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lssue 11, 2025
Editors-in-Chief
Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 2095-9184
CN 33-1389/TP
IF (2024 JCR) 2.9
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Volume 26 Issue 11，2025
Regular Papers
AI-agent communication network for 6G: vision, architecture, and key technologies Enhanced Publication AI Introduction
“AI-agent communication network (ACN) research progresses in the field of 6G mobile networks. Expert researchers established the ACN system, which provides solutions to solve the challenges of AI agents in 6G networks and lays a foundation for the construction of the ACN system.”Abstract：The booming of artificial intelligence (AI) agents has brought about promising business scenarios for sixth-generation (6G) mobile networks, while simultaneously posing significant challenges to network functionalities and infrastructure. These AI agents can be deployed on end devices (e.g., intelligent robots and intelligent cars) or as digital entities (e.g., personal AI assistants). As novel service entities with autonomous decision-making and task execution capabilities, AI agents introduce potential risks of uncontrollable actions and privacy disclosures. AI agents also require new 6G capabilities beyond traditional communication, including multimodality information interaction (e.g., AI models and tokens) and support for service requirements (e.g., computing and sensing of data). In this article, we introduce the concept of AI-agent communication network (ACN), a new paradigm to enable global information interaction and on-demand capability provisioning for single or multiple AI agents. We first introduce the vision and architectural framework of ACN. Then, key technologies and future research directions related to ACN are discussed. Furthermore, we provide potential use cases to elaborate on how ACN can expand the service capabilities of 6G networks.Keywords：Artificial intelligence agent;Sixth-generation mobile networks;Network architecture;Multimodality interaction;Multi-agent coordination20|13|0Updated：2026-01-07Regular Papers
Space–time processing for inflight broadband connectivity: critical analysis, challenges, and future directions Enhanced Publication AI Introduction
“Inflight broadband connectivity research progresses, with experts exploring space-time processing techniques to enhance airborne network capacity and reliability. This study provides insights into enabling IFC, discussing theoretical foundations, challenges, and future research directions.”Abstract：Inflight broadband connectivity (commonly termed as inflight connectivity) can be considered one of the remaining milestones for ubiquitous Internet provision; therefore, several enabling technologies are being investigated to provide high-capacity, reliable, and affordable Internet access. Multiple-input multiple-output (MIMO), based on the space–time processing (STP) concept, is one of the dominant technologies that consistently appear on the list of inflight connectivity (IFC) enablers. STP shows the potential to significantly increase user throughput, improve spectral/energy efficiencies, and increase the capacity as well as reliability of airborne networks through spatial multiplexing/diversity techniques. This article presents the preliminary outcomes of substantial research on STP techniques for enabling IFC, as the exploratory study on this topic is still in its early stages. We explore the theoretical principles behind different STP techniques and their implementation in airborne networks in direct air-to-ground (A2G) scenarios for the provision of a reliable and high-speed IFC. We also analyze the current technologies and techniques used for IFC and highlight their benefits and limitations. We present a comprehensive review that compares different STP techniques using metrics such as bit error rate (BER), spectral efficiency (SE), and capacity. Last, but not least, we discuss the substantial research challenges encountered and the prospective future research avenues that require special attention for enhancing the deployment of STP systems in forthcoming airborne networks, particularly for enabling IFC. Overall, this research study contributes to the body of knowledge by providing insights into the use of STP techniques in airborne networks for enabling IFC. It emphasizes the theoretical foundations, presents a literature review, discusses challenges and limitations, identifies potential areas for future research, and provides a performance analysis.Keywords：Airborne Internet access;Inflight broadband connectivity;Multiple-input multiple-output (MIMO);Precoding;Beamforming;Direct air-to-ground communication (DA2GC);Space–time processing12|10|0Updated：2026-01-07Regular Papers
Privacy-preserving bipartite consensus with cooperative–competitive interactions via a node decomposition strategy Enhanced Publication AI Introduction
“In the realm of multi-agent systems, a groundbreaking study has emerged, addressing the privacy protection issue within cooperative-competitive networks. The research introduces a node decomposition strategy to safeguard initial node values, effectively shielding them from both honest-but-curious nodes and eavesdroppers. This innovation paves the way for a privacy-preserving consensus algorithm, ensuring privacy performance is maintained without external algorithmic support. The study's findings are bolstered by two numerical simulations, underscoring the efficacy of the proposed algorithm in achieving bipartite consensus.”Abstract：This paper describes our investigation of the privacy protection problem of multi-agent systems under cooperative–competitive networks. A node decomposition strategy is used to protect the privacy of the initial node values, in which a node vi is split into ni nodes. By designing inter-node weights, the initial value of each node is protected from honest-but-curious nodes and eavesdroppers without relying on external algorithms. The purpose is to design a privacy-preserving consensus algorithm such that the privacy performance is guaranteed by using the node decomposition strategy, while the bipartite consensus is achieved for the cooperative–competitive multi-agent systems. Two numerical simulations are given to validate the effectiveness of the proposed privacy-preserving bipartite consensus algorithm.Keywords：Privacy-preserving;Bipartite consensus;Cooperative–competitive interactions;Multi-agent systems;Node decomposition8|7|0Updated：2026-01-07Regular Papers
Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency control Enhanced Publication AI Introduction
“In the field of load frequency control, this study introduces its research progress. Expert developed the DRL-based adaptive controller system, which provides solutions to enhance the robustness of control systems under adversarial attacks.”Abstract：Load frequency control (LFC) is usually managed by traditional proportional–integral–derivative (PID) controllers. Recently, deep reinforcement learning (DRL)-based adaptive controllers have been widely studied for their superior performance. However, the DRL-based adaptive controller exhibits inherent vulnerability due to adversarial attacks. To develop more robust control systems, this study conducts a deep analysis of DRL-based adaptive controller vulnerability under adversarial attacks. First, an adaptive controller is developed based on the DRL algorithm. Subsequently, considering the limited capability of attackers, the DRL-based LFC is evaluated under adversarial attacks using the zeroth-order optimization (ZOO) method. Finally, we use adversarial training to enhance the robustness of DRL-based adaptive controllers. Extensive simulations are conducted to evaluate the performance of the DRL-based PID controller with and without adversarial attacks.Keywords：Adaptive controller;Deep reinforcement learning;Load frequency control;Adversarial attacks10|10|0Updated：2026-01-07Regular Papers
Parallel prototype filter and feature refinement for few-shot medical image segmentation AI Introduction
“In the field of few-shot medical image segmentation, PPFFR introduces its research progress. Expert researchers established the PPFFR system, which provides solutions to solve the problems of inter-class variation and computational complexity, opening up a new direction for few-shot learning research and laying a foundation for the construction of efficient medical image segmentation systems.”Abstract：Medical image segmentation is critical for clinical diagnosis, but the scarcity of annotated data limits robust model training, making few-shot learning indispensable. Existing methods often suffer from two issues—performance degradation due to significant inter-class variations in pathological structures, and overreliance on attention mechanisms with high computational complexity (O(n²)), which hinders the efficient modeling of long-range dependencies. In contrast, the state space model (SSM) offers linear complexity (O(n)) and superior efficiency, making it a key solution. To address these challenges, we propose PPFFR (parallel prototype filter and feature refinement) for few-shot medical image segmentation. The proposed framework comprises three key modules. First, we propose the prototype refinement (PR) module to construct refined class subgraphs from encoder-extracted features of both support and query images, which generates support prototypes with minimized inter-class variation. We then propose the parallel prototype filter (PPF) module to suppress background interference and enhance the correlation between support and query prototypes. Finally, we implement the feature refinement (FR) module to further enhance segmentation accuracy and accelerate model convergence with SSM’s robust long-range dependency modeling capability, integrated with multi-head attention (MHA) to preserve spatial details. Experimental results on the Abd-MRI dataset demonstrate that FR with MHA outperforms FR alone in segmenting the left kidney, right kidney, liver, and spleen, and in terms of mean accuracy, confirming MHA’s role in improving precision. In extensive experiments conducted on three public datasets under the 1-way 1-shot setting, PPFFR achieves Dice scores of 87.62%, 86.74%, and 79.71% separately, consistently surpassing state-of-the-art few-shot medical image segmentation methods. As the critical component, SSM ensures that PPFFR balances performance with efficiency. Ablation studies validate the effectiveness of the PR, PPF, and FR modules. The results indicate that explicit inter-class variation reduction and SSM-based feature refinement can enhance accuracy without heavy computational overhead. In conclusion, PPFFR effectively enhances inter-class consistency and computational efficiency for few-shot medical image segmentation. This work provides insights for few-shot learning in medical imaging and inspires lightweight architecture designs for clinical deployment.Keywords：Few-shot learning;Medical image segmentation;Prototype filter;State space model6|8|0Updated：2026-01-07Regular Papers
HADF: a hash-adaptive dual fusion implicit network for super-resolution of turbulent flows Enhanced Publication AI Introduction
“In the field of fluid dynamics, a breakthrough has been made in reconstructing high-fidelity turbulence flow representations from sparse measurements. Expert researchers developed the HADF system, which provides solutions to address the challenges of high computational costs and limitations in multi-scale reconstruction.”Abstract：Turbulence, a complex multi-scale phenomenon inherent in fluid flow systems, presents critical challenges and opportunities for understanding physical mechanisms across scientific and engineering domains. Although high-resolution (HR) turbulence data remain indispensable for advancing both theoretical insights and engineering solutions, their acquisition is severely limited by prohibitively high computational costs. While deep learning architectures show transformative potential in reconstructing high-fidelity flow representations from sparse measurements, current methodologies suffer from two inherent constraints: strict reliance on perfectly paired training data and inability to perform multi-scale reconstruction within a unified framework. To address these challenges, we propose HADF, a hash-adaptive dynamic fusion implicit network for turbulence reconstruction. Specifically, we develop a low-resolution (LR) consistency loss that facilitates effective model training under conditions of missing paired data, eliminating the conventional requirement for fully matched LR and HR datasets. We further employ hash-adaptive spatial encoding and dynamic feature fusion to extract turbulence features, mapping them with implicit neural representations for reconstruction at arbitrary resolutions. Experimental results demonstrate that HADF achieves superior performance in global reconstruction accuracy and local physical properties compared to state-of-the-art models. It precisely recovers fine turbulence details for partially unpaired data conditions and diverse resolutions by training only once while maintaining robustness against noise.Keywords：Turbulence reconstruction;Deep learning;Unpaired data;Low-resolution consistency loss;Hash-adaptive spatial encoding;Dynamic feature fusion;Implicit neural representations14|10|0Updated：2026-01-07
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P1 Speech by Academician Baoyan Duan
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P2 Speech by Professor Min Sheng, Xidian University
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P3 Speech by Professor Yunsong Li, Xidian University
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