IEEE journal on selected areas in communications : a publication of the IEEE Communications Society最新文献_第2页

Robust and Secure STAR-RIS-Assisted UAV Communications for Multi-User and Multi-Eavesdropper 星- ris辅助无人机多用户多窃听通信鲁棒安全

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-03-02 DOI: 10.1109/JSAC.2026.3669466

Xiaojie Wang;Jiaxin Ren;Zhaolong Ning;Yihang Huang;Xiaoming Tao;Lei Guo;Yan Zhang

{"title":"Robust and Secure STAR-RIS-Assisted UAV Communications for Multi-User and Multi-Eavesdropper","authors":"Xiaojie Wang;Jiaxin Ren;Zhaolong Ning;Yihang Huang;Xiaoming Tao;Lei Guo;Yan Zhang","doi":"10.1109/JSAC.2026.3669466","DOIUrl":"10.1109/JSAC.2026.3669466","url":null,"abstract":"Enabled by 6G wireless technologies, Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) create a new dimension for optimizing performance in Uncrewed Aerial Vehicle (UAV) communications through full-space signal coverage. However, existing research on STAR-RIS-assisted UAV secure communications still faces critical challenges, including the reliance on ideal Channel State Information (CSI) assumptions, amplitude optimization complexity under mode switching protocols, and limited scalability to meet multi-user communication demands. To address these challenges, we propose a robust and secure STAR-RIS-assisted UAV communication approach for a multi-user and multi-eavesdropper scenario. By jointly optimizing user scheduling, transmitting and reflecting coefficients of STAR-RIS, transmit power and flight trajectory of UAV, we aim to maximize the average worst-case achievable secrecy rate. To tackle the non-convexity and coupled decision variables of the formulated problem, we propose an alternating optimization framework, with a Lagrange multiplier method for power allocation, a deterministic model reformulated via S-procedure for CSI uncertainty quantification and robust handling, and a penalty-based double-loop iterative algorithm forcing the phase-shift matrix toward a rank-one solution. Finally, theoretical analysis and simulation results validate the superior secrecy performance of the proposed algorithm over other representative algorithms.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3721-3736"},"PeriodicalIF":17.2,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GNN-Based Secrecy Rate Optimization in Multi-Satellite Collaborative Systems 基于gnn的多卫星协同系统保密率优化

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-03-02 DOI: 10.1109/JSAC.2026.3669126

Xuyang Zhang;Zhen Liu;Zizheng Hua;Xuanhe Yang;Shuai Wang;Gaofeng Pan;Dusit Niyato

{"title":"GNN-Based Secrecy Rate Optimization in Multi-Satellite Collaborative Systems","authors":"Xuyang Zhang;Zhen Liu;Zizheng Hua;Xuanhe Yang;Shuai Wang;Gaofeng Pan;Dusit Niyato","doi":"10.1109/JSAC.2026.3669126","DOIUrl":"10.1109/JSAC.2026.3669126","url":null,"abstract":"Next-generation satellite systems require efficient collaboration in terms of wide area coverage and signal augmentation, enabling intelligent allocation of available wireless resources to ensure the security of information. Meanwhile, machine learning (ML) is widely considered well-suited to massive, real-time data scenarios in satellite communication networks, and graph neural network (GNN) is a specific branch for processing the irregular data within such networks. In this paper, we propose physical layer security for a multi-satellite collaborative (MSC) system involving LEO satellites, users, and eavesdroppers. Specifically, the GNN-based security communication of the MSC (G-MSC-SC) architecture is designed to maximize the secrecy rate. Since heterogeneous and isomorphic methods can effectively solve multi-type node mapping and complex communication problems, the G-MSC-SC architecture is divided into two steps: A heterogeneous graph pruning attention coefficient network (HGPAN) and an isomorphic graph eavesdropper as an auxiliary node network (IGEAN). In the HGPAN architecture, different types of device nodes are embedded in the same dimensional space, addressing the challenge of matching LEO satellites to users. The IGEAN architecture maps user channel state information (CSI) to beamforming (BF) vectors through attention aggregation and an improved loss function. Moreover, the corresponding conventional optimization algorithms are designed as test and comparison baselines. Simulation results show that 1) the G-MSC-SC architecture outperforms neural networks and heuristic algorithms in terms of accuracy and efficiency; 2) as the numbers of users and virtual eavesdroppers increase, the directional alignment between the BF vectors and the LEO satellite-user channels shows an improvement; and 3) with imperfect CSI, the G-MSC-SC architecture still achieves an excellent balance between user secrecy rate and communication rate.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3648-3663"},"PeriodicalIF":17.2,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks 针对对抗性攻击的扩散辅助安全深度联合源信道编码

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-03-02 DOI: 10.1109/JSAC.2026.3669143

Changyuan Zhao;Jiacheng Wang;Ruichen Zhang;Dusit Niyato;Hongyang Du;Zehui Xiong;Dong In Kim;Ping Zhang

{"title":"SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks","authors":"Changyuan Zhao;Jiacheng Wang;Ruichen Zhang;Dusit Niyato;Hongyang Du;Zehui Xiong;Dong In Kim;Ping Zhang","doi":"10.1109/JSAC.2026.3669143","DOIUrl":"10.1109/JSAC.2026.3669143","url":null,"abstract":"Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as an unknown-operator inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3705-3720"},"PeriodicalIF":17.2,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analytical Bounds for Decoy-State Quantum Key Distribution With Discrete Phase Randomization 离散相位随机化诱饵态量子密钥分配的解析界

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-24 DOI: 10.1109/JSAC.2026.3667484

Zhaohui Liu;Ahmed Lawey;Mohsen Razavi

引用次数: 0

Residual Diffusion Models for Variable-Rate Joint Source–Channel Coding of MIMO CSI MIMO CSI变速率联合源信道编码的剩余扩散模型

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-20 DOI: 10.1109/JSAC.2026.3666741

Sravan Kumar Ankireddy;Heasung Kim;Joonyoung Cho;Hyeji Kim

{"title":"Residual Diffusion Models for Variable-Rate Joint Source–Channel Coding of MIMO CSI","authors":"Sravan Kumar Ankireddy;Heasung Kim;Joonyoung Cho;Hyeji Kim","doi":"10.1109/JSAC.2026.3666741","DOIUrl":"10.1109/JSAC.2026.3666741","url":null,"abstract":"Despite significant advancements in deep learning-based CSI compression, some key limitations remain unaddressed. Current approaches predominantly treat CSI compression as a source-coding problem, thereby neglecting transmission errors. Conventional separate source and channel coding suffers from the cliff effect, leading to significant deterioration in reconstruction performance under challenging channel conditions. While existing autoencoder-based compression schemes can be readily extended to support joint source-channel coding, they struggle to capture complex channel distributions and exhibit poor scalability with increasing parameter count. To overcome these inherent limitations of autoencoder-based approaches, we propose Residual-Diffusion Joint Source-Channel Coding (RD-JSCC), a novel framework that integrates a lightweight autoencoder with a residual diffusion module to iteratively refine CSI reconstruction. Our flexible decoding strategy balances computational efficiency and performance by dynamically switching between low-complexity autoencoder decoding and sophisticated diffusion-based refinement based on channel conditions. Comprehensive simulations demonstrate that RD-JSCC significantly outperforms existing autoencoder-based approaches in challenging wireless environments. Furthermore, RD-JSCC offers several practical features, including a low-latency 2-step diffusion during inference, support for multiple compression rates with a single model, robustness to fixed-bit quantization, and adaptability to imperfect channel estimation.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3620-3633"},"PeriodicalIF":17.2,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-Free Massive MIMO Systems With Reciprocity Calibration 互易校准毫米波无蜂窝大规模MIMO系统双向相位相干传输与传感实验性能

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-16 DOI: 10.1109/JSAC.2026.3664828

Qingji Jiang;Jing Jin;Qixing Wang;Yuanyuan Tang;Yang Cao;Bin Kuang;Jing Dong;Siying Lv;Dongming Wang;Yongming Huang;Jiangzhou Wang;Xiaohu You

{"title":"Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-Free Massive MIMO Systems With Reciprocity Calibration","authors":"Qingji Jiang;Jing Jin;Qixing Wang;Yuanyuan Tang;Yang Cao;Bin Kuang;Jing Dong;Siying Lv;Dongming Wang;Yongming Huang;Jiangzhou Wang;Xiaohu You","doi":"10.1109/JSAC.2026.3664828","DOIUrl":"10.1109/JSAC.2026.3664828","url":null,"abstract":"Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3591-3607"},"PeriodicalIF":17.2,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Activity Detection and Channel Estimation for Massive Connectivity: Where Message Passing Meets Score-Based Generative Priors 大规模连接的联合活动检测和信道估计：消息传递满足基于分数的生成先验

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-10 DOI: 10.1109/JSAC.2026.3663274

Chang Cai;Wenjun Jiang;Xiaojun Yuan;Ying-Jun Angela Zhang

{"title":"Joint Activity Detection and Channel Estimation for Massive Connectivity: Where Message Passing Meets Score-Based Generative Priors","authors":"Chang Cai;Wenjun Jiang;Xiaojun Yuan;Ying-Jun Angela Zhang","doi":"10.1109/JSAC.2026.3663274","DOIUrl":"10.1109/JSAC.2026.3663274","url":null,"abstract":"Massive connectivity supports the sporadic access of a vast number of devices without requiring prior permission from the base station (BS). In such scenarios, the BS must perform joint activity detection and channel estimation (JADCE) prior to data reception. Message-passing algorithms have emerged as a prominent solution for JADCE under a Bayesian inference framework. The existing message-passing algorithms, however, typically rely on some hand-crafted and overly simplistic priors of the wireless channel, leading to significant channel estimation errors and reduced activity detection accuracy. In this paper, we focus on the problem of JADCE in a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) grant-free random access network. We propose to incorporate a more accurate channel prior learned by score-based generative models (a.k.a. diffusion models) into message passing, so as to push towards the performance limit of JADCE. Specifically, we develop a novel turbo message passing (TMP) framework that models the entire channel matrix as a super node, rather than factorizing it element-wise. This design enables the seamless integration of score-based generative models as a minimum mean-squared error (MMSE) denoiser. The variance of the denoiser, which is essential in message passing, can also be learned through score-based generative models. Our approach, termed score-based TMP for JADCE (STMP-JADCE), takes full advantages of the powerful generative prior and, meanwhile, benefits from the fast convergence speed of message passing. Numerical simulations show that STMP-JADCE drastically enhances the activity detection and channel estimation performance compared to the state-of-the-art baseline algorithms.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3575-3590"},"PeriodicalIF":17.2,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Complexity-Scalable Near-Optimal Transceiver Design for Massive MIMO-BICM Systems 大规模MIMO-BICM系统的复杂性可扩展近最优收发器设计

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-06 DOI: 10.1109/JSAC.2026.3662310

Jie Yang;Wanchen Hu;Yi Jiang;Shuangyang Li;Xin Wang;Derrick Wing Kwan Ng;Giuseppe Caire

{"title":"Complexity-Scalable Near-Optimal Transceiver Design for Massive MIMO-BICM Systems","authors":"Jie Yang;Wanchen Hu;Yi Jiang;Shuangyang Li;Xin Wang;Derrick Wing Kwan Ng;Giuseppe Caire","doi":"10.1109/JSAC.2026.3662310","DOIUrl":"10.1109/JSAC.2026.3662310","url":null,"abstract":"The 6th generation (6G) and future wireless networks are envisioned to employ multiple-input multiple-output (MIMO) transmissions with large array sizes, and therefore, the adoption of complexity-scalable transceiver becomes crucial. In this paper, we propose a novel complexity-scalable transceiver design for MIMO systems exploiting bit-interleaved coded modulation (termed MIMO-BICM systems). The proposed scheme leverages the channel bidiagonalization decomposition (CBD), based on which an optimization framework for the precoder and post-processor is developed for maximizing the mutual information (MI) with finite-alphabet inputs. Particularly, we unveil that the desired precoder and post-processor behave distinctively different with respect to the operating signal-to-noise ratio (SNR), where the equivalent channel condition number (ECCN) serves as an effective indicator for the overall achievable rate performance. Specifically, at low SNRs, diagonal transmission with a large ECCN is advantageous, while at high SNRs, uniform subchannel gains with a small ECCN are preferred. This allows us to further propose a low-complexity generalized parallel CBD design (GP-CBD) based on the Givens rotation according to a well-approximated closed-form performance metric on the achievable rates that takes into account the insights from the ECCN. Numerical results validate the superior performance of the proposed scheme in terms of achievable rate and bit error rate (BER), compared to state-of-the-art designs across various modulation and coding schemes (MCSs).","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3557-3574"},"PeriodicalIF":17.2,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2025 Index IEEE Journal on Selected Areas in Communications 2025年IEEE通讯领域期刊索引

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-04 DOI: 10.1109/JSAC.2026.3661344

引用次数: 0

FedLoDrop: Federated LoRA With Dropout for Generalized LLM Fine-Tuning FedLoDrop：用于广义LLM微调的带有Dropout的联邦LoRA

IF 17.2

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2026-02-03 DOI: 10.1109/JSAC.2026.3660935

Sijing Xie;Dingzhu Wen;Changsheng You;Qimei Chen;Mehdi Bennis;Kaibin Huang

{"title":"FedLoDrop: Federated LoRA With Dropout for Generalized LLM Fine-Tuning","authors":"Sijing Xie;Dingzhu Wen;Changsheng You;Qimei Chen;Mehdi Bennis;Kaibin Huang","doi":"10.1109/JSAC.2026.3660935","DOIUrl":"10.1109/JSAC.2026.3660935","url":null,"abstract":"Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further enhance generalization ability while reducing training costs, this paper proposes Federated LoRA with Dropout (FedLoDrop), a new framework that applies dropout to the rows and columns of the trainable matrix in Federated LoRA. A generalization error bound and convergence analysis under sparsity regularization are obtained, which elucidate the fundamental trade-off between underfitting and overfitting. The error bound reveals that a higher dropout rate increases model sparsity, thereby lowering the upper bound of pointwise hypothesis stability (PHS). While this reduces the gap between empirical and generalization errors, it also incurs a higher empirical error, which, together with the gap, determines the overall generalization error. On the other hand, though dropout reduces communication costs, deploying FedLoDrop at the network edge still faces challenges due to limited network resources. To address this issue, an optimization problem is formulated to minimize the upper bound of the generalization error, by jointly optimizing the dropout rate and resource allocation subject to the latency and per-device energy consumption constraints. To solve this problem, a branch-and-bound (B&B)-based method is proposed to obtain its globally optimal solution. Moreover, to reduce the high computational complexity of the B&B-based method, a penalized successive convex approximation (P-SCA)-based algorithm is proposed to efficiently obtain its high-quality suboptimal solution. Finally, numerical results demonstrate the effectiveness of the proposed approach in mitigating overfitting and improving the generalization capability.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"3541-3556"},"PeriodicalIF":17.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0