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Volume 47 期 6，2026 2026年第47卷第6期

  • Papers

    Zhang Manjun, Wang Ying, Yu Peng, Qiu Xuesong, Guo Shaoyong

    DOI：10.11959/j.issn.1000-436x.TXXB250697
    摘要：Task allocation in computing power networks faces challenges such as complex task dependencies, multi-dimensional and heterogeneous resources with uneven distribution, and difficulty in global collaboration among large-scale nodes. Traditional single agent algorithms were difficult to cope with the dynamic resource collaboration issues in wide area environments, and often relied on global state information, while existing multi-agent reinforcement learning algorithms lacked targeted modeling of task dependencies and network topology, resulting in low global collaboration efficiency. To address these issues, a distributed collaborative allocation algorithms of dependency-constrained tasks in computing power networks was proposed. First, complex subtask dependencies were processed through layering, triangular dependency decoupling and multi-resource threshold merging. Then, relation-graph attention network (RGAT) was adopted to encode multi-resource relations and topology features, combined with multi-agent soft actor-critic (MASAC) for distributed optimization. Experimental results show that in multiple scenarios, the proposed algorithms significantly optimizes task completion time and energy efficiency compared to baseline algorithms.  
    关键词：computing power network;task reconfiguration;relation-graph attention network;multi-agent reinforcement learning;task allocation   
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    更新时间：2026-06-20

    Sun Lu, Xue Rui, Zha Haoran, Lin Yun, Wang Wei

    DOI：10.11959/j.issn.1000-436x.TXXB260021
    摘要：To address the challenge of few-shot class-incremental learning (FSCIL) for specific emitter identification (SEI) with scarce samples, and the issue that existing methods lacked explicit geometric constraints, leading to new class embeddings being easily confused with those of old classes, a method for FSCIL based on orthogonal space constraints was proposed. Firstly, a set of mutually orthogonal pseudo-target vectors was introduced as structured prior knowledge, and the lower and upper bounds of their quantity were theoretically derived. Next, a collaborative optimization strategy based on orthogonal pseudo-target vectors was proposed, so as to impose geometric constraints on new class representation directions and guide the feature extractor to reserve expandable representation directions in the embedding space. Finally, a classifier weight calibration strategy was designed to quantify the misclassification risk of new class samples during the decision process, using high-risk samples to enhance the decision boundaries. Experimental results on the ADS-B and Wi-Fi datasets show that, the proposed method outperforms other benchmark methods, especially in the extreme case where only one incremental training sample is available.  
    关键词：specific emitter identification;few-shot class-incremental learning;orthogonal space constraint;classifier weight calibration   
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    更新时间：2026-06-20

    Feng Qiyue, Tang Tao, Zhang Yunpu, Wang Ding, Wu Zhidong

    DOI：10.11959/j.issn.1000-436x.TXXB260042
    摘要：In the process of real-time shortwave signal detection and localization, problems such as data association and error propagation are frequently observed, by which a mismatch between detection and localization results is easily induced, and the timely output of outcomes is impeded. To address the above issues, an integrated shortwave signal detection and localization method based on multi-agent proximal policy optimization was proposed. By constructing a reinforcement learning environment using shortwave signal time-frequency diagrams, spectral characteristics of shortwave signals were effectively captured. A filtering-window agent with hybrid action space was designed for adaptive signal selection. Furthermore, localization error ellipse probability, signal matching degree, and multi-agent collaboration strategies were incorporated into the reward function design. Optimal policies were explored by the optimized multi-agent proximal policy optimization to achieve autonomous shortwave signal detection and localization. Simulation results demonstrate that compared with baseline algorithms, the proposed method reduces average recognition-localization time by 0.12 s while improving accuracy by 22%, thereby providing a novel solution for autonomous cooperative target detection and localization in complex electromagnetic environments.  
    关键词：Shortwave signal detection and localization;Multi-Agent Proximal Policy Optimization;reinforcement learning;Hybrid action space;localization error ellipses probability   
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    更新时间：2026-06-20

    Huang Ziyi, Li Guoquan, Lin Jinzhao, Pang Yu, Wu Ruiheng

    DOI：10.11959/j.issn.1000-436x.TXXB260143
    摘要：Accurate channel estimation is critical for the reliable deployment of orthogonal time frequency space (OTFS) systems in 6G high-mobility scenarios. To further improve the channel estimation accuracy in OTFS systems, a channel estimation algorithm integrating the basis expansion model (BEM) and a bidirectional long short-term memory (LSTM) network was proposed. Firstly BEM was used to reformulate the channel estimation problem in the delay-Doppler (DD) domain into a basis coefficient estimation problem. Then a bidirectional LSTM network was introduced for offline training and online prediction of the basis coefficients, and a self-attention (SA) mechanism was incorporated to strengthen global perception of temporal features, enabling high-accuracy estimation and dynamic tracking of the basis coefficients. Building on that, a low-complexity two-stage equalizer was further designed. Channel information was reconstructed from the estimated basis coefficients, multipath effects were mitigated via a single-tap equalizer, and a log likelihood ratio based iterative interference cancellation (LLR-IIC) algorithm was employed in the DD domain to suppress residual interference induced by Doppler spread. Simulation results demonstrate that the proposed algorithm offers significant advantages in channel estimation accuracy, generalization capability, and complexity. When combined with the proposed equalizer, superior bit error rate (BER) performance is achieved while maintaining low complexity.  
    关键词：channel estimation;OTFS;high-mobility scenario;BEM;bidirectional LSTM;two-stage equalizer   
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    更新时间：2026-06-20

    Zeng Jielun, Chen Jing, He Kun, Jia Meng, Lyu Lanxi, Du Ruiying

    DOI：10.11959/j.issn.1000-436x.TXXB260133
    摘要：To address the low tally-verification efficiency and insufficient support for diverse voting modes identified in electronic voting scenarios, an anonymous and accountable aggregate ring signature algorithm supporting multiple voting modes was proposed. The proposed construction was designed to preserve voter privacy while ensuring the legitimacy of voting operations. Ring signature technology was employed to enable decentralized setup and hide verifier identities, reducing the risk of tracing or manipulation during verification, while an aggregation mechanism was introduced to compress multiple ballots into compact verification objects to improve tally-verification efficiency.To regulate the number of votes cast by the same signer, a linking authority was introduced together with linkable tags, enabling support for both single-vote and multiple-vote settings. Security analysis and experimental results show that the proposed algorithm achieves a good balance between anonymous regulation and efficiency, and reduces the time overhead in the linking phase by about 98.29% on average compared with the k-LRS scheme.  
    关键词：electronic voting;ring signature;identity hiding;linkability   
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    更新时间：2026-06-20
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Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications、Posts & Telecom Press
Editor-in-Chief: Zhang Ping
Associate Editor-in-Chief:
Zhang Yanchuan, Ma Jianfeng, Yang Zhen, Shen Lianfeng, Tao Xiaofeng, Liu Hualu
Editorial Director: Wu Nada, Zhao Li
Address: F2, Beiyang Chenguang Building, Shunbatiao No.1 Courtyard, Fengtai District, Beijing, China
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ISSN(print) 1000-436X
ISSN(online) 2097-5562
CN 11-2102/TN

 

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