, Minglei Li
    2023, 20(12): 66-77. https://doi.org/10.23919/JCC.fa.2021-0347.202312
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    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 (${N^2D}$). 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 ${N^2D}$ is used for online recovery of OFDM symbols. The performance of the proposed ${N^2D}$ 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 ${N^2D}$ is obviously lower than other algorithms, especially at high signal-to-noise ratios (SNRs). Meanwhile, the proposed ${N^2D}$ is robust to the fluctuation of parameter values.
  • SIGNAL PROCESSING
    Radio Frequency Fingerprinting Identification Using Semi-Supervised Learning with Meta Labels
    Tiantian Zhang, Pinyi Ren, Dongyang Xu, Zhanyi Ren
    2023, 20(12): 78-95. https://doi.org/10.23919/JCC.fa.2022-0609.202312
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    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 high-performance 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.
  • SIGNAL PROCESSING
    High-Accuracy NLOS Identification Based on Random Forest and High-Precision Positioning on 60 GHz Millimeter Wave
    Qiuna Niu, Wei Shi, Yongdao Xu, Weijun Wen
    2023, 20(12): 96-110. https://doi.org/10.23919/JCC.fa.2021-0742.202312
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    60 GHz millimeter wave (mmWave) system provides extremely high time resolution and multipath components (MPC) separation and has great potential to achieve high precision in the indoor positioning. However, the ranging data is often contaminated by non-line-of-sight (NLOS) transmission. First, six features of 60GHz mmWave signal under LOS and NLOS conditions are evaluated. Next, a classifier constructed by random forest (RF) algorithm is used to identify line-of-sight (LOS) or NLOS channel. The identification mechanism has excellent generalization performance and the classification accuracy is over 97%. Finally, based on the identification results, a residual weighted least squares positioning method is proposed. All ranging information including that under NLOS channels is fully utilized, positioning failure caused by insufficient LOS links can be avoided. Compared with the conventional least squares approach, the positioning error of the proposed algorithm is reduced by 49%.
    • NETWORKS
  • NETWORKS
    An Efficient Federated Learning Framework Deployed in Resource-Constrained IoV: User Selection and Learning Time Optimization Schemes
    Qiang Wang, Shaoyi Xu, Rongtao Xu, Dongji Li
    2023, 20(12): 111-130. https://doi.org/10.23919/JCC.fa.2022-0726.202312
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    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.
  • NETWORKS
    Deep Reinforcement Learning for IRS-Assisted UAV Covert Communications
    Songjiao Bi, Langtao Hu, Quanjin Liu, Jianlan Wu, Rui Yang, Lei Wu
    2023, 20(12): 131-141. https://doi.org/10.23919/JCC.ea.2022-0336.202302
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    Covert communications can hide the existence of a transmission from the transmitter to receiver. This paper considers an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) covert communication system. It was inspired by the high-dimensional data processing and decision-making capabilities of the deep reinforcement learning (DRL) algorithm. In order to improve the covert communication performance, an UAV 3D trajectory and IRS phase optimization algorithm based on double deep Q network (TAP-DDQN) is proposed. The simulations show that TAP-DDQN can significantly improve the covert performance of the IRS-assisted UAV covert communication system, compared with benchmark solutions.
  • NETWORKS
    Spatial Modeling and Reliability Analyzing of Reconfigurable Intelligent Surfaces-Assisted Fog-RAN with Repulsion
    Linlin Feng, Zhizhong Zhang, Haonan Hu, Errong Pei, Yun Li
    2023, 20(12): 142-155. https://doi.org/10.23919/JCC.ea.2022-0199.202302
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    Reconfigurable Intelligent Surface (RIS), fog computing, and Cell-Free (CF) network architecture are three promising technologies for application to the Ultra-Reliable Low Latency Communication (URLLC) scenario in 6G mobile communication systems. This paper considers a RIS-assisted Fog-Radio Access Network (Fog-RAN) architecture where a) the repulsively distributed Fog-Access Points (F-APs) communicate in a CF manner to suppress inter-cell interference, b) RISs are introduced into the CF network to avoid shadowing and enhance the system performance, and c) fog computing evolved as cloud services providers at the edge of the network and an enabler for constructing a multi-layer computing power RAN. Then, we derive and validate the integral form of the maximum F-AP offloading probability and Successful Delivery Probability (SDP) of this RIS-assisted Fog-RAN over composite Fisher-Snedecor ${\cal F}$ fading, where the spatial effects are reconsidered with the assumption that the F-APs are modelled as a Beta Ginibre Point Process ($\beta$-GPP). The numeric and simulation results indicate that for the investigated RIS-assisted Fog-RAN, the $\beta$-GPP-based deployment of F-APs can increase maximum of 8$\%$ of the SDP within the repulsion-effective range, compared with the Matern Cluster Process (MCP)-based ones. Also, deploying more RISs per F-AP offers more significant SDP improvements.
  • NETWORKS
    Multi-Objective Optimization for NOMA-Based Mobile Edge Computing Offloading by Maximizing System Utility
    Hong Qin, Haitao Du, Huahua Wang, Li Su, Yunfeng Peng
    2023, 20(12): 156-165. https://doi.org/10.23919/JCC.ea.2021-0252.202302
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    Mobile Edge Computing (MEC) is a technology for the fifth-generation (5G) wireless communications to enable User Equipment (UE) to offload tasks to servers deployed at the edge of network. However, taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and contradictory. Non-orthogonal multiple access (NOMA) enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G, which makes the problem even more complex in the NOMA- MEC system. In this work, a system utility maximization model is present to NOMA-MEC system, and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation, SIC order, transmission time slot allocation, which can easily achieve a better trade-off between the delay and energy consumption. The simulation results prove that the proposed method is effective for NOMA-MEC systems.
    • SECURITY
  • SECURITY
    LDA-ID: An LDA-Based Framework for Real-Time Network Intrusion Detection
    Weidong Zhou, Shengwei Lei, Chunhe Xia, Tianbo Wang
    2023, 20(12): 166-181. https://doi.org/10.23919/JCC.ea.2021-0446.202302
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    Network intrusion poses a severe threat to the Internet. However, existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap. In addition, efficient real-time detection is an urgent problem. To address the two above problems, we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection (LDA-ID), consisting of static and online LDA-ID. The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection. Thus, the detection is based on the latent topic features. To achieve efficient real-time detection, we design an online computing mode for static LDA-ID, in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information. Furthermore, we design two matching mechanisms to accommodate the static and online LDA-ID, respectively. Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.
  • SECURITY
    A Privacy-Preserving Federated Learning Algorithm for Intelligent Inspection in Pumped Storage Power Station
    Yue Zong, Yuanlin Luo, Yuechao Wu, Wenjian Hu, Hui Luo, Yao Yu
    2023, 20(12): 182-195. https://doi.org/10.23919/JCC.fa.2022-0647.202312
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    As a distributed machine learning architecture, Federated Learning (FL) can train a global model by exchanging users' model parameters without their local data. However, with the evolution of eavesdropping techniques, attackers can infer information related to users' local data with the intercepted model parameters, resulting in privacy leakage and hindering the application of FL in smart factories. To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations, in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption (FHE), called MFHE-PPFL. Specifically, to reduce communication costs caused by deploying the FHE algorithm, we propose a self-adaptive threshold-based model parameter compression (SATMPC) method. It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server. Moreover, to protect model parameter privacy during transmission, we develop a secret sharing-based multi-key RNS-CKKS (SSMR) method that encrypts the device's uploaded parameter increments and supports decryption in device dropout scenarios. Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.
  • SECURITY
    Secure Short-Packet Transmission in Uplink Massive MU-MIMO Assisted URLLC Under Imperfect CSI
    Tian Yu, Xiaoli Sun, Yueming Cai, Zeyuan Zhu
    2023, 20(12): 196-211. https://doi.org/10.23919/JCC.ea.2021-0067.202302
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    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.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization
    Haihua Chen, Jingyao Zhang, Bin Jiang, Xuerong Cui, Rongrong Zhou, Yucheng Zhang
    2023, 20(12): 212-229. https://doi.org/10.23919/JCC.ea.2021-0031.202302
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    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.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    IoV and Blockchain-Enabled Driving Guidance Strategy in Complex Traffic Environment
    Yuchuan Fu, Changle Li, Tom H. Luan, Yao Zhang
    2023, 20(12): 230-243. https://doi.org/10.23919/JCC.ea.2020-0174.202302
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    Diversified traffic participants and complex traffic environment (e.g., roadblocks or road damage exist) challenge the decision-making accuracy of a single connected and autonomous vehicle (CAV) due to its limited sensing and computing capabilities. Using Internet of Vehicles (IoV) to share driving rules between CAVs can break limitations of a single CAV, but at the same time may cause privacy and safety issues. To tackle this problem, this paper proposes to combine IoV and blockchain technologies to form an efficient and accurate autonomous guidance strategy. Specifically, we first use reinforcement learning for driving decision learning, and give the corresponding driving rule extraction method. Then, an architecture combining IoV and blockchain is designed to ensure secure driving rule sharing. Finally, the shared rules will form an effective autonomous driving guidance strategy through driving rules selection and action selection. Extensive simulation proves that the proposed strategy performs well in complex traffic environment, mainly in terms of accuracy, safety, and robustness.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    ELM-Based Impact Analysis of Meteorological Parameters on the Radio Transmission of X-Band over the Qiongzhou Strait of China
    Cheng Yang, Yafei Shi, Jian Wang, Jianguo Ma
    2023, 20(12): 244-256. https://doi.org/10.23919/JCC.fa.2022-0687.202312
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    Communication in the evaporation duct layer is greatly affected by the variation of meteorological parameters. Based on the experimental result of the radio transmission of the X-band over the Qiongzhou Strait of China, the characteristic of the duct and its influence on the transmission effect is analyzed. The results indicate that the evaporation duct height (EDH) has a negative Spearman's rank correlation of -0.90 with the relative humidity and a positive correlation coefficient of 0.84 with the wind speed. Based on the Extreme Learning Machine (ELM) network, we proposed a Met-ELM model that can provide efficient support in predicting propagation characteristics at nighttime. The predicted results of the Met-ELM model are consistent with the measurements; the root-mean-square-error is 1.66 dB, with the correlation coefficient reaching 0.96, while the proportion of mean absolute error less than 2 dB has reached 81.41%. The data-derived Met-ELM model shows great accuracy in predicting propagation characteristics at nighttime, which also meets the acceptable requirements for radio wave propagation.