Pub Date : 2025-03-11DOI: 10.1109/TBC.2025.3541860
Chunguang Li;Dayoung Lee;Minseok Song
360-degree videos inherently require significant storage space because each segment consists of many tiles, each of which is further transcoded and stored in multiple versions. It is thus impractical to store all transcoded versions, which makes it essential to make effective use of limited storage space. However, the inefficiency of existing heuristic-based management schemes arises from the challenge of incorporating various factors, such as variable bandwidth requirements influenced by network conditions, tile access distribution, and video quality dependent on content. To address this, we propose a new storage space management scheme, which combines the dueling deep Q-network (DQN) algorithm based on the field-of-view (FoV) distribution and the greedy algorithm that considers the overall video popularity. We first model an environment in which the agent can determine the versions for each tile to achieve the best video quality under various storage limit conditions. The dueling DQN environment comprises 1) an action space determining version combinations for each tile within specified storage limits, 2) an observation space enabling the agent to learn variable bandwidths and tile access distributions, and 3) a reward model deriving the expected video quality for different actions. Building upon the dueling DQN model correlating storage limits with expected video quality, we present a greedy algorithm that selects versions among multiple videos within storage limits for the purpose of maximizing popularity-weighted video quality. Extensive simulations evaluated the proposed scheme under various storage limits, bandwidth changes, and FoV distributions, demonstrating an improvement in overall popularity-weighted video quality ranging from 0.49% to 37.77% (with an average improvement of 13.96%) compared to existing benchmark schemes.
{"title":"Using Deep Reinforcement Learning (DRL) to Optimize Quality in 360-Degree Video Tile Management","authors":"Chunguang Li;Dayoung Lee;Minseok Song","doi":"10.1109/TBC.2025.3541860","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541860","url":null,"abstract":"360-degree videos inherently require significant storage space because each segment consists of many tiles, each of which is further transcoded and stored in multiple versions. It is thus impractical to store all transcoded versions, which makes it essential to make effective use of limited storage space. However, the inefficiency of existing heuristic-based management schemes arises from the challenge of incorporating various factors, such as variable bandwidth requirements influenced by network conditions, tile access distribution, and video quality dependent on content. To address this, we propose a new storage space management scheme, which combines the dueling deep Q-network (DQN) algorithm based on the field-of-view (FoV) distribution and the greedy algorithm that considers the overall video popularity. We first model an environment in which the agent can determine the versions for each tile to achieve the best video quality under various storage limit conditions. The dueling DQN environment comprises 1) an action space determining version combinations for each tile within specified storage limits, 2) an observation space enabling the agent to learn variable bandwidths and tile access distributions, and 3) a reward model deriving the expected video quality for different actions. Building upon the dueling DQN model correlating storage limits with expected video quality, we present a greedy algorithm that selects versions among multiple videos within storage limits for the purpose of maximizing popularity-weighted video quality. Extensive simulations evaluated the proposed scheme under various storage limits, bandwidth changes, and FoV distributions, demonstrating an improvement in overall popularity-weighted video quality ranging from 0.49% to 37.77% (with an average improvement of 13.96%) compared to existing benchmark schemes.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"555-569"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243592","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}
The evolution of 5G and Beyond 5G (B5G) networks has intensified the demand for efficient Multimedia Broadcast Multicast Services (MBMS), particularly in dynamic edge environments. The frequent alterations in network topology and multicast group configurations in these environments present substantial scalability challenges for traditional IP MultiCast (IPMC) mechanisms. Bit Index Explicit Replication (BIER) offers a stateless IPMC alternative that mitigates the limitations of traditional IPMC mechanisms. However, it still encounters fault tolerance issues in dynamic edge networks, where link faults occur frequently. This paper propose a Fault-Tolerant BIER Multicast (FTBM) mechanism specifically designed for MBMS in dynamic edge networks. FTBM optimizes BIER multicast paths by employing Multi-Agent Deep Reinforcement Learning (MADRL) to minimize transmission delays while addressing constraints such as random link faults, limited queue capacity, and forwarding restrictions. Extensive simulations demonstrate that FTBM significantly enhances multicast performance under varying traffic loads and dense fault conditions, leading to improved transmission efficiency and network load balancing. This work provides a resilient and scalable solution for next-generation MBMS in dynamic network environments.
5G和超5G （B5G）网络的发展加剧了对高效多媒体广播多播服务（MBMS）的需求，特别是在动态边缘环境中。在这些环境中，网络拓扑结构和组播组配置的频繁变化给传统的IP组播（IPMC）机制带来了巨大的可扩展性挑战。Bit Index Explicit Replication （BIER）提供了一种无状态的IPMC替代方案，减轻了传统IPMC机制的局限性。但是，在链路故障频繁发生的动态边缘网络中，仍然存在容错问题。针对动态边缘网络中的MBMS，提出了一种容错BIER组播（FTBM）机制。FTBM通过使用多智能体深度强化学习（MADRL）来优化BIER组播路径，以最大限度地减少传输延迟，同时解决诸如随机链路故障，有限队列容量和转发限制等约束。大量的仿真结果表明，在不同的流量负载和密集的故障条件下，FTBM可以显著提高组播性能，从而提高传输效率和网络负载均衡。这项工作为动态网络环境下的下一代MBMS提供了弹性和可扩展的解决方案。
{"title":"FTBM: A Fault-Tolerant BIER Multicast for MBMS in 5G/B5G Dynamic Edge Networks","authors":"Honglin Fang;Peng Yu;Xinxiu Liu;Ying Wang;Wenjing Li;Xuesong Qiu;Zhaowei Qu","doi":"10.1109/TBC.2025.3541889","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541889","url":null,"abstract":"The evolution of 5G and Beyond 5G (B5G) networks has intensified the demand for efficient Multimedia Broadcast Multicast Services (MBMS), particularly in dynamic edge environments. The frequent alterations in network topology and multicast group configurations in these environments present substantial scalability challenges for traditional IP MultiCast (IPMC) mechanisms. Bit Index Explicit Replication (BIER) offers a stateless IPMC alternative that mitigates the limitations of traditional IPMC mechanisms. However, it still encounters fault tolerance issues in dynamic edge networks, where link faults occur frequently. This paper propose a Fault-Tolerant BIER Multicast (FTBM) mechanism specifically designed for MBMS in dynamic edge networks. FTBM optimizes BIER multicast paths by employing Multi-Agent Deep Reinforcement Learning (MADRL) to minimize transmission delays while addressing constraints such as random link faults, limited queue capacity, and forwarding restrictions. Extensive simulations demonstrate that FTBM significantly enhances multicast performance under varying traffic loads and dense fault conditions, leading to improved transmission efficiency and network load balancing. This work provides a resilient and scalable solution for next-generation MBMS in dynamic network environments.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"411-425"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243676","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 : 2025-03-05DOI: 10.1109/TBC.2025.3542626
{"title":"IEEE Transactions on Broadcasting Information for Authors","authors":"","doi":"10.1109/TBC.2025.3542626","DOIUrl":"https://doi.org/10.1109/TBC.2025.3542626","url":null,"abstract":"","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"C3-C4"},"PeriodicalIF":3.2,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10913472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553154","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 : 2025-03-04DOI: 10.1109/TBC.2025.3541862
Yipeng Liu;Qi Yang;Yujie Zhang;Yiling Xu;Le Yang;Xiaozhong Xu;Shan Liu
The visual quality of point clouds plays a crucial role in the development and broadcasting of immersive media. Therefore, investigating point cloud quality assessment (PCQA) is instrumental in facilitating immersive media applications, including virtual reality and augmented reality applications. Considering reference point clouds are not available in many cases, no-reference (NR) metrics have become a research hotspot. Existing NR methods suffer from difficult training. To address this shortcoming, we propose a novel NR-PCQA method, Point Cloud Quality Assessment via Domain-relevance Degradation Description (D3-PCQA). First, we demonstrate our model’s interpretability by deriving the function of each module using a kernelized ridge regression model. Specifically, quality assessment can be characterized as a leap from the scattered perceptual domain (reflecting subjective perception) to the ordered quality domain (reflecting mean opinion score). Second, to reduce the significant domain discrepancy, we establish an intermediate domain, the description domain, based on insights from the human visual system (HVS), by considering the domain relevance among samples located in the perception domain and learning a structured latent space. The anchor features derived from the learned latent space are generated as cross-domain auxiliary information to promote domain transformation. Furthermore, the newly established description domain decomposes the NR-PCQA problem into two relevant stages. These stages include a classification stage that gives the degradation descriptions to point clouds and a regression stage to determine the confidence degrees of descriptions, providing a semantic explanation for the predicted quality scores. Experimental results demonstrate that D3-PCQA exhibits robust performance and outstanding generalization on several publicly available datasets.
{"title":"Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-Relevance Degradation Description","authors":"Yipeng Liu;Qi Yang;Yujie Zhang;Yiling Xu;Le Yang;Xiaozhong Xu;Shan Liu","doi":"10.1109/TBC.2025.3541862","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541862","url":null,"abstract":"The visual quality of point clouds plays a crucial role in the development and broadcasting of immersive media. Therefore, investigating point cloud quality assessment (PCQA) is instrumental in facilitating immersive media applications, including virtual reality and augmented reality applications. Considering reference point clouds are not available in many cases, no-reference (NR) metrics have become a research hotspot. Existing NR methods suffer from difficult training. To address this shortcoming, we propose a novel NR-PCQA method, Point Cloud Quality Assessment via Domain-relevance Degradation Description (D3-PCQA). First, we demonstrate our model’s interpretability by deriving the function of each module using a kernelized ridge regression model. Specifically, quality assessment can be characterized as a leap from the scattered perceptual domain (reflecting subjective perception) to the ordered quality domain (reflecting mean opinion score). Second, to reduce the significant domain discrepancy, we establish an intermediate domain, the description domain, based on insights from the human visual system (HVS), by considering the domain relevance among samples located in the perception domain and learning a structured latent space. The anchor features derived from the learned latent space are generated as cross-domain auxiliary information to promote domain transformation. Furthermore, the newly established description domain decomposes the NR-PCQA problem into two relevant stages. These stages include a classification stage that gives the degradation descriptions to point clouds and a regression stage to determine the confidence degrees of descriptions, providing a semantic explanation for the predicted quality scores. Experimental results demonstrate that D3-PCQA exhibits robust performance and outstanding generalization on several publicly available datasets.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"616-630"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243586","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 : 2025-03-01DOI: 10.1109/TBC.2025.3559003
Lei Luo;Ziyang He;Junjie Wu;Hongwei Guo;Ce Zhu
Deep learning based joint source-channel coding (DJSCC) has recently made significant progress and emerged as a potential solution for future wireless communication. However, there are still several crucial issues that necessitate further in-depth exploration to enhance the efficiency of DJSCC, such as channel quality adaptability, bandwidth adaptability, and the delicate balance between efficiency and complexity. This work proposes an adaptive deep joint source-channel coding scheme tailored for one-to-many wireless image transmission scenarios (ADMIT). First, to effectively improve transmission performance, neighboring attention is introduced as the backbone for the proposed ADMIT method. Second, a channel quality adaptive module (CQAM) is designed based on multi-scale feature fusion, which seamlessly adapts to fluctuating channel conditions across a wide range of channel signal-to-noise ratios (CSNRs). Third, to be precisely tailored to different bandwidth resources, the channel gained adaptive module (CGAM) dynamically adjusts the significance of individual channels within the latent space, which ensures seamless varying bandwidth accommodation with a single model through bandwidth adaptation and symbol completion. Additionally, to mitigate the imbalance of loss across multiple bandwidth ratios during the training process, the gradient normalization (GradNorm) based training strategy is leveraged to ensure adaptive balancing of loss decreasing. The extensive experimental results demonstrate that the proposed method significantly enhances transmission performance while maintaining relatively low computational complexity. The source codes are available at: https://github.com/llsurreal919/ADMIT.
{"title":"Adaptive Deep Joint Source-Channel Coding for One-to-Many Wireless Image Transmission","authors":"Lei Luo;Ziyang He;Junjie Wu;Hongwei Guo;Ce Zhu","doi":"10.1109/TBC.2025.3559003","DOIUrl":"https://doi.org/10.1109/TBC.2025.3559003","url":null,"abstract":"Deep learning based joint source-channel coding (DJSCC) has recently made significant progress and emerged as a potential solution for future wireless communication. However, there are still several crucial issues that necessitate further in-depth exploration to enhance the efficiency of DJSCC, such as channel quality adaptability, bandwidth adaptability, and the delicate balance between efficiency and complexity. This work proposes an <underline>a</u>daptive <underline>d</u>eep joint source-channel coding scheme tailored for one-to-<underline>m</u>any wireless <underline>i</u>mage <underline>t</u>ransmission scenarios (ADMIT). First, to effectively improve transmission performance, neighboring attention is introduced as the backbone for the proposed ADMIT method. Second, a channel quality adaptive module (CQAM) is designed based on multi-scale feature fusion, which seamlessly adapts to fluctuating channel conditions across a wide range of channel signal-to-noise ratios (CSNRs). Third, to be precisely tailored to different bandwidth resources, the channel gained adaptive module (CGAM) dynamically adjusts the significance of individual channels within the latent space, which ensures seamless varying bandwidth accommodation with a single model through bandwidth adaptation and symbol completion. Additionally, to mitigate the imbalance of loss across multiple bandwidth ratios during the training process, the gradient normalization (GradNorm) based training strategy is leveraged to ensure adaptive balancing of loss decreasing. The extensive experimental results demonstrate that the proposed method significantly enhances transmission performance while maintaining relatively low computational complexity. The source codes are available at: <uri>https://github.com/llsurreal919/ADMIT</uri>.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 3","pages":"914-929"},"PeriodicalIF":4.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998093","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 : 2025-02-27DOI: 10.1109/TBC.2025.3541869
Haifeng Guo;Sam Kwong;Mingliang Zhou
Deep video compression methods typically use autoencoder-style networks for encoding and decoding, which can result in the loss of information during encoding that cannot be retrieved during decoding. To address this issue, recent work has explored the use of invertible neural networks for enhanced invertible encoding, which has successfully mitigated spatial information loss for better image compression. In this paper, we propose a new approach that extends invertible encoding to temporal information and introduces an encoding-decoding network for deep video compression. Our network incorporates a novel attentive channel squeeze module to improve compression performance while also leveraging a conditional coding framework for motion compression. The entire framework is optimized via a single loss function that balances bit cost and frame quality. The experimental results demonstrate the effectiveness of our approach, which achieves 15.45%/57.92% bit savings in terms of PSNR/MS-SSIM compared with the high-efficiency video coding low-delay P configuration.
{"title":"Exploring Invertible Encoding for Deep Video Compression","authors":"Haifeng Guo;Sam Kwong;Mingliang Zhou","doi":"10.1109/TBC.2025.3541869","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541869","url":null,"abstract":"Deep video compression methods typically use autoencoder-style networks for encoding and decoding, which can result in the loss of information during encoding that cannot be retrieved during decoding. To address this issue, recent work has explored the use of invertible neural networks for enhanced invertible encoding, which has successfully mitigated spatial information loss for better image compression. In this paper, we propose a new approach that extends invertible encoding to temporal information and introduces an encoding-decoding network for deep video compression. Our network incorporates a novel attentive channel squeeze module to improve compression performance while also leveraging a conditional coding framework for motion compression. The entire framework is optimized via a single loss function that balances bit cost and frame quality. The experimental results demonstrate the effectiveness of our approach, which achieves 15.45%/57.92% bit savings in terms of PSNR/MS-SSIM compared with the high-efficiency video coding low-delay P configuration.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"517-528"},"PeriodicalIF":3.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906771","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243816","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 : 2025-02-25DOI: 10.1109/TBC.2025.3541887
Qian Huang;Xiaoyin Yi;Fei Qi;Lei Liu;Qingming Xie;Qin Jiang;Chunxia Hu
This paper addresses the challenges of wireless resource allocation for 5G Ultra-reliable Low-latency Communication (URLLC) broadcast/multicast services in Vehicle-to-Everything (V2X) scenarios. It proposes three key algorithms: an iterative resource allocation approach that decomposes optimization into power and spectrum subproblems, a federated learning-based multicast resource allocation scheme that protects data privacy while enabling distributed training, and a cooperative multi-agent reinforcement learning solution that treats vehicles as intelligent nodes to jointly optimize system throughput, URLLC delivery rate, and multicast performance. Path loss models, mobility patterns, and interference scenarios are analyzed for both unicast and multicast transmissions. Simulation results demonstrate that the proposed algorithms achieve superior performance in terms of throughput, reliability, and latency compared to traditional and baseline approaches.
{"title":"Enhancing 5G V2X URLLC Broadcast/Multicast Services With FL-Based Wireless Resource Allocation","authors":"Qian Huang;Xiaoyin Yi;Fei Qi;Lei Liu;Qingming Xie;Qin Jiang;Chunxia Hu","doi":"10.1109/TBC.2025.3541887","DOIUrl":"https://doi.org/10.1109/TBC.2025.3541887","url":null,"abstract":"This paper addresses the challenges of wireless resource allocation for 5G Ultra-reliable Low-latency Communication (URLLC) broadcast/multicast services in Vehicle-to-Everything (V2X) scenarios. It proposes three key algorithms: an iterative resource allocation approach that decomposes optimization into power and spectrum subproblems, a federated learning-based multicast resource allocation scheme that protects data privacy while enabling distributed training, and a cooperative multi-agent reinforcement learning solution that treats vehicles as intelligent nodes to jointly optimize system throughput, URLLC delivery rate, and multicast performance. Path loss models, mobility patterns, and interference scenarios are analyzed for both unicast and multicast transmissions. Simulation results demonstrate that the proposed algorithms achieve superior performance in terms of throughput, reliability, and latency compared to traditional and baseline approaches.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"384-396"},"PeriodicalIF":3.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243868","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}
In the 6G network, integrating broadcasting and mobile networks will significantly improve the transmission capability. Considering the excellent error-correction performance, polarized-adjusted convolutional (PAC) codes are promising for ensuring reliable data transmission in 6G broadcasting services. However, the inherent high decoding latency of PAC codes poses challenges for seamless switching between broadcasting and mobile services. In this paper, we propose a simplified fast list (SFL) PAC decoder, which jointly exploits the node thresholds and adaptive path-pruning technology to reduce the decoding latency while maintaining high reliability. Firstly, we present a novel path expansion rule based on the node thresholds to avoid unnecessary computations. Secondly, the introduction of the adaptive path-pruning technology efficiently reduces the number of sorting operations. Moreover, we implement the proposed decoder on general purpose processors (GPPs) by software. Simulation results show that the proposed SFL decoding algorithm significantly reduces the decoding latency by up to 75.18% compared to the state-of-the-art (SOTA) work with no noticeable degradation in error-correction performance. Software implementation of the proposed decoder achieves an 18.80% improvement in throughput performance over the SOTA PAC software decoder.
{"title":"Simplified Fast List PAC Decoder for Broadcasting Services in 6G: Algorithm and Implementation","authors":"Jingxin Dai;Hang Yin;Yansong Lv;Yuhuan Wang;Yin Xu;Rui Lv","doi":"10.1109/TBC.2025.3534624","DOIUrl":"https://doi.org/10.1109/TBC.2025.3534624","url":null,"abstract":"In the 6G network, integrating broadcasting and mobile networks will significantly improve the transmission capability. Considering the excellent error-correction performance, polarized-adjusted convolutional (PAC) codes are promising for ensuring reliable data transmission in 6G broadcasting services. However, the inherent high decoding latency of PAC codes poses challenges for seamless switching between broadcasting and mobile services. In this paper, we propose a simplified fast list (SFL) PAC decoder, which jointly exploits the node thresholds and adaptive path-pruning technology to reduce the decoding latency while maintaining high reliability. Firstly, we present a novel path expansion rule based on the node thresholds to avoid unnecessary computations. Secondly, the introduction of the adaptive path-pruning technology efficiently reduces the number of sorting operations. Moreover, we implement the proposed decoder on general purpose processors (GPPs) by software. Simulation results show that the proposed SFL decoding algorithm significantly reduces the decoding latency by up to 75.18% compared to the state-of-the-art (SOTA) work with no noticeable degradation in error-correction performance. Software implementation of the proposed decoder achieves an 18.80% improvement in throughput performance over the SOTA PAC software decoder.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"397-410"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243867","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}
Inter-Tower Communication Network (ITCN), which supports communication between different base stations via wireless links, has excellent potential for simultaneous transmission of broadcast data and personalized data using Layered Division Multiplexing (LDM). In this paper, a heterogeneous ITCN architecture using LDM and wireless backhauling is proposed. In uplink transmission, the power-limited user devices transmit data to the secondary transmitters (STs), and the STs relay the data to the master transmitter (MT). In downlink transmission, the MT and multiple STs cooperatively transmit broadcast data using single frequency network (SFN) mode, and the STs relay the personalized data from the MT to users simultaneously. Considering the co-channel interference, this paper proposes a joint subchannel assignment and power allocation scheme for both uplink and downlink transmission. A mixed integer optimization problem is formulated, and an alternating optimization algorithm (AO) based on game theory and convex optimization is proposed. Simulation results are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.
{"title":"A Heterogeneous Network Transmission Architecture Based on NOMA for Next-Generation Converged Communications and Broadcasting Systems","authors":"Xiaowu Ou;Haoyang Li;Yin Xu;Dazhi He;Wenjun Zhang;Yiyan Wu","doi":"10.1109/TBC.2025.3534620","DOIUrl":"https://doi.org/10.1109/TBC.2025.3534620","url":null,"abstract":"Inter-Tower Communication Network (ITCN), which supports communication between different base stations via wireless links, has excellent potential for simultaneous transmission of broadcast data and personalized data using Layered Division Multiplexing (LDM). In this paper, a heterogeneous ITCN architecture using LDM and wireless backhauling is proposed. In uplink transmission, the power-limited user devices transmit data to the secondary transmitters (STs), and the STs relay the data to the master transmitter (MT). In downlink transmission, the MT and multiple STs cooperatively transmit broadcast data using single frequency network (SFN) mode, and the STs relay the personalized data from the MT to users simultaneously. Considering the co-channel interference, this paper proposes a joint subchannel assignment and power allocation scheme for both uplink and downlink transmission. A mixed integer optimization problem is formulated, and an alternating optimization algorithm (AO) based on game theory and convex optimization is proposed. Simulation results are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"372-383"},"PeriodicalIF":3.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243677","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：