IEEE Transactions on Broadcasting最新文献_第4页

A Low-Sampling-Rate Digital Predistortion Method Based on Inverse Filter Signal Recovery for Wideband Power Amplifiers 基于反滤波信号恢复的宽带功率放大器低采样率数字预失真方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-25 DOI: 10.1109/TBC.2025.3549995

Xiaofang Wu;Jiawen Yan;Dehuang Zhang;Jianyang Zhou

{"title":"A Low-Sampling-Rate Digital Predistortion Method Based on Inverse Filter Signal Recovery for Wideband Power Amplifiers","authors":"Xiaofang Wu;Jiawen Yan;Dehuang Zhang;Jianyang Zhou","doi":"10.1109/TBC.2025.3549995","DOIUrl":"https://doi.org/10.1109/TBC.2025.3549995","url":null,"abstract":"To address the high cost associated with using high-speed and large-acquisition-bandwidth analog-to-digital-converters (ADCs) in the feedback path, a new low-sampling-rate digital predistortion (DPD) method is proposed in this paper. To model the analog bandpass filter (BPF) in the feedback path, a training method for digital finite impulse response (FIR) filter coefficients in a practical band-limited DPD system is proposed, and a filter matrix is constructed in different forms in the case of continuous signal and cyclic signal inputs. The filter matrix provides an extra degree of band-limited power amplifier (PA) model accuracy and robustness. Then, an inverse filter signal recovery (IFSR) method is proposed to recover the full-band output signal of the PA, which can be used to train the predistorter using conventional DPD techniques. Simulation results validates the effectiveness of the IFSR method, demonstrating that the IFSR-DPD method can reduce the ADC sampling rate to 1/10 or less compared to full-rate sampling methods, and decrease the ADC acquisition bandwidth to about 0.3 times that of the original input signal bandwidth. The linearization performance of the IFSR-DPD method is also evaluated on an instrument-based test platform. When the passband and transition band characteristics of the BPF are unsatisfactory, the proposed low-sampling rate DPD method improves the adjacent channel power ratio (ACPR) by 18.67 dB and the error vector magnitude (EVM) by 1.214%, compared to the scenario without DPD.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"653-665"},"PeriodicalIF":3.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameter Estimation for Adaptive Impulsive Noise Suppression: A Deep Learning-Based Memoryless Nonlinearity Approach 自适应脉冲噪声抑制参数估计：基于深度学习的无记忆非线性方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-24 DOI: 10.1109/TBC.2025.3550016

Zhu Xiao;Yiqiu Zhang;Tong Li;Jing Bai;Siwang Zhou;Yonghu Zhang

{"title":"Parameter Estimation for Adaptive Impulsive Noise Suppression: A Deep Learning-Based Memoryless Nonlinearity Approach","authors":"Zhu Xiao;Yiqiu Zhang;Tong Li;Jing Bai;Siwang Zhou;Yonghu Zhang","doi":"10.1109/TBC.2025.3550016","DOIUrl":"https://doi.org/10.1109/TBC.2025.3550016","url":null,"abstract":"In the OFDM-based digital terrestrial broadcasting systems, impulsive noise is a significant factor affecting communication quality. A prominent method to suppress impulsive noise is to incorporate a memoryless nonlinearity at the receiver front-end of the OFDM demodulator, in which parameter estimation of memoryless nonlinearity directly impact the effectiveness of impulsive noise suppression. In this paper, we proposes a deep learning-based memoryless nonlinearity approach for impulsive noise suppression. The proposed method can adaptively estimate the parameters of the memoryless nonlinearity in dynamic impulsive noise environments and achieve totically-optimal parameter estimation. To specific, we design a High-Amplitude Priority Downsampling method to extract the key amplitude characteristics from the input signal, which effectively resolves the issue of extracting amplitude features of impulsive noise. Besides, to address the issue of performance degradation due to insufficient training samples, we propose a novel training method that integrates progressive fine-tuning to complete the training only using few samples. Furthermore, we conduct experiments on signal-to-noise ratio (SNR) and bit error rate (BER) of the signal after impulsive noise suppression. The results validate that the parameters estimated by the proposed method can approximate the theoretical optimal values and the proposed method can effectively suppress impulsive noise and outperform the traditional methods in terms of SNR and BER.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"641-652"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Terahertz Hybrid Precoding With Low-Resolution PSs Under Frequency Selective Channel: A Partial Decoupling Method 频率选择信道下低分辨率ps的太赫兹混合预编码：一种部分解耦方法

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-21 DOI: 10.1109/TBC.2025.3550020

Yang Wang;Chuang Yang;Mugen Peng

{"title":"Terahertz Hybrid Precoding With Low-Resolution PSs Under Frequency Selective Channel: A Partial Decoupling Method","authors":"Yang Wang;Chuang Yang;Mugen Peng","doi":"10.1109/TBC.2025.3550020","DOIUrl":"https://doi.org/10.1109/TBC.2025.3550020","url":null,"abstract":"Terahertz (THz) communication is considered as one of the most critical technologies for 6G broadcasting communications because of its abundant bandwidth. To compensate for the high propagation of THz, analog/digital hybrid precoding for THz massive multiple input multiple output (MIMO) is proposed to focus signals and extend the broadcasting communication range. Notably, considering hardware cost and power consumption, infinite and high-resolution phase shifters (PSs) are difficult to implement in THz massive MIMO, and low-resolution PSs are typically adopted in practice. However, low-resolution PSs cause severe performance degradation, which also poses challenges for the design of analog precoders for multi-carrier systems. Moreover, THz communication with broadband suffers severe frequency selective fading, further increasing the analog precoder design difficulty. Motivated by the above factors, in this paper, we propose a new heuristic algorithm under a fully connected (FC) structure and partially-connected (PC) architecture, which firstly decouples partially the digital precoder and the analog precoder and then optimizes alternately. To further improve the performance, we extend our partial decoupling method to dynamic subarrays in which each RF chain is connected to an antenna that does not duplicate. The numerical results demonstrate that our proposed THz hybrid precoding with low-resolution PSs achieves better performance to the comparisons for both FC structure and PC structure.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 2","pages":"453-466"},"PeriodicalIF":3.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Latitude-Aware and Importance-Activated Transform Coding for Learned Omnidirectional Image Compression 面向学习全向图像压缩的自适应纬度感知和重要激活变换编码

IF 4.8 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-15 DOI: 10.1109/TBC.2025.3565895

Hui Hu;Yunhui Shi;Jin Wang;Nam Ling;Baocai Yin

{"title":"Adaptive Latitude-Aware and Importance-Activated Transform Coding for Learned Omnidirectional Image Compression","authors":"Hui Hu;Yunhui Shi;Jin Wang;Nam Ling;Baocai Yin","doi":"10.1109/TBC.2025.3565895","DOIUrl":"https://doi.org/10.1109/TBC.2025.3565895","url":null,"abstract":"Based on the measured latitude and longitude, users can freely view different perspectives of the omnidirectional image. Typically, omnidirectional images are represented in the equirectangular projection (ERP) format. Although ERP images suffer from distortion and redundancy due to oversampling, making traditional codec inefficient, they maintain visual consistency and enhance compatibility with deep learning-based image processing tools. This has led to the emergence of end-to-end omnidirectional image compression methods based on the ERP format. In fact, transform coding, a key component in learned planar image compression, has not yet been fully explored in the domain of learned omnidirectional image compression. In this paper, we propose a transform coding method with adaptive latitude-aware and importance-activated features for omnidirectional image compression. Specifically, the adaptive latitude-aware mechanism comprises two modules. The first module, termed Adaptive Latitude-aware Module (ALAM), employs rectangular dilated convolutional kernels of multiple sizes to perceive distortion redundancy across different latitudes, followed by latitude-adaptive weighting to select optimal features for respective latitudes. The second module, named Multi-scale Convolutional Gated Feedforward Network (MCGFN), fully exploits local contextual information while suppressing feature redundancy induced by diverse dilated convolutions in the first module. Furthermore, to further reduce ERP redundancy, we design an importance-activated spatial feature transform module that regulates latent representations to allocate more bits to significant regions. Experimental results demonstrate that our proposed method outperforms existing VVC standards and learning-based omnidirectional image compression approaches at medium-to-high bitrates while maintaining low computational complexity.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 3","pages":"874-888"},"PeriodicalIF":4.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Energy Replenishment Station Site Selection and Path Planning for Drone Video Streaming 无人机视频流能量补给站选址与路径规划

IF 4.8 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-12 DOI: 10.1109/TBC.2025.3553307

Jian Xiong;Junqi Wu;You Zhou;Shiqing Xu

{"title":"On Energy Replenishment Station Site Selection and Path Planning for Drone Video Streaming","authors":"Jian Xiong;Junqi Wu;You Zhou;Shiqing Xu","doi":"10.1109/TBC.2025.3553307","DOIUrl":"https://doi.org/10.1109/TBC.2025.3553307","url":null,"abstract":"In recent years, with the advancement of autonomous aerial vehicles (AAV) technologies, small AAVs have been utilized for borderline patrol, especially for real-time video transmission without interruption. However, these small AAVs face limitations in conducting long-endurance and long-distance missions solely relying on their initial onboard resources. To address this issue, this paper introduces a novel combined AAV air resupply system based on energy cycle resupply. In this system, a ground energy resupply station dispatches a replenishing AAV (AAV-R) to dock with it along the border and transmit energy to the task AAV (AAV-T), when its energy resources are depleted, ensuring continuous energy supply. To tackle the challenge of siting the energy recharge station, we propose a greedy siting algorithm utilizing Monte Carlo methods and an algorithm based on ant colony and clustering. Simulations demonstrate that the number of energy recharge stations can be reduced to 47.6% - 52.9% compared to the AAV-T autonomous return recharge scheme. Additionally, we present a Q Learning-based energy cycle resupply algorithm for AAV-R path planning, offering practical applications in real-world borderline patrol scenarios.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 3","pages":"862-873"},"PeriodicalIF":4.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Deep Reinforcement Learning (DRL) to Optimize Quality in 360-Degree Video Tile Management 使用深度强化学习（DRL）优化360度视频贴图管理的质量

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-11 DOI: 10.1109/TBC.2025.3541860

Chunguang Li;Dayoung Lee;Minseok Song

{"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}

引用次数: 0

FTBM: A Fault-Tolerant BIER Multicast for MBMS in 5G/B5G Dynamic Edge Networks FTBM: 5G/B5G动态边缘网络中MBMS的容错BIER组播

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-07 DOI: 10.1109/TBC.2025.3541889

Honglin Fang;Peng Yu;Xinxiu Liu;Ying Wang;Wenjing Li;Xuesong Qiu;Zhaowei Qu

{"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}

引用次数: 0

IEEE Transactions on Broadcasting Publication Information IEEE广播出版信息汇刊

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-05 DOI: 10.1109/TBC.2025.3542624

引用次数: 0

IEEE Transactions on Broadcasting Information for Authors IEEE作者广播信息汇刊

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-05 DOI: 10.1109/TBC.2025.3542626

引用次数: 0

Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-Relevance Degradation Description 一次训练全精：基于域相关退化描述的无参考点云质量评估

IF 3.2 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2025-03-04 DOI: 10.1109/TBC.2025.3541862

Yipeng Liu;Qi Yang;Yujie Zhang;Yiling Xu;Le Yang;Xiaozhong Xu;Shan Liu

{"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}

引用次数: 0