As wireless technology continues its rapid evolution, the sixth-generation (6G) networks are capable of offering exceptionally high data transmission rates as well as low latency, which is promisingly able to meet the high-demand needs for digital twins (DTs). Quality-of-experience (QoE) in this situation, which refers to the users’ overall satisfaction and perception of the provided DT service in 6G networks, is significant to optimize the service and help improve the users’ experience. Despite progress in developing theories and systems for digital twin transmission under 6G networks, the assessment of QoE for users falls behind. To address this gap, our paper introduces the first QoE evaluation database for human digital twins (HDTs) in 6G network environments, aiming to systematically analyze and quantify the related quality factors. We utilize a mmWave network model for channel capacity simulation and employ high-quality digital humans as source models, which are further animated, encoded, and distorted for final QoE evaluation. Subjective quality ratings are collected from a well-controlled subjective experiment for the 400 generated HDT sequences. Additionally, we propose a novel QoE evaluation metric that considers both quality-of-service (QoS) and content-quality features. Experimental results indicate that our model outperforms existing state-of-the-art QoE evaluation models and other competitive quality assessment models, thus making significant contributions to the domain of 6G network applications for HDTs.
{"title":"Quality-of-Experience Evaluation for Digital Twins in 6G Network Environments","authors":"Zicheng Zhang;Yingjie Zhou;Long Teng;Wei Sun;Chunyi Li;Xiongkuo Min;Xiao-Ping Zhang;Guangtao Zhai","doi":"10.1109/TBC.2023.3345656","DOIUrl":"10.1109/TBC.2023.3345656","url":null,"abstract":"As wireless technology continues its rapid evolution, the sixth-generation (6G) networks are capable of offering exceptionally high data transmission rates as well as low latency, which is promisingly able to meet the high-demand needs for digital twins (DTs). Quality-of-experience (QoE) in this situation, which refers to the users’ overall satisfaction and perception of the provided DT service in 6G networks, is significant to optimize the service and help improve the users’ experience. Despite progress in developing theories and systems for digital twin transmission under 6G networks, the assessment of QoE for users falls behind. To address this gap, our paper introduces the first QoE evaluation database for human digital twins (HDTs) in 6G network environments, aiming to systematically analyze and quantify the related quality factors. We utilize a mmWave network model for channel capacity simulation and employ high-quality digital humans as source models, which are further animated, encoded, and distorted for final QoE evaluation. Subjective quality ratings are collected from a well-controlled subjective experiment for the 400 generated HDT sequences. Additionally, we propose a novel QoE evaluation metric that considers both quality-of-service (QoS) and content-quality features. Experimental results indicate that our model outperforms existing state-of-the-art QoE evaluation models and other competitive quality assessment models, thus making significant contributions to the domain of 6G network applications for HDTs.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"995-1007"},"PeriodicalIF":3.2,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947547","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}
Pub Date : 2024-01-03DOI: 10.1109/TBC.2023.3342707
Zongyao Hu;Lixiong Liu;Qingbing Sang
Spherical signals of omnidirectional videos need to be projected to a 2D plane for transmission or storage. The projection will produce geometrical deformation that affects the feature representation of Convolutional Neural Networks (CNN) on the perception of omnidirectional videos. Currently developed omnidirectional video quality assessment (OVQA) methods leverage viewport images or spherical CNN to circumvent the geometrical deformation. However, the viewport-based methods neglect the interaction between viewport images while there lacks sufficient pre-training samples for taking spherical CNN as an efficient backbone in OVQA model. In this paper, we alleviate the influence of geometrical deformation from a causal perspective. A structural causal model is adopted to analyze the implicit reason for the disturbance of geometrical deformation on quality representation and we find the latitude factor confounds the feature representation and distorted contents. Based on this evidence, we propose a Causal Intervention-based Quality prediction Network (CIQNet) to alleviate the causal effect of the confounder. The resulting framework first segments the video content into sub-areas and trains feature encoders to obtain latitude-invariant representation for removing the relationship between the latitude and feature representation. Then the features of each sub-area are aggregated by estimated weights in a backdoor adjustment module to remove the relationship between the latitude and video contents. Finally, the temporal dependencies of aggregated features are modeled to implement the quality prediction. We evaluate the performance of CIQNet on three publicly available OVQA databases. The experimental results show CIQNet achieves competitive performance against state-of-art methods. The source code of CIQNet is available at: https://github.com/Aca4peop/CIQNet .
{"title":"Omnidirectional Video Quality Assessment With Causal Intervention","authors":"Zongyao Hu;Lixiong Liu;Qingbing Sang","doi":"10.1109/TBC.2023.3342707","DOIUrl":"10.1109/TBC.2023.3342707","url":null,"abstract":"Spherical signals of omnidirectional videos need to be projected to a 2D plane for transmission or storage. The projection will produce geometrical deformation that affects the feature representation of Convolutional Neural Networks (CNN) on the perception of omnidirectional videos. Currently developed omnidirectional video quality assessment (OVQA) methods leverage viewport images or spherical CNN to circumvent the geometrical deformation. However, the viewport-based methods neglect the interaction between viewport images while there lacks sufficient pre-training samples for taking spherical CNN as an efficient backbone in OVQA model. In this paper, we alleviate the influence of geometrical deformation from a causal perspective. A structural causal model is adopted to analyze the implicit reason for the disturbance of geometrical deformation on quality representation and we find the latitude factor confounds the feature representation and distorted contents. Based on this evidence, we propose a Causal Intervention-based Quality prediction Network (CIQNet) to alleviate the causal effect of the confounder. The resulting framework first segments the video content into sub-areas and trains feature encoders to obtain latitude-invariant representation for removing the relationship between the latitude and feature representation. Then the features of each sub-area are aggregated by estimated weights in a backdoor adjustment module to remove the relationship between the latitude and video contents. Finally, the temporal dependencies of aggregated features are modeled to implement the quality prediction. We evaluate the performance of CIQNet on three publicly available OVQA databases. The experimental results show CIQNet achieves competitive performance against state-of-art methods. The source code of CIQNet is available at: \u0000<uri>https://github.com/Aca4peop/CIQNet</uri>\u0000.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 1","pages":"238-250"},"PeriodicalIF":4.5,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947353","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}
Pub Date : 2023-12-28DOI: 10.1109/TBC.2023.3342696
Bo Hu;Tuoxun Zhao;Jia Zheng;Yan Zhang;Leida Li;Weisheng Li;Xinbo Gao
Image Quality Assessment (IQA) plays an important role in the field of computer vision. However, most of the existing metrics for Blind IQA (BIQA) adopt an end-to-end way and do not adequately simulate the process of human subjective evaluation, which limits further improvements in model performance. In the process of perception, people first give a preliminary impression of the distortion type and relative quality of the images, and then give a specific quality score under the influence of the interaction of the two. Although some methods have attempted to explore the effects of distortion type and relative quality, the relationship between them has been neglected. In this paper, we propose a BIQA with coarse-grained perception construction and fine-grained interaction learning, called PINet for short. The fundamental idea is to learn from the two-stage human perceptual process. Specifically, in the pre-training stage, the backbone initially processes a pair of synthetic distorted images with pseudo-subjective scores, and the multi-scale feature extraction module integrates the deep information and delivers it to the coarse-grained perception construction module, which performs the distortion discrimination and the quality ranking. In the fine-tuning stage, we propose a fine-grained interactive learning module to interact with the two pieces of information to further improve the performance of the proposed PINet. The experimental results prove that the proposed PINet not only achieves competing performances on synthetic distortion datasets but also performs better on authentic distortion datasets.
{"title":"Blind Image Quality Assessment With Coarse-Grained Perception Construction and Fine-Grained Interaction Learning","authors":"Bo Hu;Tuoxun Zhao;Jia Zheng;Yan Zhang;Leida Li;Weisheng Li;Xinbo Gao","doi":"10.1109/TBC.2023.3342696","DOIUrl":"https://doi.org/10.1109/TBC.2023.3342696","url":null,"abstract":"Image Quality Assessment (IQA) plays an important role in the field of computer vision. However, most of the existing metrics for Blind IQA (BIQA) adopt an end-to-end way and do not adequately simulate the process of human subjective evaluation, which limits further improvements in model performance. In the process of perception, people first give a preliminary impression of the distortion type and relative quality of the images, and then give a specific quality score under the influence of the interaction of the two. Although some methods have attempted to explore the effects of distortion type and relative quality, the relationship between them has been neglected. In this paper, we propose a BIQA with coarse-grained perception construction and fine-grained interaction learning, called PINet for short. The fundamental idea is to learn from the two-stage human perceptual process. Specifically, in the pre-training stage, the backbone initially processes a pair of synthetic distorted images with pseudo-subjective scores, and the multi-scale feature extraction module integrates the deep information and delivers it to the coarse-grained perception construction module, which performs the distortion discrimination and the quality ranking. In the fine-tuning stage, we propose a fine-grained interactive learning module to interact with the two pieces of information to further improve the performance of the proposed PINet. The experimental results prove that the proposed PINet not only achieves competing performances on synthetic distortion datasets but also performs better on authentic distortion datasets.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 2","pages":"533-544"},"PeriodicalIF":4.5,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292391","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}
As social media communication develops, reliable multimedia quality evaluation indicators have become a prerequisite for enriching user experience services. In this paper, we propose a multiscale spatiotemporal pyramid attention (SPA) block for constructing a blind video quality assessment (VQA) method to evaluate the perceptual quality of videos. First, we extract motion information from the video frames at different temporal scales to form a feature pyramid, which provides a feature representation with multiple visual perceptions. Second, an SPA module, which can effectively extract multiscale spatiotemporal information at various temporal scales and develop a cross-scale dependency relationship, is proposed. Finally, the quality estimation process is completed by passing the extracted features obtained from a network of multiple stacked spatiotemporal pyramid blocks through a regression network to determine the perceived quality. The experimental results demonstrate that our method is on par with the state-of-the-art approaches. The source code necessary for conducting groundbreaking scientific research is accessible online https://github.com/Land5cape/SPBVQA .
{"title":"A Blind Video Quality Assessment Method via Spatiotemporal Pyramid Attention","authors":"Wenhao Shen;Mingliang Zhou;Xuekai Wei;Heqiang Wang;Bin Fang;Cheng Ji;Xu Zhuang;Jason Wang;Jun Luo;Huayan Pu;Xiaoxu Huang;Shilong Wang;Huajun Cao;Yong Feng;Tao Xiang;Zhaowei Shang","doi":"10.1109/TBC.2023.3340031","DOIUrl":"https://doi.org/10.1109/TBC.2023.3340031","url":null,"abstract":"As social media communication develops, reliable multimedia quality evaluation indicators have become a prerequisite for enriching user experience services. In this paper, we propose a multiscale spatiotemporal pyramid attention (SPA) block for constructing a blind video quality assessment (VQA) method to evaluate the perceptual quality of videos. First, we extract motion information from the video frames at different temporal scales to form a feature pyramid, which provides a feature representation with multiple visual perceptions. Second, an SPA module, which can effectively extract multiscale spatiotemporal information at various temporal scales and develop a cross-scale dependency relationship, is proposed. Finally, the quality estimation process is completed by passing the extracted features obtained from a network of multiple stacked spatiotemporal pyramid blocks through a regression network to determine the perceived quality. The experimental results demonstrate that our method is on par with the state-of-the-art approaches. The source code necessary for conducting groundbreaking scientific research is accessible online \u0000<uri>https://github.com/Land5cape/SPBVQA</uri>\u0000.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 1","pages":"251-264"},"PeriodicalIF":4.5,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052981","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}
Pub Date : 2023-12-21DOI: 10.1109/TBC.2023.3340022
Ronghui Zhang;Quan Zhou;Xuesong Qiu;Lijian Xin
As satellite and communication technology advances, terrestrial-satellite communications and broadcasting (TSCB) provide uninterrupted services, meeting the demand for seamless communication and broadcasting interconnection. The evolving TSCB technology faces challenges in handling dynamic time-frequency features of wireless signals. Stable satellite-ground interaction is crucial, as co-channel interference can disrupt communication, causing instability. To address this, the TSCB system needs an effective mechanism to eliminate signal interference. Current methods often overlook complex domain features, resulting in suboptimal outcomes. Leveraging deep learning’s computational power, we introduce WSIE-Net, an encoder-decoder model for TSCB signal interference elimination. The model learns an effective separation matrix for robust separation amidst wireless signal interference, comprehensively capturing orthogonal features. We analyze time-frequency diagrams, bit error rates, and other parameters. Performance assessment involves similarity coefficients and Kullback-Leibler Divergence, comparing the proposed algorithm with common blind separation methods. Results indicate significant progress in signal interference elimination for TSCB.
{"title":"Data-Driven Co-Channel Signal Interference Elimination Algorithm for Terrestrial-Satellite Communications and Broadcasting","authors":"Ronghui Zhang;Quan Zhou;Xuesong Qiu;Lijian Xin","doi":"10.1109/TBC.2023.3340022","DOIUrl":"10.1109/TBC.2023.3340022","url":null,"abstract":"As satellite and communication technology advances, terrestrial-satellite communications and broadcasting (TSCB) provide uninterrupted services, meeting the demand for seamless communication and broadcasting interconnection. The evolving TSCB technology faces challenges in handling dynamic time-frequency features of wireless signals. Stable satellite-ground interaction is crucial, as co-channel interference can disrupt communication, causing instability. To address this, the TSCB system needs an effective mechanism to eliminate signal interference. Current methods often overlook complex domain features, resulting in suboptimal outcomes. Leveraging deep learning’s computational power, we introduce WSIE-Net, an encoder-decoder model for TSCB signal interference elimination. The model learns an effective separation matrix for robust separation amidst wireless signal interference, comprehensively capturing orthogonal features. We analyze time-frequency diagrams, bit error rates, and other parameters. Performance assessment involves similarity coefficients and Kullback-Leibler Divergence, comparing the proposed algorithm with common blind separation methods. Results indicate significant progress in signal interference elimination for TSCB.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1065-1075"},"PeriodicalIF":3.2,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207657","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}
Pub Date : 2023-12-19DOI: 10.1109/TBC.2023.3333750
Motong Xu;Byeungwoo Jeon
The rate-distortion optimized quantization (RDOQ) in HEVC has improved the coding efficiency of the conventional uniform scalar quantization (SQ) very much. Since the RDOQ is computationally complex, in this paper, we investigate a way of performing RDOQ more efficiently in HEVC. Based on our statistical observation of non-trivial percentage of transform blocks (TB) for which RDOQ does not change their quantization results of SQ, we design a learning-based quantizer selection scheme which can tell in advance whether RDOQ is expected to modify the quantization levels calculated by SQ. Only those TBs likely to be changed by RDOQ are subject to the actual RDOQ process. For the remaining TBs, we design an improved SQ which adapts the dead-zone interval size and round offset based on coefficient group and entropy coding features. The proposed improved SQ has much lower computational complexity than RDOQ while achieving better coding efficiency than the conventional SQ. The experimental results show that our efficient quantization scheme respectively provides 9% and 34% of encoding and quantization time reduction by selectively performing RDOQ only for 21% of TBs. The average BDBR performances of Y, Cb, and Cr channels are respectively–0.03%, 0.48%, and 0.45%.
{"title":"Learning-Based Efficient Quantizer Selection for Fast HEVC Encoder","authors":"Motong Xu;Byeungwoo Jeon","doi":"10.1109/TBC.2023.3333750","DOIUrl":"https://doi.org/10.1109/TBC.2023.3333750","url":null,"abstract":"The rate-distortion optimized quantization (RDOQ) in HEVC has improved the coding efficiency of the conventional uniform scalar quantization (SQ) very much. Since the RDOQ is computationally complex, in this paper, we investigate a way of performing RDOQ more efficiently in HEVC. Based on our statistical observation of non-trivial percentage of transform blocks (TB) for which RDOQ does not change their quantization results of SQ, we design a learning-based quantizer selection scheme which can tell in advance whether RDOQ is expected to modify the quantization levels calculated by SQ. Only those TBs likely to be changed by RDOQ are subject to the actual RDOQ process. For the remaining TBs, we design an improved SQ which adapts the dead-zone interval size and round offset based on coefficient group and entropy coding features. The proposed improved SQ has much lower computational complexity than RDOQ while achieving better coding efficiency than the conventional SQ. The experimental results show that our efficient quantization scheme respectively provides 9% and 34% of encoding and quantization time reduction by selectively performing RDOQ only for 21% of TBs. The average BDBR performances of Y, Cb, and Cr channels are respectively–0.03%, 0.48%, and 0.45%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 1","pages":"161-173"},"PeriodicalIF":4.5,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052881","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}
Pub Date : 2023-12-15DOI: 10.1109/TBC.2023.3334137
Yuhong Xie;Yuan Zhang;Tao Lin
Deep reinforcement learning (DRL) has demonstrated remarkable potential within the domain of video adaptive bitrate (ABR) optimization. However, training a well-performing DRL agent in the two-tier 360° video streaming system is non-trivial. The conventional DRL training approach fails to enable the model to start learning from simpler environments and then progressively explore more challenging ones, leading to suboptimal asymptotic performance and poor long-tail performance. In this paper, we propose a novel approach called DCRL360, which seamlessly integrates automatic curriculum learning (ACL) with DRL techniques to enable adaptive decision-making for 360° video bitrate selection and chunk scheduling. To tackle the training issue, we introduce a structured two-stage training framework. The first stage focuses on the selection of tasks conducive to learning, guided by a newly introduced training metric called Pscore, to enhance asymptotic performance. The newly introduced metric takes into consideration multiple facets, including performance improvement potential, the risk of being forgotten, and the uncertainty of a decision, to encourage the agent to train in rewarding environments. The second stage utilizes existing rule-based techniques to identify challenging tasks for fine-tuning the model, thereby alleviating the long-tail effect. Our experimental results demonstrate that DCRL360 outperforms state-of-the-art algorithms under various network conditions - including 5G/LTE/Broadband - with a remarkable improvement of 6.51-20.86% in quality of experience (QoE), as well as a reduction in bandwidth wastage by 10.60-31.50%.
{"title":"Deep Curriculum Reinforcement Learning for Adaptive 360° Video Streaming With Two-Stage Training","authors":"Yuhong Xie;Yuan Zhang;Tao Lin","doi":"10.1109/TBC.2023.3334137","DOIUrl":"https://doi.org/10.1109/TBC.2023.3334137","url":null,"abstract":"Deep reinforcement learning (DRL) has demonstrated remarkable potential within the domain of video adaptive bitrate (ABR) optimization. However, training a well-performing DRL agent in the two-tier 360° video streaming system is non-trivial. The conventional DRL training approach fails to enable the model to start learning from simpler environments and then progressively explore more challenging ones, leading to suboptimal asymptotic performance and poor long-tail performance. In this paper, we propose a novel approach called DCRL360, which seamlessly integrates automatic curriculum learning (ACL) with DRL techniques to enable adaptive decision-making for 360° video bitrate selection and chunk scheduling. To tackle the training issue, we introduce a structured two-stage training framework. The first stage focuses on the selection of tasks conducive to learning, guided by a newly introduced training metric called Pscore, to enhance asymptotic performance. The newly introduced metric takes into consideration multiple facets, including performance improvement potential, the risk of being forgotten, and the uncertainty of a decision, to encourage the agent to train in rewarding environments. The second stage utilizes existing rule-based techniques to identify challenging tasks for fine-tuning the model, thereby alleviating the long-tail effect. Our experimental results demonstrate that DCRL360 outperforms state-of-the-art algorithms under various network conditions - including 5G/LTE/Broadband - with a remarkable improvement of 6.51-20.86% in quality of experience (QoE), as well as a reduction in bandwidth wastage by 10.60-31.50%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 2","pages":"441-452"},"PeriodicalIF":4.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292482","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}
5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time localization while preserving privacy. The pedestrian location data enables adaptive 5G network planning, contextual location-based services, quality of service improvements, and load balancing. Simulations demonstrate the effectiveness of our FML scheme for accurate pedestrian localization. They also highlight significant enhancements to 5G multicast/broadcast services enabled by real-time pedestrian positioning. In summary, our work facilitates enhanced 5G multicast/broadcast services through federated on-device learning for real-time pedestrian localization.
{"title":"Federated Multitask Learning for Pedestrian Location-Aware 5G Multicast/Broadcast Services","authors":"Zexuan Jing;Junsheng Mu;Jian Jin;Zhenzhen Jiao;Peng Yu","doi":"10.1109/TBC.2023.3332012","DOIUrl":"https://doi.org/10.1109/TBC.2023.3332012","url":null,"abstract":"5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time localization while preserving privacy. The pedestrian location data enables adaptive 5G network planning, contextual location-based services, quality of service improvements, and load balancing. Simulations demonstrate the effectiveness of our FML scheme for accurate pedestrian localization. They also highlight significant enhancements to 5G multicast/broadcast services enabled by real-time pedestrian positioning. In summary, our work facilitates enhanced 5G multicast/broadcast services through federated on-device learning for real-time pedestrian localization.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 1","pages":"66-77"},"PeriodicalIF":4.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052873","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}
Pub Date : 2023-12-11DOI: 10.1109/TBC.2023.3335815
Zhiqiang Li;Shuai Han;Liang Xiao;Mugen Peng
The integrated satellite-terrestrial network (ISTN) is gaining traction for providing seamless communication and various services, i.e., broadcast and unicast information services. However, meeting massive terminal access and diverse information services poses challenges due to limited spectrum resources and complex multiple access interference in ISTN. Recently, rate-splitting multiple access (RSMA) has emerged as a promising solution offering non-orthogonal transmission and robust interference management. Inspired by this, we design the non-orthogonal broadcast and unicast (NOBU) transmission model by utilizing the common and private data streams of RSMA. Taking different levels of cooperation between satellite and base station (BS) into consideration, we propose two cooperative NOBU transmission schemes, where one is that only broadcast messages are shared, and the other is that the broadcast message and the sub-common message split by terminals are shared and jointly encoded into a super-common stream. Building upon this, we formulate joint max-min rate optimization problems while satisfying the broadcast information rate requirement in ISTN. To address these non-convex problems, we introduce an improved alternating optimization algorithm based on weighted minimum mean square error. Simulation results validate the significant gains of cooperative NOBU schemes compared to various baseline schemes.
{"title":"Cooperative Non-Orthogonal Broadcast and Unicast Transmission for Integrated Satellite–Terrestrial Network","authors":"Zhiqiang Li;Shuai Han;Liang Xiao;Mugen Peng","doi":"10.1109/TBC.2023.3335815","DOIUrl":"10.1109/TBC.2023.3335815","url":null,"abstract":"The integrated satellite-terrestrial network (ISTN) is gaining traction for providing seamless communication and various services, i.e., broadcast and unicast information services. However, meeting massive terminal access and diverse information services poses challenges due to limited spectrum resources and complex multiple access interference in ISTN. Recently, rate-splitting multiple access (RSMA) has emerged as a promising solution offering non-orthogonal transmission and robust interference management. Inspired by this, we design the non-orthogonal broadcast and unicast (NOBU) transmission model by utilizing the common and private data streams of RSMA. Taking different levels of cooperation between satellite and base station (BS) into consideration, we propose two cooperative NOBU transmission schemes, where one is that only broadcast messages are shared, and the other is that the broadcast message and the sub-common message split by terminals are shared and jointly encoded into a super-common stream. Building upon this, we formulate joint max-min rate optimization problems while satisfying the broadcast information rate requirement in ISTN. To address these non-convex problems, we introduce an improved alternating optimization algorithm based on weighted minimum mean square error. Simulation results validate the significant gains of cooperative NOBU schemes compared to various baseline schemes.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1052-1064"},"PeriodicalIF":3.2,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207656","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}
Pub Date : 2023-12-08DOI: 10.1109/TBC.2023.3336210
The 2023 Scott Helt Memorial Award was awarded to Hequn Zhang, Yue Zhang, John Cosmas, Nawar Jawad, Wei Li, Robert Muller, Tao Jiang for their paper, “mmWave Indoor Channel Measurement Campaign for 5G New Radio Indoor Broadcasting”. The papers appeared in the IEEE Transactions on Broadcasting, vol. 68, no. 2, pp. 331–344, June 2022. The purpose of the IEEE Scott Helt Memorial Award is to recognize exceptional publications in the field and to stimulate interest in and encourage contributions to the fields of interest of the Society.
2023 年斯科特-赫尔特纪念奖授予张贺群、张越、约翰-科斯马斯、纳瓦尔-贾瓦德、李伟、罗伯特-穆勒、蒋涛，以表彰他们的论文 "mmWave Indoor Channel Measurement Campaign for 5G New Radio Indoor Broadcasting"。论文发表于 2022 年 6 月出版的《电气和电子工程师学会广播学报》（IEEE Transactions on Broadcasting）第 68 卷第 2 期第 331-344 页。IEEE Scott Helt 纪念奖的目的是表彰在该领域发表的杰出论文，激发对学会感兴趣领域的兴趣并鼓励为这些领域做出贡献。
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