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Table of Content

    17 March 2026, Volume 24 Issue 1
    To the Communications Community —2026 New Year’s Message
    Zhang Ping
    2026, 24(1):  1-1.  doi:10.12142/ZTECOM.202601001
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    Special Topic
    Special Topic on Achievements of ZTE’s Industry-University-Institute Cooperation Projects
    Xu Chengzhong
    2026, 24(1):  2-3.  doi:10.12142/ZTECOM.202601002
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    Deep CSI Compression and Feedback for Massive MIMO: A Survey
    Lu Zhaohua, Yi Chenyang, Wu Jie, Shao Bo, Xu Wei
    2026, 24(1):  4-15.  doi:10.12142/ZTECOM.202601003
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    To achieve the potential performance gain of massive multiple-input multiple-output (MIMO) systems, base stations (BS) require downlink channel state information (CSI) fed back by users to execute beamforming design, especially in the frequency division duplex (FDD) systems. However, due to the enormous number of antennas in massive MIMO systems, the feedback overhead of downlink CSI acquisition is extremely large. To address this issue, deep learning (DL) techniques have been introduced to develop high-accuracy feedback strategies under limited backhaul constraints. In this paper, we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems. Specifically, we introduce the conventional CSI compression and feedback schemes and the existing problems. Besides, we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques. We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback. In addition, we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.

    Low-Complexity OTFS Channel Equalization Based on CLU-MMSE
    Jia Haoxiang, Zhao Danfeng, Xin Yu, Hua Jian
    2026, 24(1):  16-24.  doi:10.12142/ZTECOM.202601004
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    In view of the high computational complexity of traditional linear equalization algorithms in Orthogonal Time Frequency Space (OTFS) systems, a minimum mean square error (MMSE) channel equalization algorithm based on Matrix Chunking Lower and Upper Triangular Decomposition (CLU) is proposed. The proposed algorithm derives the structural properties of the chunked MMSE equalization matrix by leveraging the block diagonal structure of the Cyclic Prefix OTFS (CP-OTFS) time-domain channel matrix and the quasi-band structure of its constituent block matrices. On this basis, triangular decomposition combined with forward and backward substitution is used to avoid matrix inversion. This approach significantly reduces the complexity of the MMSE algorithm without sacrificing its performance.

    Carrier Frequency Offset Based Robust Radio Frequency Fingerprint for OFDM Communication in Time-Varying Channels
    Liu Gengyi, Pan Yijin, Wang Junbo, Chen Yijian, Yu Hongkang
    2026, 24(1):  25-33.  doi:10.12142/ZTECOM.202601005
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    The radio frequency (RF) fingerprint technique is a robust method for security enhancement of the physical layer by leveraging the unique RF imperfections inherent in various wireless devices. Among these imperfections, the carrier frequency offset (CFO) stands out as a primary RF fingerprint (RFF) of the transmitter, offering the potential to distinguish among different transmitters. However, accurately estimating CFO in time-varying channels poses significant challenges due to multipath effects and Doppler shifts. In this paper, we focus on estimating CFO for wireless device identification in the orthogonal frequency division multiplexing (OFDM) communication system. To achieve precise CFO estimation under time-varying channels, we propose a frequency domain correlation and spline interpolation (FCSI) algorithm. This approach utilizes pilots distributed across different subcarriers to correlate with prior local sequences, facilitating accurate CFO estimation. Classification is then performed based on the Euclidean distance between the prior RFF and the tested RFF dataset. Simulation results demonstrate that the proposed M-consecutive average method effectively reduces the classification error rate in the challenging high-frequency (HF) skywave channel environment.

    Key Technologies for AI-Driven Network Traffic Classification Workflow and Data Distribution Shift
    Zhao Jianchao, Geng Zhaosen, Li Zeyi, Wang Pan
    2026, 24(1):  34-44.  doi:10.12142/ZTECOM.202601006
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    With the evolution of next-generation network technologies, the complexity of network management has significantly increased, and the means of network attacks are diversified, bringing new challenges to network traffic classification. This paper presents a general AI-driven network traffic classification workflow and elaborates on a traffic data and feature engineering framework. Most importantly, it analyzes the concept and causes of data distribution shifts in network traffic, proposing detection methods and countermeasures. Experimental results on real traffic collected at different time intervals show that application evolution can induce data distribution shifts, which in turn lead to a noticeable degradation in traffic classification performance. Comparative drift detection experiments further confirm that such shifts are more evident over long-term intervals, while short-term traffic remains relatively stable. These findings demonstrate the necessity of incorporating drift-aware mechanisms into AI-driven network traffic classification systems.

    Efficient and Secure Data Storage in 5G Industrial Internet Collaborative Systems
    Wang Jigang, Liu Dong, Wan Changsheng, Lu Ping
    2026, 24(1):  45-55.  doi:10.12142/ZTECOM.202601007
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    Security and access control for data storage in 5G industrial Internet collaborative systems are facing significant challenges. The characteristics of 5G networks, such as low latency and high speed, facilitate data transmission in the industrial Internet but also increase vulnerability to attacks like theft and tampering. Moreover, in 5G industrial Internet collaborative system environments, data flows across multiple entities and links, which necessitates a flexible access control model to meet specific data access requirements. Traditional role-based and attribute-based access control mechanisms are difficult to apply in such dynamic application scenarios. To address these challenges, we propose a novel data storage solution for 5G industrial Internet collaborative systems. Similar to existing approaches, it provides integrity and confidentiality protection for transmitted data. In terms of security, only authenticated data owners and users can obtain file decryption keys, preventing malicious attackers from data forgery. Regarding access control, decryption is permitted only to authorized data users, safeguarding against unauthorized file access. Furthermore, by introducing an attribute-based encryption mechanism, only data users with specific attributes can decrypt files. In terms of efficiency, our approach utilizes bilinear and modular exponentiation operations solely during the authentication process. For handling substantial data loads, lightweight cryptographic algorithms are employed. Consequently, our solution achieves higher efficiency compared with other known methods. Experimental results demonstrate the feasibility of our approach in real-world applications.

    Complexity-Reduced Equalization for 200 Gbit/s PON Downstream Systems Based on SSB Modulation and Direct Detection
    Yang Tao, Huang Xingang, Ma Zhuang, Zhong Yiming, Huang Xiatao, Liu Bo
    2026, 24(1):  56-64.  doi:10.12142/ZTECOM.202601008
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    The 200 Gbit/s passive optical network (PON) is most likely to be the next-generation scheme following 50G PON. The cost-effective direct detection (DD) system is the economical choice. However, larger-capacity DD systems will face much more serious power fading caused by chromatic dispersion (CD) combined with square-law DD and thereby significantly increases the complexity of equalization algorithms. In this paper, a 200 Gbit/s Nyquist 4-level pulse amplitude modulation (PAM4) single side-band (SSB) modulation-DD downlink scheme is designed, and a low complexity quadratic-nonlinear equalizer is proposed for this system. The computational complexity of the quadratic nonlinear equalizer is about 28% of that of the conventional Volterra nonlinear equalizer, while still exhibiting excellent nonlinear equalization ability. Simulation results for the 200 Gbit/s system with 20 km fiber transmission show that it can achieve a power budget of 29 dB, while a 30.4 dB power budget is obtained in the 50 Gbit/s experimental transmission.

    Enhancing Code Quality with LLM in Software Static Analysis
    Niu Zhi, Dong Luming
    2026, 24(1):  65-71.  doi:10.12142/ZTECOM.202601009
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    In the modern era of ubiquitous and highly interconnected information technology, cybersecurity threats stemming from software code vulnerabilities have become increasingly severe, posing significant risks to the confidentiality, integrity, and availability of modern information systems. To enhance software code quality, enterprises often integrate static code analysis tools into Continuous Integration (CI) pipelines. However, the high rates of false positives and false negatives remain a challenge. The advent of large language models (LLMs), such as ChatGPT, presents a new opportunity to address these challenges. In this paper, we propose AI-SCDF, a framework that utilizes the custom-built Nebula-Coder AI model for detecting and fixing code security issues in real time during the developer’s personal build process. We construct a static code checking rule knowledge base through summarizing and classifying Common Weakness Enumeration (CWE) code security problems identified by security and quality assurance teams. The rule knowledge base is combined with CodeFuse-processed code contexts to serve as input for an AI code security detection microservice, which assists in identifying code quality and security issues. If any abnormalities are detected, they are addressed by an AI code security patching microservice, which alerts the developer and requests confirmation before committing the code into the repository. Experimental results show that our approach effectively improves code quality. We also develop a VSCode plugin for code alert detection and fix based on LLMs, which facilitates test shift-left and lowers the risk of software development.

    AED-NeRF: Audio-Driven and Emotion- Editing Dynamic Neural Radiance Fields for Expressive Talking Face Avatar
    Lu Ping, Song Li, Shi Wenzhe, Lin Zonghao, Ling Jun
    2026, 24(1):  72-80.  doi:10.12142/ZTECOM.202601010
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    While neural radiance field (NeRF) methods have shown promising results in generating talking faces, existing studies primarily focus on the correlation between avatars and driving sources. However, these studies often overlook emotion modeling, resulting in the generation of emotionless or unnatural facial animations. In response, this paper introduces an audio-driven and emotion-editing dynamic NeRF (AED-NeRF) approach, designed for the real-time generation of expressive talking face avatars driven by audio inputs. Specifically, we integrate audio features into a grid-based NeRF to compensate for the lack of a deformation channel, successfully capturing lip dynamics and enabling end-to-end generation from audio-driven sources to talking face avatars. Emotion labels, comprising emotion categories and intensity levels, guide the proposed NeRF framework to implicitly model visual emotions, allowing for explicit control and editing of facial expressions. Extensive qualitative and quantitative experiments validate the effectiveness and advantages of our proposed method, demonstrating its ability to achieve real-time, photo-realistic talking face avatar generation across different audio and emotion scenarios.

    Steel Surface Anomaly Detection Using 3D Depth and 2D RGB Features
    Zheng Wangguandong, Lu Ping, Deng Fangwei, Huang Shijun, Xia Siyu
    2026, 24(1):  81-87.  doi:10.12142/ZTECOM.202601011
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    The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment, processes, and steel characteristics. To alleviate the problem, this paper proposes a detection and localization method combining 3D depth and 2D RGB features. The framework comprises three stages: defect classification, defect location, and warpage judgment. The first stage uses a data-efficient image Transformer model, the second stage utilizes reverse knowledge distillation, and the third stage performs feature fusion using 3D depth and 2D RGB features. Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility, and can be effectively used in industrial scenarios.

    Synthesis and Design of Generalized Strongly Coupled Resonator Quartet Combline Filters with Redundant Resonance
    Xiong Zhi’ang, Fan Jiyuan, Zhao Ping, Zhou Jinzhu, Shen Nan, Wu Qingqiang
    2026, 24(1):  88-96.  doi:10.12142/ZTECOM.202601012
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    This article proposes a generalized strongly coupled resonator quartet (GSCRQ) filter along with its synthesis approach. By introducing out-of-band reflection zeros (RZs), the proposed GSCRQ can generate a transmission zero on each side of the passband without negative couplings. The coupling coefficients in this coupling structure change with the positions of the out-of-band RZs. Thus, the GSCRQ configuration admits flexible design solutions. For GSCRQ coaxial combline filters, all couplings can be implemented as inductive couplings, simplifying the design and manufacturing process. In this article, a 6-2 filter in the GSCRQ configuration is synthesized and designed. The simulated results of the designed filter agree very well with the theoretical characteristics.

    Modern Graphics APIs: Design Principles, A Use Case, and New Perspectives
    Lu Ping, Sun Qi, Wang Chen, Guo Jie, Guo Yanwen, Shi Wenzhe
    2026, 24(1):  97-106.  doi:10.12142/ZTECOM.202601013
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    In this paper, we provide a comprehensive examination of the evolution of graphics Application Programming Interfaces (APIs). We begin by exploring traditional graphics APIs, elucidating their distinct features and inherent challenges. This sets the stage for a detailed exploration of modern graphics APIs, with a focus on four critical design principles. These principles are further analyzed through specific case studies and categorical examinations. The paper then introduces MoerEngine, a bespoke rendering engine, as a practical case to demonstrate the real-world application of these modern principles in software engineering. In conclusion, the study offers insights into the potential future trajectory of graphics APIs, spotlighting emerging design patterns and technological innovations. It also ventures to predict the development trends and capabilities of next-generation graphics APIs.