Pub Date : 2024-09-19DOI: 10.1109/TBC.2024.3417228
Iñigo Bilbao;Eneko Iradier;Jon Montalban;Pablo Angueira;Sung-Ik Park
Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged as viable alternatives to conventional Physical Layer (PHY) signal processing methods. Specifically, in any wireless point-to-multipoint communication, accurate channel estimation plays a pivotal role in exploiting spectrum efficiency with functionalities such as higher-order modulation or full-duplex communication. This research paper proposes leveraging ML solutions, including Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs), to enhance channel estimation within broadcast environments. Each architecture is instantiated using distinct procedures, focusing on two fundamental approaches: channel estimation denoising and ML-assisted pilot interpolation. Rigorous evaluations are conducted across diverse configurations and conditions, spanning rural areas and co-channel interference scenarios. The results demonstrate that MLP and CNN architectures consistently outperform classical methods, yielding 10 and 20 dB performance improvements, respectively. These results underscore the efficacy of ML-driven approaches in advancing channel estimation capabilities for broadcast communication systems.
{"title":"Enhancing Channel Estimation in Terrestrial Broadcast Communications Using Machine Learning","authors":"Iñigo Bilbao;Eneko Iradier;Jon Montalban;Pablo Angueira;Sung-Ik Park","doi":"10.1109/TBC.2024.3417228","DOIUrl":"https://doi.org/10.1109/TBC.2024.3417228","url":null,"abstract":"Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged as viable alternatives to conventional Physical Layer (PHY) signal processing methods. Specifically, in any wireless point-to-multipoint communication, accurate channel estimation plays a pivotal role in exploiting spectrum efficiency with functionalities such as higher-order modulation or full-duplex communication. This research paper proposes leveraging ML solutions, including Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs), to enhance channel estimation within broadcast environments. Each architecture is instantiated using distinct procedures, focusing on two fundamental approaches: channel estimation denoising and ML-assisted pilot interpolation. Rigorous evaluations are conducted across diverse configurations and conditions, spanning rural areas and co-channel interference scenarios. The results demonstrate that MLP and CNN architectures consistently outperform classical methods, yielding 10 and 20 dB performance improvements, respectively. These results underscore the efficacy of ML-driven approaches in advancing channel estimation capabilities for broadcast communication systems.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1181-1191"},"PeriodicalIF":3.2,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810675","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 : 2024-09-16DOI: 10.1109/TBC.2024.3453631
{"title":"IEEE Transactions on Broadcasting Information for Authors","authors":"","doi":"10.1109/TBC.2024.3453631","DOIUrl":"https://doi.org/10.1109/TBC.2024.3453631","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"C3-C4"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235706","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}
Pub Date : 2024-09-16DOI: 10.1109/TBC.2024.3434731
Shuai Han;Zhiqiang Li;Weixiao Meng;Cheng Li
The multibeam satellite system is crucial for providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within a limited spectrum of resources presents formidable challenges. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using rate-splitting multiple access (RSMA), which leverages non-orthogonal transmission and precoding strategies. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose two novel distributed frequency reuse (DFR) and centralized frequency reuse (CFR) strategies. Taking satellite beam gain characteristics and interference tolerance threshold into consideration, we further propose another two expansions of DFR and CFR strategies with innovative inner and outer divisions. For the NOBU joint transmission based on four novel frequency reuse patterns, we maximize the weighted sum rate (WSR). Subsequently, we introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problems into tractable convex counterparts. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.
{"title":"Next-Gen Satellite System: Integrative Non-Orthogonal Broadcast and Unicast Services Based on Innovative Frequency Reuse Patterns","authors":"Shuai Han;Zhiqiang Li;Weixiao Meng;Cheng Li","doi":"10.1109/TBC.2024.3434731","DOIUrl":"10.1109/TBC.2024.3434731","url":null,"abstract":"The multibeam satellite system is crucial for providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within a limited spectrum of resources presents formidable challenges. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using rate-splitting multiple access (RSMA), which leverages non-orthogonal transmission and precoding strategies. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose two novel distributed frequency reuse (DFR) and centralized frequency reuse (CFR) strategies. Taking satellite beam gain characteristics and interference tolerance threshold into consideration, we further propose another two expansions of DFR and CFR strategies with innovative inner and outer divisions. For the NOBU joint transmission based on four novel frequency reuse patterns, we maximize the weighted sum rate (WSR). Subsequently, we introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problems into tractable convex counterparts. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1153-1166"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263748","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 : 2024-09-16DOI: 10.1109/TBC.2024.3453611
{"title":"IEEE Transactions on Broadcasting Information for Authors","authors":"","doi":"10.1109/TBC.2024.3453611","DOIUrl":"https://doi.org/10.1109/TBC.2024.3453611","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"C3-C4"},"PeriodicalIF":3.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235712","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}
Existing no reference image quality assessment(NR-IQA) methods have not incorporated image semantics explicitly in the assessment process, thus overlooking the significant correlation between image content and its quality. To address this gap, we leverages image semantics as guiding information for quality assessment, integrating it explicitly into the NR-IQA process through a Semantic-Guided NR-IQA model(SGIQA), which is based on the Swin Transformer. Specifically, we introduce a Semantic Attention Module and a Perceptual Rule Learning Module. The Semantic Attention Module refines the features extracted by the deep network according to the image content, allowing the network to dynamically extract quality perceptual features according to the semantic context of the image. The Perceptual Rule Learning Module generates parameters for the image quality regression module tailored to the image content, facilitating a dynamic assessment of image quality based on its semantic information. Employing the Swin Transformer and integrating these two modules, we have developed the final semantic-guided NR-IQA model. Extensive experiments on five widely-used IQA datasets demonstrate that our method not only exhibits excellent generalization capabilities but also achieves state-of-the-art performance.
{"title":"SGIQA: Semantic-Guided No-Reference Image Quality Assessment","authors":"Linpeng Pan;Xiaozhe Zhang;Fengying Xie;Haopeng Zhang;Yushan Zheng","doi":"10.1109/TBC.2024.3450320","DOIUrl":"10.1109/TBC.2024.3450320","url":null,"abstract":"Existing no reference image quality assessment(NR-IQA) methods have not incorporated image semantics explicitly in the assessment process, thus overlooking the significant correlation between image content and its quality. To address this gap, we leverages image semantics as guiding information for quality assessment, integrating it explicitly into the NR-IQA process through a Semantic-Guided NR-IQA model(SGIQA), which is based on the Swin Transformer. Specifically, we introduce a Semantic Attention Module and a Perceptual Rule Learning Module. The Semantic Attention Module refines the features extracted by the deep network according to the image content, allowing the network to dynamically extract quality perceptual features according to the semantic context of the image. The Perceptual Rule Learning Module generates parameters for the image quality regression module tailored to the image content, facilitating a dynamic assessment of image quality based on its semantic information. Employing the Swin Transformer and integrating these two modules, we have developed the final semantic-guided NR-IQA model. Extensive experiments on five widely-used IQA datasets demonstrate that our method not only exhibits excellent generalization capabilities but also achieves state-of-the-art performance.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1292-1301"},"PeriodicalIF":3.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207648","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 : 2024-09-06DOI: 10.1109/TBC.2024.3443466
Meixia Hu;Jingqing Wang;Wenchi Cheng;Hailin Zhang
Since the inherent high envelope fluctuation characteristics of OFDM signals present a significant challenge in reducing energy consumption, it is crucial to minimize the range of the envelope fluctuations of OFDM signals. As companding is a well-known technique for reducing the envelope fluctuations of OFDM signals, we explore the optimal companding transform by building a multi-objective optimization model with the goal of minimizing peak-to-average power ratio (PAPR), inner-band distortions, and out-of-band (OOB) radiations in this paper. The solution reveals that the optimal form of companding transform is a piecewise one and closely resembles a linear transform. Furthermore, we find that the average power of the optimal companded signal is never greater than that of the original signal, which contradicts the constraint of constant average signal power usually used in the design of companding transform. Based on the characteristics of the optimal companding transform, we propose a near-optimal piecewise linear companding transform to obviate the extremely high computational complexity of the optimal companding transform. The proposed near-optimal piecewise linear companding transform is a promising solution for mitigating companding distortions while reducing PAPR. However, it should be noted that there may still be some unavoidable distortions after decompanding, which results in a degradation of the BER performance. Thus, we diminish the remaining distortions after decompanding by relaxing the constraint of the proposed near-optimal piecewise linear companding transform on the average power of the companded signals. Simulation results demonstrate that the relaxation can improve the BER performance while ensuring the PAPR performance with only a small sacrifice on OOB radiations.
{"title":"Near-Optimal Piecewise Linear Companding Transform for PAPR Reduction of OFDM Systems","authors":"Meixia Hu;Jingqing Wang;Wenchi Cheng;Hailin Zhang","doi":"10.1109/TBC.2024.3443466","DOIUrl":"10.1109/TBC.2024.3443466","url":null,"abstract":"Since the inherent high envelope fluctuation characteristics of OFDM signals present a significant challenge in reducing energy consumption, it is crucial to minimize the range of the envelope fluctuations of OFDM signals. As companding is a well-known technique for reducing the envelope fluctuations of OFDM signals, we explore the optimal companding transform by building a multi-objective optimization model with the goal of minimizing peak-to-average power ratio (PAPR), inner-band distortions, and out-of-band (OOB) radiations in this paper. The solution reveals that the optimal form of companding transform is a piecewise one and closely resembles a linear transform. Furthermore, we find that the average power of the optimal companded signal is never greater than that of the original signal, which contradicts the constraint of constant average signal power usually used in the design of companding transform. Based on the characteristics of the optimal companding transform, we propose a near-optimal piecewise linear companding transform to obviate the extremely high computational complexity of the optimal companding transform. The proposed near-optimal piecewise linear companding transform is a promising solution for mitigating companding distortions while reducing PAPR. However, it should be noted that there may still be some unavoidable distortions after decompanding, which results in a degradation of the BER performance. Thus, we diminish the remaining distortions after decompanding by relaxing the constraint of the proposed near-optimal piecewise linear companding transform on the average power of the companded signals. Simulation results demonstrate that the relaxation can improve the BER performance while ensuring the PAPR performance with only a small sacrifice on OOB radiations.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"350-359"},"PeriodicalIF":3.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207651","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}
Learning-based point cloud compression has achieved great success in Rate-Distortion (RD) efficiency. Existing methods usually utilize Variational AutoEncoder (VAE) network, which might lead to poor detail reconstruction and high computational complexity. To address these issues, we propose a Scale-adaptive Asymmetric Sparse Variational AutoEncoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which exploits multi-resolution branches to aggregate multiscale features at encoder, and excludes symmetric feature fusion branches to control the model complexity at decoder. Second, we design a Scale Adaptive Feature Refinement Structure (SAFRS) to adaptively adjust the number of Feature Refinement Modules (FRMs), thereby improving RD performance with an acceptable computational overhead. Third, we implement our framework with AMSC and SAFRS, and train it with an RD loss based on Fine-grained Weighted Binary Cross-Entropy (FWBCE) function. Experimental results on 8iVFB, Owlii, and MVUV datasets show that our method outperforms several popular methods, with a 90.0% time reduction and a 51.8% BD-BR saving compared with V-PCC. The code will be available soon at https://github.com/fancj2017/SAS-VAE .
{"title":"Scale-Adaptive Asymmetric Sparse Variational AutoEncoder for Point Cloud Compression","authors":"Jian Chen;Yingtao Zhu;Wei Huang;Chengdong Lan;Tiesong Zhao","doi":"10.1109/TBC.2024.3437161","DOIUrl":"10.1109/TBC.2024.3437161","url":null,"abstract":"Learning-based point cloud compression has achieved great success in Rate-Distortion (RD) efficiency. Existing methods usually utilize Variational AutoEncoder (VAE) network, which might lead to poor detail reconstruction and high computational complexity. To address these issues, we propose a Scale-adaptive Asymmetric Sparse Variational AutoEncoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which exploits multi-resolution branches to aggregate multiscale features at encoder, and excludes symmetric feature fusion branches to control the model complexity at decoder. Second, we design a Scale Adaptive Feature Refinement Structure (SAFRS) to adaptively adjust the number of Feature Refinement Modules (FRMs), thereby improving RD performance with an acceptable computational overhead. Third, we implement our framework with AMSC and SAFRS, and train it with an RD loss based on Fine-grained Weighted Binary Cross-Entropy (FWBCE) function. Experimental results on 8iVFB, Owlii, and MVUV datasets show that our method outperforms several popular methods, with a 90.0% time reduction and a 51.8% BD-BR saving compared with V-PCC. The code will be available soon at \u0000<uri>https://github.com/fancj2017/SAS-VAE</uri>\u0000.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"884-894"},"PeriodicalIF":3.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207649","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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