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    25 March 2025, Volume 23 Issue 1
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    The whole issue of ZTE Communications March 2025, Vol. 23 No. 1
    2025, 23(1):  0. 
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    Special Topic
    Special Topic on Native Intelligence at the Physical Layer
    2025, 23(1):  1-2.  doi:10.12142/ZTECOM.202501001
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    Efficient Spatio-Temporal Predictive Learning for Massive MIMO CSI Prediction
    CHENG Jiaming, CHEN Wei, LI Lun, AI Bo
    2025, 23(1):  3-10.  doi:10.12142/ZTECOM.202501002
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    Accurate channel state information (CSI) is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services. In massive multiple-input multiple-output (MIMO) systems, traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility. To address these issues, we propose a novel spatio-temporal predictive network (STPNet) that jointly integrates CSI feedback and prediction modules. STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI, which captures both the local and the global spatio-temporal features. In addition, the signal-to-noise ratio (SNR) adaptive module is designed to adapt flexibly to diverse feedback channel conditions. Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.

    RIS Enabled Simultaneous Transmission and Key Generation with PPO: Exploring Security Boundary of RIS Phase Shift
    FAN Kaiqing, YAO Yuze, GAO Ning, LI Xiao, JIN Shi
    2025, 23(1):  11-17.  doi:10.12142/ZTECOM.202501003
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    Due to the broadcast nature of wireless channels and the development of quantum computers, the confidentiality of wireless communication is seriously threatened. In this paper, we propose an integrated communications and security (ICAS) design to enhance communication security using reconfigurable intelligent surfaces (RIS), in which the physical layer key generation (PLKG) rate and the data transmission rate are jointly considered. Specifically, to deal with the threat of eavesdropping attackers, we focus on studying the simultaneous transmission and key generation (STAG) by configuring the RIS phase shift. Firstly, we derive the key generation rate of the RIS assisted PLKG and formulate the optimization problem. Then, in light of the dynamic wireless environments, the optimization problem is modeled as a finite Markov decision process. We put forward a policy gradient-based proximal policy optimization (PPO) algorithm to optimize the continuous phase shift of the RIS, which improves the convergence stability and explores the security boundary of the RIS phase shift for STAG. The simulation results demonstrate that the proposed algorithm outperforms the benchmark method in convergence stability and system performance. By reasonably allocating the weight factors for the data transmission rate and the key generation rate, “one-time pad” communication can be achieved. The proposed method has about 90% performance improvement for “one-time pad” communication compared with the benchmark methods.

    Endogenous Security Through AI-Driven Physical-Layer Authentication for Future 6G Networks
    MENG Rui, FAN Dayu, XU Xiaodong, LYU Suyu, TAO Xiaofeng
    2025, 23(1):  18-29.  doi:10.12142/ZTECOM.202501004
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    To ensure the access security of 6G, physical-layer authentication (PLA) leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters. Furthermore, the introduction of artificial intelligence (AI) facilitates the learning of the distribution characteristics of channel fingerprints, effectively addressing the uncertainties and unknown dynamic challenges in wireless link modeling. This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network (GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile users. Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy. Furthermore, this paper outlines the future development directions of PLA.

    Separate Source Channel Coding Is Still What You Need: An LLM-Based Rethinking
    REN Tianqi, LI Rongpeng, ZHAO Mingmin, CHEN Xianfu, LIU Guangyi, YANG Yang, ZHAO Zhifeng, ZHANG Honggang
    2025, 23(1):  30-44.  doi:10.12142/ZTECOM.202501005
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    Along with the proliferating research interest in semantic communication (SemCom), joint source channel coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. Nevertheless, this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding (SSCC) to enjoy a more underlying degree of freedom for optimization. We demonstrate that SSCC, after leveraging the strengths of the Large Language Model (LLM) for source coding and Error Correction Code Transformer (ECCT) complemented for channel coding, offers superior performance over JSCC. Our proposed framework also effectively highlights the compatibility challenges between SemCom approaches and digital communication systems, particularly concerning the resource costs associated with the transmission of high-precision floating point numbers. Through comprehensive evaluations, we establish that assisted by LLM-based compression and ECCT-enhanced error correction, SSCC remains a viable and effective solution for modern communication systems. In other words, separate source channel coding is still what we need.

    Exploration of NWDAF Development Architecture for 6G AI-Native Networks
    HE Shiwen, PENG Shilin, DONG Haolei, WANG Liangpeng, AN Zhenyu
    2025, 23(1):  45-52.  doi:10.12142/ZTECOM.202501006
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    Artificial intelligence (AI)-native communication is considered one of the key technologies for the development of 6G mobile communication networks. This paper investigates the architecture for developing the network data analytics function (NWDAF) in 6G AI-native networks. The architecture integrates two key components: data collection and management, and model training and management. It achieves real-time data collection and management, establishing a complete workflow encompassing AI model training, deployment, and intelligent decision-making. The architecture workflow is evaluated through a vertical scaling use case by constructing an AI-native network testbed on Kubernetes. Within this proposed NWDAF, several machine learning (ML) models are trained to make vertical scaling decisions for user plane function (UPF) instances based on data collected from various network functions (NFs). These decisions are executed through the Kubernetes API, which dynamically allocates appropriate resources to UPF instances. The experimental results show that all implemented models demonstrate satisfactory predictive capabilities. Moreover, compared with the threshold-based method in Kubernetes, all models show a significant advantage in response time. This study not only introduces a novel AI-native NWDAF architecture but also demonstrates the potential of AI models to significantly improve network management and resource scaling in 6G networks.

    Device Activity Detection and Channel Estimation Using Score-Based Generative Models in Massive MIMO
    TANG Chenyue, LI Zeshen, CHEN Zihan, YANG Howard H.
    2025, 23(1):  53-62.  doi:10.12142/ZTECOM.202501007
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    The growing demand for wireless connectivity has made massive multiple-input multiple-output (MIMO) a cornerstone of modern communication systems. To optimize network performance and resource allocation, an efficient and robust approach is joint device activity detection and channel estimation. In this paper, we present an approach utilizing score-based generative models to address the under-determined nature of channel estimation, which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems. Our experimental results, based on a comprehensive dataset generated through Monte-Carlo sampling, demonstrate the high precision of our channel estimation approach, with errors reduced to as low as -45 dB, and exceptional accuracy in detecting active devices.

    Efficient PSS Detection Algorithm Aided by CNN
    LI Lanlan
    2025, 23(1):  63-70.  doi:10.12142/ZTECOM.202501008
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    In a 5G mobile communication system, cell search is the initial step in establishing downlink synchronization between user equipment (UE) and base stations (BS). Primary synchronization signal (PSS) detection is a crucial part of this process, and enhancing PSS detection speed can reduce communication latency and improve overall quality. This paper proposes a fast PSS detection algorithm based on the correlation characteristics of PSS time-domain superposition signals. Conducting PSS signal correlation within a smaller range can reduce computational complexity and accelerates communication speed. Additionally, frequency offset can impact the accuracy of calculations during the PSS detection process. To address this issue, we propose applying convolutional neural networks (CNN) for frequency offset estimation of synchronization signals. By compensating for the frequency of related signals, the accuracy of PSS detection is improved. Finally, the analysis and simulation results demonstrate the effectiveness of the proposed approach.

    Research Papers
    A Basis Function Generation Based Digital Predistortion Concurrent Neural Network Model for RF Power Amplifiers
    SHAO Jianfeng, HONG Xi, WANG Wenjie, LIN Zeyu, LI Yunhua
    2025, 23(1):  71-77.  doi:10.12142/ZTECOM.202501009
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    This paper proposes a concurrent neural network model to mitigate non-linear distortion in power amplifiers using a basis function generation approach. The model is designed using polynomial expansion and comprises a feedforward neural network (FNN) and a convolutional neural network (CNN). The proposed model takes the basic elements that form the bases as input, defined by the generalized memory polynomial (GMP) and dynamic deviation reduction (DDR) models. The FNN generates the basis function and its output represents the basis values, while the CNN generates weights for the corresponding bases. Through the concurrent training of FNN and CNN, the hidden layer coefficients are updated, and the complex multiplication of their outputs yields the trained in-phase/quadrature (I/Q) signals. The proposed model was trained and tested using 300 MHz and 400 MHz broadband data in an orthogonal frequency division multiplexing (OFDM) communication system. The results show that the model achieves an adjacent channel power ratio (ACPR) of less than –48 dB within a 100 MHz integral bandwidth for both the training and test datasets.

    A Wide Passband Frequency Selective Surface with Angular Stability
    TANG Xingyang, SUI Jia, FU Jiahui, YANG Kaiwen, ZHAO Zhipeng
    2025, 23(1):  78-84.  doi:10.12142/ZTECOM.202501010
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    A wide passband frequency selective surface (FSS) is proposed using a five-layer stacked structure. The proposed structure applies four layers of dielectric plates and five layers of metal patches to provide a passband and exhibits more stable frequency responses and lower insertion loss under wide-angle oblique incidence compared with the typical three-layer metal-dielectric structure. According to the simulation results, the proposed FSS can achieve a passband range of 1.7–2.7 GHz with an insertion loss of less than 0.5 dB and a relative bandwidth of 44.1%, and it can preserve stable transmission characteristics with the incident angle ranging from 0° to 45°.

    Dual-Polarized 2D Beam-Scanning Antenna Based on Reconfigurable Reflective Elements
    LIU Zhipeng, LI Kexin, CAI Yuanming, LIU Feng, GUO Jiayin
    2025, 23(1):  85-89.  doi:10.12142/ZTECOM.202501011
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    In this paper, a dual-polarized antenna operating at 3.5 GHz is presented with 2D beam-scanning performance. The steerable beam is realized based on a 2×2 active reflective metasurface. The active metasurface is composed of folded annular rings and cross dipoles embedded with voltage-controlled varactor diodes. By tuning the capacitance values of the varactors, the reflective phase of the metasurface is reconfigured to tilt the main beam. To verify the scanning performance, a prototype is fabricated and measured. At 3.5 GHz, the measured scanning ranges are from -25° to 29° and -27° to 29° in the XOZ and YOZ planes, respectively.

    VFabric: A Digital Twin Emulator for Core Switching Equipment
    WANG Qianglin, ZHANG Xiaoning, YANG Yi, FAN Chenyu, YUE Yangyang, WU Wei, DUAN Wei
    2025, 23(1):  90-100.  doi:10.12142/ZTECOM.202501012
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    The proliferation of heterogeneous networks, such as the Internet of Things (IoT), unmanned aerial vehicle (UAV) networks, and edge networks, has increased the complexity of network operation and administration, driving the emergence of digital twin networks (DTNs) that create digital-physical network mappings. While DTNs enable performance analysis through emulation testbeds, current research focuses on network-level systems, neglecting equipment-level emulation of critical components like core switches and routers. To address this issue, we propose vFabric (short for virtual switch), a digital twin emulator for high-capacity core switching equipment. This solution implements virtual switching and network processor (NP) chip models through specialized processes, deployable on single or distributed servers via socket communication. The vFabric emulator can realize the accurate emulation for the core switching equipment with 720 ports and 100 Gbit/s per port on the largest scale. To our knowledge, this represents the first digital twin emulation framework specifically designed for large-capacity core switching equipment in communication networks.

    Precise Location of Passive Intermodulation in Long Cables by Fractional Frequency Based Multi-Range Rulers
    DONG Anhua, LIANG Haodong, ZHU Shaohao, ZHANG Qi, ZHAO Deshuang
    2025, 23(1):  101-106.  doi:10.12142/ZTECOM.202501013
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    A novel method is developed by utilizing the fractional frequency based multi-range rulers to precisely position the passive intermodulation (PIM) sources within radio frequency (RF) cables. The proposed method employs a set of fractional frequencies to create multiple measuring rulers with different metric ranges to determine the values of the tens, ones, tenths, and hundredths digits of the distance. Among these rulers, the one with the lowest frequency determines the maximum metric range, while the one with the highest frequency decides the highest achievable accuracy of the position system. For all rulers, the metric accuracy is uniquely determined by the phase accuracy of the detected PIM signals. With the all-phase Fourier transform method, the phases of the PIM signals at all fractional frequencies maintain almost the same accuracy, approximately 1°(about 1/360 wavelength in the positioning accuracy) at the signal-to-noise ratio (SNR) of 10 dB. Numerical simulations verify the effectiveness of the proposed method, improving the positioning accuracy of the cable PIM up to a millimeter level with the highest fractional frequency operating at 200 MHz.

    Measurement and Analysis of Radar-Cross-Section of UAV at 21–26 GHz Frequency Band
    AN Hao, LIU Ting, HE Danping, MA Yihua, DOU Jianwu
    2025, 23(1):  107-114.  doi:10.12142/ZTECOM.202501014
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    With the emergence of the 6G technology, integrated sensing and communication (ISAC) has become a hot-spot vertical application. The low-altitude scenario is considered to be a significant use case of the ISAC. However, the existing channel model is hard to meet the demands of the sensing function. The radar-cross-section (RCS) is a critical feature for the sensing part, while accurate RCS data for the typical frequency band of ISAC are still lacking. Therefore, this paper conducts measurements and analysis of the RCS data of the unmanned aerial vehicles (UAVs) under multiple poses and angles in real flying conditions. The echo from a UAV is acquired in an anechoic chamber, and the RCS values are calculated. The results of different flying attitudes are analyzed, providing RCS features for the ISAC applications.

    Doppler Rate Estimation for OTFS via Large-Scale Antenna Array
    SHAN Yaru, WANG Fanggang, HAO Yaxing, HUA Jian, XIN Yu
    2025, 23(1):  115-122.  doi:10.12142/ZTECOM.202501015
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    Orthogonal time frequency space (OTFS) can resist the Doppler effect and guarantee reliable communication in high-speed scenarios. However, the Doppler rate induced by the relative acceleration between the transmitter and receiver degrades the performance of the OTFS. So far, the impact of the Doppler rate on OTFS systems has not been addressed. In this paper, we first introduce the Doppler rate in the OTFS system and derive the delay-Doppler domain input-output relation. In addition, the impact of the Doppler rate on the effective delay-Doppler domain channel is characterized by utilizing the first mean value theorem for definite integrals to avoid complicated integrals. To mitigate the effect of the Doppler rate, a large-scale antenna array is arranged at the receiver to separate each path of the multi-path channel through a high-resolution spatial matched filter beamformer. Next, the Doppler rate estimation scheme for an arbitrary order Doppler rate is proposed based on the successive interference cancellation pattern and the maximization of the spectrum of the ratio of high-order moments between the received samples in the identified branch and the transmitted samples. Finally, the estimation accuracy of the Doppler rate and the error performance of the proposed transceiver are validated by the numerical results.