The vision of the future 5G - Multicast Broadcast Services (5G-MBS) is to achieve full convergence of broadcast and broadband services by providing these services on the same infrastructure and dynamically switching between them without impacting user experiences. By incorporating Layered Division Multiplexing (LDM) into the new 5G-MBS system and performing proper antenna precoding, the network can transmit a 2-layered signal where the higher-power Core Layer (CL) transmits a Single Frequency Network (SFN) broadcast signal, and the lower-power Enhanced Layer (EL) is used for broadband services. To evaluate the performance of the 2-layered network, a 5G system-level simulator is created and configured according to the 3GPP self-evaluation scenarios to compare against the 3GPP calibration results. The resulting Signal to Interference & Noise Ratio (SINR) Cumulative Distribution Function (CDF) curves fall within the tolerance margin of 1~2 dB from the 3GPP calibration average. Full simulations of the 2-layered network show for an urban scenario with Inter-Site Distance (ISD) of less than 1 km, the network can provide up to three 4K video broadcast services in the CL while supporting a near full broadband network in the EL. For further ISDs up to 3 km, the network can sustain video broadcast service at 1080p while supporting a partial broadband network. For a rural scenario, at the reference ISD of 1732 m, the CL can support three 4k video broadcast services while the EL performance matches a standalone broadband network. Finally, for further ISD of up to 5 km, the CL can support one 1080p and one 720p video broadcast service, and for ISD up to 10 km, the network can provide one broadcast service at 720p in the CL, all while providing a full broadband network in the EL.
{"title":"Layered Division Multiplexing Enabled Broadcast Broadband Convergence in 5G: Theory, Simulations, and Scenarios","authors":"Yu Xue;Wei Li;Yuxiao Zhai;Liang Zhang;Zhihong Hong;Elvino Sousa;Yiyan Wu","doi":"10.1109/TBC.2024.3437204","DOIUrl":"10.1109/TBC.2024.3437204","url":null,"abstract":"The vision of the future 5G - Multicast Broadcast Services (5G-MBS) is to achieve full convergence of broadcast and broadband services by providing these services on the same infrastructure and dynamically switching between them without impacting user experiences. By incorporating Layered Division Multiplexing (LDM) into the new 5G-MBS system and performing proper antenna precoding, the network can transmit a 2-layered signal where the higher-power Core Layer (CL) transmits a Single Frequency Network (SFN) broadcast signal, and the lower-power Enhanced Layer (EL) is used for broadband services. To evaluate the performance of the 2-layered network, a 5G system-level simulator is created and configured according to the 3GPP self-evaluation scenarios to compare against the 3GPP calibration results. The resulting Signal to Interference & Noise Ratio (SINR) Cumulative Distribution Function (CDF) curves fall within the tolerance margin of 1~2 dB from the 3GPP calibration average. Full simulations of the 2-layered network show for an urban scenario with Inter-Site Distance (ISD) of less than 1 km, the network can provide up to three 4K video broadcast services in the CL while supporting a near full broadband network in the EL. For further ISDs up to 3 km, the network can sustain video broadcast service at 1080p while supporting a partial broadband network. For a rural scenario, at the reference ISD of 1732 m, the CL can support three 4k video broadcast services while the EL performance matches a standalone broadband network. Finally, for further ISD of up to 5 km, the CL can support one 1080p and one 720p video broadcast service, and for ISD up to 10 km, the network can provide one broadcast service at 720p in the CL, all while providing a full broadband network in the EL.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1018-1031"},"PeriodicalIF":3.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226508","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 : 2024-08-28DOI: 10.1109/TBC.2024.3443544
Hao Yang;Tao Lin;Yuan Zhang;Yin Xu;Zhe Chen;Jinyao Yan
Multi-device video streaming applications enable seamless playback across various devices, including large-screen TVs, tablets, and smartphones, revolutionizing digital content consumption and enhancing user experience. However, ensuring consistently high quality of experience (QoE) across these heterogeneous devices remains a substantial challenge due to intrinsic differences in screen sizes and viewing conditions. In this paper, we first build an open-source, multi-device, and time-continuous QoE dataset named MCQoE by conducting a large-scale subjective experiment to analyze QoE variations among different screen-size devices. Then, we thoroughly investigate the dataset and observe that video quality and rebuffering impact on TVs is more significant than on other devices, such as middle-size PC monitors and small-screen smartphones, emphasizing the importance of building specific QoE models for different devices. Furthermore, we propose a novel low-complexity but effective QoE model denoted as LiteDC, integrating a temporal dilated convolution network with a targeted pruning technique to align with the computational constraints of embedded platforms. Extensive results show that compared to a state-of-the-art baseline algorithm, LiteDC achieves a remarkable 20.9-fold improvement in execution speed while increasing prediction accuracy by 6.4%. The MCQoE dataset is available for download at https://github.com/yanghaocuc/mcqoe.
{"title":"Enhancing QoE for Multi-Device Video Delivery: A Novel Dataset and Model Perspective","authors":"Hao Yang;Tao Lin;Yuan Zhang;Yin Xu;Zhe Chen;Jinyao Yan","doi":"10.1109/TBC.2024.3443544","DOIUrl":"10.1109/TBC.2024.3443544","url":null,"abstract":"Multi-device video streaming applications enable seamless playback across various devices, including large-screen TVs, tablets, and smartphones, revolutionizing digital content consumption and enhancing user experience. However, ensuring consistently high quality of experience (QoE) across these heterogeneous devices remains a substantial challenge due to intrinsic differences in screen sizes and viewing conditions. In this paper, we first build an open-source, multi-device, and time-continuous QoE dataset named <italic>MCQoE</i> by conducting a large-scale subjective experiment to analyze QoE variations among different screen-size devices. Then, we thoroughly investigate the dataset and observe that video quality and rebuffering impact on TVs is more significant than on other devices, such as middle-size PC monitors and small-screen smartphones, emphasizing the importance of building specific QoE models for different devices. Furthermore, we propose a novel low-complexity but effective QoE model denoted as <italic>LiteDC</i>, integrating a temporal dilated convolution network with a targeted pruning technique to align with the computational constraints of embedded platforms. Extensive results show that compared to a state-of-the-art baseline algorithm, <italic>LiteDC</i> achieves a remarkable 20.9-fold improvement in execution speed while increasing prediction accuracy by 6.4%. The <italic>MCQoE</i> dataset is available for download at <uri>https://github.com/yanghaocuc/mcqoe</uri>.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"277-290"},"PeriodicalIF":3.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207653","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 : 2024-08-23DOI: 10.1109/TBC.2024.3394293
Qian Huang;Xueguang Yuan;Xiaoyin Yi;Qingming Xie;Qin Jiang;Bingxin Wang
The advent of 5G technology and new energy radio communication systems heralds a significant shift in the landscape of automated driving. This paper focuses on the integration of 5G and broadcast services in the realm of new energy automatic assisted driving, emphasizing the importance of reliable, energy-efficient communication in the large-scale IoV. The enhanced capabilities of 5G enable improved vehicle battery endurance while safeguarding user data privacy and road traffic safety. We introduce RIS relay reflection as a novel approach to optimize non-line-of-sight links, presenting a RIS-assisted communication model tailored for 5G-enhanced large-scale IoV. The paper evaluates the trustworthiness of RIS relays using user behavior data, proposing a reliable and energy-efficient communication scheme that incorporates RIS security relay assistance. This scheme ensures the selection of trustworthy relays, synergizing the beam direction of transmitters and RISs for optimal 5G broadcast service delivery and OTA updates. Our approach promises to revolutionize communication in large-scale IoV systems, paving the way for a more connected and efficient future in automated vehicle networks.
{"title":"Enhancing 5G Broadcast Services in Large-Scale IoV Networks Using Reliable RIS Relaying","authors":"Qian Huang;Xueguang Yuan;Xiaoyin Yi;Qingming Xie;Qin Jiang;Bingxin Wang","doi":"10.1109/TBC.2024.3394293","DOIUrl":"10.1109/TBC.2024.3394293","url":null,"abstract":"The advent of 5G technology and new energy radio communication systems heralds a significant shift in the landscape of automated driving. This paper focuses on the integration of 5G and broadcast services in the realm of new energy automatic assisted driving, emphasizing the importance of reliable, energy-efficient communication in the large-scale IoV. The enhanced capabilities of 5G enable improved vehicle battery endurance while safeguarding user data privacy and road traffic safety. We introduce RIS relay reflection as a novel approach to optimize non-line-of-sight links, presenting a RIS-assisted communication model tailored for 5G-enhanced large-scale IoV. The paper evaluates the trustworthiness of RIS relays using user behavior data, proposing a reliable and energy-efficient communication scheme that incorporates RIS security relay assistance. This scheme ensures the selection of trustworthy relays, synergizing the beam direction of transmitters and RISs for optimal 5G broadcast service delivery and OTA updates. Our approach promises to revolutionize communication in large-scale IoV systems, paving the way for a more connected and efficient future in automated vehicle networks.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1104-1112"},"PeriodicalIF":3.2,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207654","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 : 2024-08-21DOI: 10.1109/TBC.2024.3443470
Jingbo He;Xiaohai He;Shuhua Xiong;Honggang Chen
The exponential growth in the volume of image data has imposed immense pressure on transmission and storage systems, while simultaneously presenting opportunities for intelligent image analysis towards machine vision. Recent years, learned image coding approach have made remarkable advancements with impressive performance. The application of the learned image coding method in machine vision holds promising prospects for achieving human-machine collaboration. In this paper, we propose a learned image coding approach based on Transformer-CNN interaction structure for human-machine vision collaborative optimization, which can generate a single and compact bitstream for efficient representation in image compression. The bitstream can be directly decoded to generate a reconstructed image for human visual perception. In parallel, without the need for decoding and reconstructing the image, the bitstream can serve as input for machine vision tasks. This not only reduces computational costs on the decoding end but also enhances machine analysis efficiency. Experimental results demonstrate that our proposed learned image coding method achieves a single bitstream that concurrently considers image reconstruction and machine task analysis, ensuring high accuracy in machine tasks and superior quality in reconstructed images compared to state-of-the-art (SOTA) methods.
{"title":"Learned Image Coding for Human-Machine Collaborative Optimization","authors":"Jingbo He;Xiaohai He;Shuhua Xiong;Honggang Chen","doi":"10.1109/TBC.2024.3443470","DOIUrl":"10.1109/TBC.2024.3443470","url":null,"abstract":"The exponential growth in the volume of image data has imposed immense pressure on transmission and storage systems, while simultaneously presenting opportunities for intelligent image analysis towards machine vision. Recent years, learned image coding approach have made remarkable advancements with impressive performance. The application of the learned image coding method in machine vision holds promising prospects for achieving human-machine collaboration. In this paper, we propose a learned image coding approach based on Transformer-CNN interaction structure for human-machine vision collaborative optimization, which can generate a single and compact bitstream for efficient representation in image compression. The bitstream can be directly decoded to generate a reconstructed image for human visual perception. In parallel, without the need for decoding and reconstructing the image, the bitstream can serve as input for machine vision tasks. This not only reduces computational costs on the decoding end but also enhances machine analysis efficiency. Experimental results demonstrate that our proposed learned image coding method achieves a single bitstream that concurrently considers image reconstruction and machine task analysis, ensuring high accuracy in machine tasks and superior quality in reconstructed images compared to state-of-the-art (SOTA) methods.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"203-216"},"PeriodicalIF":3.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207655","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 : 2024-08-20DOI: 10.1109/TBC.2024.3443469
Kuang-Hsun Lin;Ting-Wei Chen;Hung-Yu Wei
3GPP has established the Multicast Broadcast Services (MBS) standard to accommodate the escalating bandwidth demands of emerging applications like mixed reality and online gaming. MBS offers an efficient means of simultaneously delivering content to different users through the same wireless resources. However, the efficacy of grouping is intricately linked to user mobility and the channel quality of the weakest link. Notably, it is identified that handovers can cause significant interruptions in MBS transmissions. To address this, our paper introduces a novel dynamic grouping scheme capable of adapting to user mobility. Our results demonstrate superior performance compared to state-of-the-art methods without introducing much signaling overhead associated with MBS group management.
{"title":"Mobility-Enabled Dynamic Grouping for Multicast Broadcast Service","authors":"Kuang-Hsun Lin;Ting-Wei Chen;Hung-Yu Wei","doi":"10.1109/TBC.2024.3443469","DOIUrl":"https://doi.org/10.1109/TBC.2024.3443469","url":null,"abstract":"3GPP has established the Multicast Broadcast Services (MBS) standard to accommodate the escalating bandwidth demands of emerging applications like mixed reality and online gaming. MBS offers an efficient means of simultaneously delivering content to different users through the same wireless resources. However, the efficacy of grouping is intricately linked to user mobility and the channel quality of the weakest link. Notably, it is identified that handovers can cause significant interruptions in MBS transmissions. To address this, our paper introduces a novel dynamic grouping scheme capable of adapting to user mobility. Our results demonstrate superior performance compared to state-of-the-art methods without introducing much signaling overhead associated with MBS group management.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1167-1180"},"PeriodicalIF":3.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810676","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 : 2024-08-07DOI: 10.1109/TBC.2024.3437216
Vasileios P. Rekkas;Sotirios P. Sotiroudis;Lazaros Alexios Iliadis;Sander Bastiaens;Wout Joseph;David Plets;Christos G. Christodoulou;George K. Karagiannidis;Sotirios K. Goudos
Optical wireless communication (OWC) is emerging as a pivotal technology for next-generation broadcast networks, with visible light communication (VLC) poised to meet the escalating demands of advanced radio frequency systems. This study focuses on enhancing visible light positioning (VLP), recognized for its precision, simplicity, and cost-effectiveness, which are essential for accurate indoor localization and responsive location-based services. Central to our approach is the integration of advanced machine learning (ML) techniques, which fundamentally transform the accuracy and efficiency of 3D indoor positioning systems. We introduce an advanced VLP framework where ML is leveraged not merely as an adjunct but as the primary driver of innovation, significantly refining the processing of received signal strength (RSS) indicators. The methodology centers around a system comprising four light-emitting diodes (LEDs) arranged in a star geometry, optimized for precise spatial localization. We evaluate three distinct methodologies: a foundational star-shaped configuration for baseline position estimation, a repeated unit cell strategy to extend the four-LED configuration to a larger positioning area, and a sophisticated implementation employing Nyström kernel approximation. This integration of Nyström approximation into our ML framework drastically enhances the system’s predictive accuracy, achieving an exceptional average relative root mean square error (aRRMSE) of 2.1 cm in a simulated setup. The results demonstrate that ML, especially combined with the application of the Nyström kernel approximation, significantly elevates the precision and operational efficiency of traditional VLP systems, setting new benchmarks for accuracy in indoor 3D positioning technologies and fostering advancements towards more sophisticated and adaptable communication networks.
{"title":"Enhancing 3D Indoor Visible Light Positioning With Machine Learning Combined Nyström Kernel Approximation","authors":"Vasileios P. Rekkas;Sotirios P. Sotiroudis;Lazaros Alexios Iliadis;Sander Bastiaens;Wout Joseph;David Plets;Christos G. Christodoulou;George K. Karagiannidis;Sotirios K. Goudos","doi":"10.1109/TBC.2024.3437216","DOIUrl":"10.1109/TBC.2024.3437216","url":null,"abstract":"Optical wireless communication (OWC) is emerging as a pivotal technology for next-generation broadcast networks, with visible light communication (VLC) poised to meet the escalating demands of advanced radio frequency systems. This study focuses on enhancing visible light positioning (VLP), recognized for its precision, simplicity, and cost-effectiveness, which are essential for accurate indoor localization and responsive location-based services. Central to our approach is the integration of advanced machine learning (ML) techniques, which fundamentally transform the accuracy and efficiency of 3D indoor positioning systems. We introduce an advanced VLP framework where ML is leveraged not merely as an adjunct but as the primary driver of innovation, significantly refining the processing of received signal strength (RSS) indicators. The methodology centers around a system comprising four light-emitting diodes (LEDs) arranged in a star geometry, optimized for precise spatial localization. We evaluate three distinct methodologies: a foundational star-shaped configuration for baseline position estimation, a repeated unit cell strategy to extend the four-LED configuration to a larger positioning area, and a sophisticated implementation employing Nyström kernel approximation. This integration of Nyström approximation into our ML framework drastically enhances the system’s predictive accuracy, achieving an exceptional average relative root mean square error (aRRMSE) of 2.1 cm in a simulated setup. The results demonstrate that ML, especially combined with the application of the Nyström kernel approximation, significantly elevates the precision and operational efficiency of traditional VLP systems, setting new benchmarks for accuracy in indoor 3D positioning technologies and fostering advancements towards more sophisticated and adaptable communication networks.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1192-1206"},"PeriodicalIF":3.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940834","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 : 2024-08-05DOI: 10.1109/TBC.2024.3434736
Yu Zhao;Mao Ye;Luping Ji;Hongwei Guo;Ce Zhu
As the amount of surveillance video data increases at an exponential rate, the need for efficient video compression algorithms becomes increasingly urgent. The inter-frame compression schemes of existing surveillance video compression methods predict the current frame through the previous frame, causing the error to gradually increase because the quality of the reference frame decreases progressively. In this paper, we propose a Temporal Adaptive enhancement method for Learned surveillance video Compression (TALC). The proposed TALC has two modules: Forward Temporal Adaptive (FTA) module and Backward Temporal Adaptive (BTA) module which are put before and after motion and residual bits transmission modules respectively. These two modules have the same network structure which consists of a Temporal Adaptive Selection (TAS) block and a Feature Enhancement (FE) block. TAS block can analyze the extent which errors accumulate in optical flow and residuals, then select the corresponding enhancement sub-block; while FE block consists of several enhancement sub-blocks according to different levels of error accumulation. The proposed TALC has strong versatility and low coupling, which can be applied in almost all learned video compression frameworks as a plugin. Experimental results show that the proposed TALC method can significantly improve the coding performance of learned surveillance video compression networks without changing the original basic structure.
{"title":"Temporal Adaptive Learned Surveillance Video Compression","authors":"Yu Zhao;Mao Ye;Luping Ji;Hongwei Guo;Ce Zhu","doi":"10.1109/TBC.2024.3434736","DOIUrl":"10.1109/TBC.2024.3434736","url":null,"abstract":"As the amount of surveillance video data increases at an exponential rate, the need for efficient video compression algorithms becomes increasingly urgent. The inter-frame compression schemes of existing surveillance video compression methods predict the current frame through the previous frame, causing the error to gradually increase because the quality of the reference frame decreases progressively. In this paper, we propose a Temporal Adaptive enhancement method for Learned surveillance video Compression (TALC). The proposed TALC has two modules: Forward Temporal Adaptive (FTA) module and Backward Temporal Adaptive (BTA) module which are put before and after motion and residual bits transmission modules respectively. These two modules have the same network structure which consists of a Temporal Adaptive Selection (TAS) block and a Feature Enhancement (FE) block. TAS block can analyze the extent which errors accumulate in optical flow and residuals, then select the corresponding enhancement sub-block; while FE block consists of several enhancement sub-blocks according to different levels of error accumulation. The proposed TALC has strong versatility and low coupling, which can be applied in almost all learned video compression frameworks as a plugin. Experimental results show that the proposed TALC method can significantly improve the coding performance of learned surveillance video compression networks without changing the original basic structure.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"142-153"},"PeriodicalIF":3.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969069","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 : 2024-08-05DOI: 10.1109/TBC.2024.3434625
Hao Chang;Renlong Han;Chengye Jiang;Guichen Yang;Qianqian Zhang;Junsen Wang;Falin Liu
This paper proposes a dual feature indexed quadratic polynomial-based piecewise (DIQP) behavioral modeling technique for digital predistortion (DPD) of RF transmitters. The proposed DIQP model is used to find the most suitable DPD model by performing a dual feature classification on the optimized submodels with a reuse-based function screening algorithm. The optimized submodel is adapted from the previous instantaneous sample indexed magnitude-selective affine (I-MSA) function-based model by transforming the original single linear term into a quadratic term with stronger fitting ability. This key improvement not only enhances the flexibility of the model but also boosts its fitting capability. The segmentation rule of the piecewise model has evolved from a simple threshold segmentation to a dual feature segmentation based on threshold and clustering segments. This reconstruction provides the model with enhanced feature-building capabilities. Additionally, the corresponding hybrid basis function screening (HBFS) algorithm and running complexity identification algorithm based on basis function reuse are proposed. The ingenious design of this reuse-based function screening algorithm not only enhances running efficiency but also ensures the overall performance of the model. The experimental part uses two different power amplifiers (PAs) for behavioral modeling and linearization tests. And the results of the experiments prove that the screened DIQP model is able to achieve the linearization performance-complexity trade-off excellently.
{"title":"Dual Feature Indexed Quadratic Polynomial-Based Piecewise Behavioral Model for Digital Predistortion of RF Power Amplifiers","authors":"Hao Chang;Renlong Han;Chengye Jiang;Guichen Yang;Qianqian Zhang;Junsen Wang;Falin Liu","doi":"10.1109/TBC.2024.3434625","DOIUrl":"10.1109/TBC.2024.3434625","url":null,"abstract":"This paper proposes a dual feature indexed quadratic polynomial-based piecewise (DIQP) behavioral modeling technique for digital predistortion (DPD) of RF transmitters. The proposed DIQP model is used to find the most suitable DPD model by performing a dual feature classification on the optimized submodels with a reuse-based function screening algorithm. The optimized submodel is adapted from the previous instantaneous sample indexed magnitude-selective affine (I-MSA) function-based model by transforming the original single linear term into a quadratic term with stronger fitting ability. This key improvement not only enhances the flexibility of the model but also boosts its fitting capability. The segmentation rule of the piecewise model has evolved from a simple threshold segmentation to a dual feature segmentation based on threshold and clustering segments. This reconstruction provides the model with enhanced feature-building capabilities. Additionally, the corresponding hybrid basis function screening (HBFS) algorithm and running complexity identification algorithm based on basis function reuse are proposed. The ingenious design of this reuse-based function screening algorithm not only enhances running efficiency but also ensures the overall performance of the model. The experimental part uses two different power amplifiers (PAs) for behavioral modeling and linearization tests. And the results of the experiments prove that the screened DIQP model is able to achieve the linearization performance-complexity trade-off excellently.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1302-1315"},"PeriodicalIF":3.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940836","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 : 2024-08-05DOI: 10.1109/TBC.2024.3434745
Jinxin Zuo;Ziping Wang;Chenqing Guo;Weixuan Xie;Hao Wu;Peng Yu;Yueming Lu
This paper investigates the challenges of data trust sharing faced by Internet of Vehicles (IoV) network with 5G broadcast services. Particularly we develop a decentralized IoV reputation management model with spatiotemporal feature perception fusion (RMM-STFP) based on blockchain. The proposed reputation evaluation method evaluates the reputation value of nodes from the two aspects of time continuity and spatial transitivity and thus improves the identification accuracy of malicious nodes. To further accelerate the dissemination of reputation data, we have constructed a blockchain-based management storage system, where PBFT consensus scheme combines reputation and Bayesian inference. Finally, numerical results are given to justify the superiority of our proposed scheme. When proportion of malicious nodes reaches 45%, the accuracy of our proposed method is 94.5%, and the suppression rate of malicious messages is 83%. Moreover, compared with the traditional PBFT consensus scheme, the consensus delay and communication overhead are reduced by 87.57% and 78.45%, respectively, and the transaction throughput is increased by 70.65%.
{"title":"A Decentralized Reputation Management Model for Enhanced IoV Networks With 5G Broadcast Services","authors":"Jinxin Zuo;Ziping Wang;Chenqing Guo;Weixuan Xie;Hao Wu;Peng Yu;Yueming Lu","doi":"10.1109/TBC.2024.3434745","DOIUrl":"10.1109/TBC.2024.3434745","url":null,"abstract":"This paper investigates the challenges of data trust sharing faced by Internet of Vehicles (IoV) network with 5G broadcast services. Particularly we develop a decentralized IoV reputation management model with spatiotemporal feature perception fusion (RMM-STFP) based on blockchain. The proposed reputation evaluation method evaluates the reputation value of nodes from the two aspects of time continuity and spatial transitivity and thus improves the identification accuracy of malicious nodes. To further accelerate the dissemination of reputation data, we have constructed a blockchain-based management storage system, where PBFT consensus scheme combines reputation and Bayesian inference. Finally, numerical results are given to justify the superiority of our proposed scheme. When proportion of malicious nodes reaches 45%, the accuracy of our proposed method is 94.5%, and the suppression rate of malicious messages is 83%. Moreover, compared with the traditional PBFT consensus scheme, the consensus delay and communication overhead are reduced by 87.57% and 78.45%, respectively, and the transaction throughput is increased by 70.65%.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"63-73"},"PeriodicalIF":3.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940841","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}
Light field imaging captures both the intensity and directional information of light rays, providing users with more immersive visual experience. However, during the processes of imaging, processing, coding and reconstruction, light field images (LFIs) may encounter various distortions that degrade their visual quality. Compared to two-dimensional image quality assessment, light field image quality assessment (LFIQA) needs to consider not only the image quality in the spatial domain but also the quality degradation in the angular domain. To effectively model the factors related to visual perception and LFI quality, this paper proposes a multi-scale attention feature fusion based blind LFIQA metric, named MAFBLiF. The proposed metric consists of the following parts: MLI-Patch generation, spatial-angular feature separation module, spatial-angular feature extraction backbone network, pyramid feature alignment module and patch attention module. These modules are specifically designed to extract spatial and angular information of LFIs, and capture multi-level information and regions of interest. Furthermore, a pooling scheme guided by the LFI’s gradient information and saliency is proposed, which integrates the quality of all MLI-patches into the overall quality of the input LFI. Finally, to demonstrate the effectiveness of the proposed metric, extensive experiments are conducted on three representative LFI quality evaluation datasets. The experimental results show that the proposed metric outperforms other state-of-the-art image quality assessment metrics. The code will be publicly available at https://github.com/oldblackfish/MAFBLiF .
{"title":"MAFBLiF: Multi-Scale Attention Feature Fusion-Based Blind Light Field Image Quality Assessment","authors":"Rui Zhou;Gangyi Jiang;Yueli Cui;Yeyao Chen;Haiyong Xu;Ting Luo;Mei Yu","doi":"10.1109/TBC.2024.3434699","DOIUrl":"10.1109/TBC.2024.3434699","url":null,"abstract":"Light field imaging captures both the intensity and directional information of light rays, providing users with more immersive visual experience. However, during the processes of imaging, processing, coding and reconstruction, light field images (LFIs) may encounter various distortions that degrade their visual quality. Compared to two-dimensional image quality assessment, light field image quality assessment (LFIQA) needs to consider not only the image quality in the spatial domain but also the quality degradation in the angular domain. To effectively model the factors related to visual perception and LFI quality, this paper proposes a multi-scale attention feature fusion based blind LFIQA metric, named MAFBLiF. The proposed metric consists of the following parts: MLI-Patch generation, spatial-angular feature separation module, spatial-angular feature extraction backbone network, pyramid feature alignment module and patch attention module. These modules are specifically designed to extract spatial and angular information of LFIs, and capture multi-level information and regions of interest. Furthermore, a pooling scheme guided by the LFI’s gradient information and saliency is proposed, which integrates the quality of all MLI-patches into the overall quality of the input LFI. Finally, to demonstrate the effectiveness of the proposed metric, extensive experiments are conducted on three representative LFI quality evaluation datasets. The experimental results show that the proposed metric outperforms other state-of-the-art image quality assessment metrics. The code will be publicly available at \u0000<uri>https://github.com/oldblackfish/MAFBLiF</uri>\u0000.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1266-1278"},"PeriodicalIF":3.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940842","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}
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