IEEE Transactions on Broadcasting最新文献_第5页

Hierarchical Rate-Splitting for Non-Orthogonal Broadcast Multicast and Unicast Transmission in Multibeam Satellite System 多波束卫星系统中非正交广播组播和单播传输的分层速率分割

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-06-02 DOI: 10.1109/TBC.2026.3696548

Yuyan Ren;Meilin Xu;Jiayan Zhang;Yunkai Guo;Chengzhao Shan;Shuai Han;Yongkui Ma

{"title":"Hierarchical Rate-Splitting for Non-Orthogonal Broadcast Multicast and Unicast Transmission in Multibeam Satellite System","authors":"Yuyan Ren;Meilin Xu;Jiayan Zhang;Yunkai Guo;Chengzhao Shan;Shuai Han;Yongkui Ma","doi":"10.1109/TBC.2026.3696548","DOIUrl":"https://doi.org/10.1109/TBC.2026.3696548","url":null,"abstract":"Multibeam satellite systems, benefiting from wide coverage, enable ubiquitous connectivity. Meanwhile, the promising hierarchical rate-splitting multiple access (HRSMA) technique, with robust interference management capabilities, can effectively handle both inter-beam and intra-beam interference caused by multibeam satellite systems. To simultaneously support diverse information services in multibeam satellite systems, this paper proposes a non-orthogonal broadcast, multicast, and unicast (NOBMU) joint transmission scheme based on HRSMA. Specifically, we formulate a maximum sum rate (MSR) problem and develop a block coordinate descent (BCD) iterative optimization (IO) algorithm based on weighted minimum mean square error (WMMSE). The closed-form expressions for beamforming vectors are derived using the Lagrangian expression and Karush-Kuhn-Tucker (KKT) conditions. To enhance the sum rate while ensuring fairness, a two-stage optimization framework is proposed: the first stage solves a max-min fairness (MMF) problem to obtain a guaranteed minimum user rate, which is then applied as a constraint in the second-stage MSR problem. Finally, simulation results demonstrate that the proposed HRSMA-assisted NOBMU transmission scheme significantly outperforms other multiple access schemes.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"405-417"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Broadcasting Information for Readers and Authors 面向读者和作者的广播信息IEEE汇刊

IF 4.8 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-06 DOI: 10.1109/TBC.2026.3666580

引用次数: 0

STSR360: Joint Spatio-Temporal Sampling and Reconstruction for 360-Degree Video Streaming STSR360: 360度视频流的联合时空采样和重建

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-10-23 DOI: 10.1109/TBC.2025.3617651

Li Yu;Jinhuan Yao;Zhaoqing Pan;Byeungwoo Jeon

{"title":"STSR360: Joint Spatio-Temporal Sampling and Reconstruction for 360-Degree Video Streaming","authors":"Li Yu;Jinhuan Yao;Zhaoqing Pan;Byeungwoo Jeon","doi":"10.1109/TBC.2025.3617651","DOIUrl":"https://doi.org/10.1109/TBC.2025.3617651","url":null,"abstract":"The streaming of 360-degree video faces significant bandwidth shortage due to its ultra-high resolution. To address bandwidth constraints, viewport prediction-based methods prioritize high-quality transmission of predicted viewport regions while omitting or reducing the quality of contents outside the viewport. However, accurate prediction of viewports is both difficult and challenging. To avoid reliance on viewport prediction, recent research has shifted toward sampling and reconstruction-based methods, which sample partial video frame to transmit and reconstruct the full frame at the client side. However, existing methods rely on random sampling, neglecting the high spatio-temporal relevance within the 360-degree video, which limits the efficiency. To address this, our paper proposes a Joint Spatio-Temporal Sampling and Reconstruction method for 360-Degree Video Streaming (STSR360), which utilizes an end-to-end feedback-driven dynamic sampling strategy that enables collaborative optimization between the sampling and reconstruction networks. To reduce spatio-temporal redundancy, we design an adaptive frame sampling network to sample video frames with complex motion, followed by an adaptive patch sampling network for patches with critical details. The sampled video is sent to the client for reconstruction. To enable the network to prioritize sampling video with more details, both patch and frame reconstruction losses are calculated to update model parameters. The experimental results show that our method outperforms the state-of-the-art methods by 0.89 dB in PSNR and 0.006 in SSIM, with bandwidth consumption reduced by 1.56%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"170-181"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STAR-RIS Aided RSMA for Multi-Group Joint Multicast and Unicast Transmission STAR-RIS辅助的多组联合多播和单播传输RSMA

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1109/TBC.2025.3622339

Yin Xu;Xiaowu Ou;Haoze Yang;Cixiao Zhang;Yihang Huang;Hang Yin;Dazhi He;Wenjun Zhang;Yiyan Wu

{"title":"STAR-RIS Aided RSMA for Multi-Group Joint Multicast and Unicast Transmission","authors":"Yin Xu;Xiaowu Ou;Haoze Yang;Cixiao Zhang;Yihang Huang;Hang Yin;Dazhi He;Wenjun Zhang;Yiyan Wu","doi":"10.1109/TBC.2025.3622339","DOIUrl":"https://doi.org/10.1109/TBC.2025.3622339","url":null,"abstract":"Rate splitting multiple access (RSMA) is a novel transmission technique that enables simultaneous delivery of common and private messages to multiple users, which is suitable for joint multicast and unicast transmission. In this paper, a multi-group joint multicast and unicast downlink system is considered, where users are divided into different groups. A user grouping policy based on joint services and channel conditions is investigated, and a sum-rate maximization problem for the downlink RSMA system is formulated. Furthermore, the simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is introduced to improve the extremely poor user channel conditions. Then a joint optimization framework based on alternating optimization (AO) for user grouping, phase adjustment, and resource allocation in STAR-RIS-assisted RSMA system is proposed, where successive convex approximation (SCA) and the projected gradient method (PGM) serve as key optimization algorithms. Simulation results demonstrate that an appropriate user grouping policy fully exploits the performance of RSMA, and the introduction of STAR-RIS significantly further enhances the system capacity across various channel scenarios. Notably, when multiple users experience poor channel conditions, STAR-RIS effectively improves the channel conditions and incorporates them into RSMA groups, further boosting overall system performance.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"117-129"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Offloading and Resource Allocation in LEO-GEO Satellite Cooperative Transmission Networks LEO-GEO卫星协同传输网络中的数据卸载与资源分配

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-10-24 DOI: 10.1109/TBC.2025.3609079

Huimin Deng;Lin Gui;Kai Ying

引用次数: 0

Novel Distributed Multimedia Recommendation Systems Using Personalized Information 基于个性化信息的新型分布式多媒体推荐系统

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-07-08 DOI: 10.1109/TBC.2025.3583989

Shih Yu Chang;Hsiao-Chun Wu;Kun Yan;Scott Chih-Hao Huang;Yiyan Wu

{"title":"Novel Distributed Multimedia Recommendation Systems Using Personalized Information","authors":"Shih Yu Chang;Hsiao-Chun Wu;Kun Yan;Scott Chih-Hao Huang;Yiyan Wu","doi":"10.1109/TBC.2025.3583989","DOIUrl":"https://doi.org/10.1109/TBC.2025.3583989","url":null,"abstract":"In this paper, we propose a novel distributed multimedia recommendation system (DMRS) to address personalized preference by use of the matrix-sketching approach for dimensionality reduction and local information updates. Conventional recommendation systems can hardly address scalability, privacy, and robustness, all of which are very important in practice. To combat the aforementioned challenges, we propose to incorporate local information updates based on local private information at the client side to protect privacy by restricting users’ data from the server and utilize the matrix-sketching scheme to further reduce the dimensionality of the global user-item interaction data so that the personalized (distributed) recommendations can be made by users’ devices in local. To evaluate the system performance, we define a new robustness measure, namely <inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>-robustness, which quantifies the performance consistency of the recommendation system and involves both sketching errors and local rating updates. Furthermore, we introduce a novel randomized matrix-factorization algorithm to achieve the desired robustness while still maintaining the interaction-data fidelity in terms of normalized root-mean-square error (NRMSE). Our experimental results on both simulated and real-world data demonstrate the effectiveness of our proposed novel DMRS in attaining a good balance between the interaction-data fidelity and the system robustness subject to the privacy protection.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"144-155"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Adaptive Fault-Tolerant Resource Optimization for Highly-Dynamic Integrated Air-Ground Broadcast Ad-Hoc Networks 高动态集成地空广播Ad-Hoc网络鲁棒自适应容错资源优化

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-10-06 DOI: 10.1109/TBC.2025.3611637

Yingjian Wang;Zhifang Wang;Yanzhu Hu;Changqiao Xu;Jianguo Yu;Xinghao Zhao;Meng Zhan;Zihan Liang;Tianqi Tian;Gabriel-Miro Muntean

{"title":"Robust Adaptive Fault-Tolerant Resource Optimization for Highly-Dynamic Integrated Air-Ground Broadcast Ad-Hoc Networks","authors":"Yingjian Wang;Zhifang Wang;Yanzhu Hu;Changqiao Xu;Jianguo Yu;Xinghao Zhao;Meng Zhan;Zihan Liang;Tianqi Tian;Gabriel-Miro Muntean","doi":"10.1109/TBC.2025.3611637","DOIUrl":"https://doi.org/10.1109/TBC.2025.3611637","url":null,"abstract":"Highly dynamic air–ground integrated broadcast ad-hoc networks are essential for wide-area wireless coverage. However, high node mobility, co-channel interference, and network faults hinder reliable delivery and efficient resource use. This paper presents a robust, adaptive, and fault-tolerant resource-optimization framework based on distributed chaotic neural networks. First, it introduces a space-air-ground broadcast-transmission model that accounts for node mobility, link failures, and interference. Then, it presents the design of a distributed local chaotic-neural-network controller that allocates spectrum, power, and link resources in real time, explicitly handling uncertainties and abrupt faults. Finally, simulations and experiments show substantial mitigation of performance loss under high mobility (relative velocities 180-210 km/h) and varying link distances (coverage radius 10-12 km). With real-time topology adaptation, broadcast-link stability improves by up to 60%. These results provide a basis for reliable deployment and efficient resource optimization of broadcast ad-hoc networks, supporting future low-altitude airborne networking applications.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"71-86"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11185243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

5G Multicast Broadcast Services: Performance Overview and Future Extensions 5G组播广播业务：性能概述和未来扩展

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-10-07 DOI: 10.1109/TBC.2025.3611690

Álvaro Ibanez;Carlos Barjau;David Gomez-Barquero;Manuel Fuentes;Sung-Ik Park;Seok-Ki Ahn

{"title":"5G Multicast Broadcast Services: Performance Overview and Future Extensions","authors":"Álvaro Ibanez;Carlos Barjau;David Gomez-Barquero;Manuel Fuentes;Sung-Ik Park;Seok-Ki Ahn","doi":"10.1109/TBC.2025.3611690","DOIUrl":"https://doi.org/10.1109/TBC.2025.3611690","url":null,"abstract":"This paper presents an extensive evaluation of 5G Multicast Broadcast Services (5MBS) services framework standardized by 3rd Generation Partnership Project (3GPP) in terms of standardization, performance evaluation, deployment feasibility, and physical layer enhancements. Unlike traditional Point-to-Point (PtP) communications, 5MBS enables efficient Point-to-Multipoint (PtM) transmission to multiple User Equipment (UE)s, supporting both unicast and group-based delivery. We assess three delivery modes, unicast under Close Loop Spacial Multiplexing (CLSM), multicast under Open Loop Spacial Multiplexing (OLSM), and broadcast (Single Input Single Output (SISO)) across rural, urban, and vehicular scenarios using the Vienna System Level Simulator (SLS). Our results show that multicast strikes a performance balance under medium user densities, while broadcast ensures minimum coverage guarantees in high-load or mobility-critical situations. Additionally, we demonstrate that incorporating Multiple Input Multiple Output (MIMO) configurations into 5G broadcast significantly improves Signal-to-Interference-plus-Noise Ratio (SINR) and user throughput at the 5th percentile, thus covering 95% coverage, with MIMO <inline-formula> <tex-math>$4times 4$ </tex-math></inline-formula> achieving up to 0.7 Mbps gains and 5.5 dB improvements over SISO. These findings highlight the suitability of 5MBS for scalable, reliable content delivery, and underline the importance of adaptive delivery strategies based on traffic density, mobility, and deployment environment. The insights derived offer practical guidelines for efficient use of multicast and broadcast in next-generation 5G networks.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"14-28"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Text-Guided ROI-Based Neural Image Compression 基于文本引导roi的神经图像压缩

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-10-03 DOI: 10.1109/TBC.2025.3609065

Kai Hu;Han Wang;Yongxiang Lyu;Renhe Liu;Yu Liu

{"title":"Text-Guided ROI-Based Neural Image Compression","authors":"Kai Hu;Han Wang;Yongxiang Lyu;Renhe Liu;Yu Liu","doi":"10.1109/TBC.2025.3609065","DOIUrl":"https://doi.org/10.1109/TBC.2025.3609065","url":null,"abstract":"ROI-based image compression can significantly reduce data redundancy and improve storage efficiency while preserving the high fidelity of critical content. Current methods typically employ explicit bit allocation for region-adaptive coding by masking features before quantization to suppress background information. Although this strategy enhances compression performance, it severely degrades background reconstruction quality. In response, we propose a text-guided ROI-based neural image compression method, which mainly adopts a novel implicit bit allocation strategy similar to attention mechanism and incorporates natural-language descriptions to enable flexible user interaction. We firstly leverage a high-performance referencing-image segmentation model to generate masks from text prompts. Next, we introduce an efficient mask-guided feature transform (MGFT) module, composed of a region-adaptive attention (RAA) block and a region-adaptive transform (RAT) block, to more effectively apply these masks for implicit bit allocation. To improve reconstructed background quality, we design a weight-shared dual-decoder network that separately reconstructs foreground and background regions. To the best of our knowledge, this is the first work to integrate textual descriptions into ROI-based image compression and to employ implicit bit allocation for high-quality region-adaptive coding. Experiments on the COCO2017 dataset demonstrate that our method achieves optimal rate–distortion performance and supports text-specified targeted compression with excellent coding efficiency. The decoded results maintain high foreground fidelity while preserving pleasing background perceptual quality.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"223-232"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Image Compression With Multi-Type Feature Fusion and Multi-Distribution Mixture Likelihood 基于多类型特征融合和多分布混合似然的神经图像压缩

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-03-01 Epub Date: 2025-10-06 DOI: 10.1109/TBC.2025.3609024

Dongjian Yang;Xiaopeng Fan;Debin Zhao

{"title":"Neural Image Compression With Multi-Type Feature Fusion and Multi-Distribution Mixture Likelihood","authors":"Dongjian Yang;Xiaopeng Fan;Debin Zhao","doi":"10.1109/TBC.2025.3609024","DOIUrl":"https://doi.org/10.1109/TBC.2025.3609024","url":null,"abstract":"Recently, significant progress has been made in neural image compression (NIC). The two core components of NIC are the encoder-decoder network and the entropy model. Existing encoder-decoder networks enhance their transform performance by employing strategies that combine local (high-frequency) and global (low-frequency) features or by improving the ability to capture global dependencies. However, these methods still face two main issues. First, hybrid architectures focus solely on local and global features, neglecting intermediate-scale information. Second, complete modeling of global dependencies has not been achieved due to computational complexity constraints. To address these challenges, we propose a Multi-Type Feature Fusion (MTFF) module consisting of Visual Linear Attention Duality (VLAD) units and multi-scale Content-Aware Residual (CAR) blocks. Specifically, the key of VLAD is a Linear Attention Duality (LAD), which introduces linear attention into the State Space Duality (SSD) to achieve efficient and complete global modeling capabilities. Meanwhile, we develop a Content-Aware Depth-Wise Convolution (CADWC) for building the CAR block by coupling the weights of depth-wise convolutions with inputs to adaptively extract non-global features. Based on MTFF, we construct a novel encoder-decoder network to capture dependencies at different scales (frequencies) to further enhance its transform performance. Besides, we design a Multi-Distribution Mixture Likelihood (MDML) by weighting five basic distributions including Cauchy, Laplace, Logistic, Gaussian, and Gumbel to model the distribution of discrete latent representations, further enhancing the flexibility and accuracy of the entropy model. Extensive experiments demonstrate that our method achieves promising rate-distortion performance on the Kodak, Tecnick, and CLIC datasets.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 1","pages":"233-246"},"PeriodicalIF":5.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0