A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

  Subjects -> ELECTRONICS (Total: 207 journals)
The end of the list has been reached or no journals were found for your choice.
   Most Followed Journals

   Top Publishers

   Top Subjects
Similar Journals
Journal Cover
IEEE Transactions on Broadcasting
Journal Prestige (SJR): 0.941
Citation Impact (citeScore): 5
Number of Followers: 12  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0018-9316
Published by IEEE Homepage  [228 journals]
  • IEEE Transactions on Broadcasting

    • Free pre-print version: Loading...

      Pages: C2 - C2
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • IEEE Transactions on Broadcasting information for authors

    • Free pre-print version: Loading...

      Pages: C3 - C4
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • NHK Meta Studio: A Compact Volumetric TV Studio for 3-D Reconstruction

    • Free pre-print version: Loading...

      Authors: Hirofumi Morioka;Toshie Misu;Taichi Suginoshita;Hideki Mitsumine;
      Pages: 2 - 9
      Abstract: Many studies have been conducted on the 3D reconstruction of subjects using multiple fixed cameras. Accepting the trade-off between the number of cameras and reconstruction quality, our studio is designed to capture high-quality models of one or two subjects for TV program use. Several cameras are mounted on a hemispherical dome with the stage in the center and a cloth cover on the frame for chroma-keying. The optimal camera numbers and placements for reconstruction were determined by simulation, and the 3D reconstruction was performed as a point cloud by a combination of visual hull and stereo matching. The quality was still not high enough, however, so we also added a surface light field to the point cloud to obtain the weighted average of rays from camera images close to the viewpoint. In the final stage, the images were then combined to the video, and errors generated during the reconstruction were compensated for by use of a deep neural network (DNN) for video translation. An offline processing studio has been built as a preliminary step towards real-time processing, and the reconstructed 3D images have been evaluated subjectively for a number of subjects. These studies confirm the effectiveness of this studio design.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video
           Emotion Recognition Inference

    • Free pre-print version: Loading...

      Authors: Qinglan Wei;Xuling Huang;Yuan Zhang;
      Pages: 10 - 20
      Abstract: In the latest social networks, more and more people prefer to express their emotions in videos through text, speech, and rich facial expressions. Multimodal video emotion analysis techniques can help understand users’ inner world automatically based on human expressions and gestures in images, tones in voices, and recognized natural language. However, in the existing research, the acoustic modality has long been in a marginal position as compared to visual and textual modalities. That is, it tends to be more difficult to improve the contribution of the acoustic modality for the whole multimodal emotion recognition task. Besides, although better performance can be obtained by introducing common deep learning methods, the complex structures of these training models always result in low inference efficiency, especially when exposed to high-resolution and long-length videos. Moreover, the lack of a fully end-to-end multimodal video emotion recognition system hinders its application. In this paper, we designed a fully multimodal video-to-emotion system (named FV2ES) for fast yet effective recognition inference, whose benefits are threefold: (1) The adoption of the hierarchical attention method upon the sound spectra breaks through the limited contribution of the acoustic modality, and outperforms the existing models’ performance on both IEMOCAP and CMU-MOSEI datasets; (2) the introduction of the idea of multi-scale for visual extraction while single-branch for inference brings higher efficiency and maintains the prediction accuracy at the same time; (3) the further integration of data pre-processing into the aligned multimodal learning model allows the significant reduction of computational costs and storage space.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Fast Rate-Distortion Optimization for Depth Maps in 3-D Video Coding

    • Free pre-print version: Loading...

      Authors: Junyan Huo;Xile Zhou;Hui Yuan;Shuai Wan;Fuzheng Yang;
      Pages: 21 - 32
      Abstract: To ensure the fidelity of virtual views, rate-distortion optimization (RDO) criterion for the 3D extension of the High Efficiency Video Coding (3D-HEVC) is well designed, in which the synthesized view distortion (SVD) is introduced to derive the rate-distortion (RD) cost. To obtain accurate SVDs, the rendering operation is employed which demands a fairly high computational complexity. To address this problem, a fast RDO method for depth maps is proposed, which checks the RD cost during its calculation process. Specifically, given a coding mode, the RD cost is composed of several cumulative items. If the accumulated RD cost is equal to or exceeds the minimum RD cost of previously coded modes, it will not be necessary to continue the RD cost calculation for the mode. To reduce the encoding complexity, existing methods usually aim at reducing the number of tested modes or block partitions. To the best of our knowledge, it is the first time that the latent redundant complexity in the RD cost calculation is investigated and removed. Experimental results demonstrate that, compared with the 3D-HEVC reference software, the proposed method can save 28.1% of depth coding time with a small coding gain (0.04% BD-rate saving). An additional test is designed to evaluate four typical fast coding methods with/without the proposed method. Extensive results verify that the proposed method can be seamlessly combined with the state-of-the-art methods.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Precise Encoding Complexity Control for Versatile Video Coding

    • Free pre-print version: Loading...

      Authors: Yan Huang;Jun Xu;Chen Zhu;Li Song;Wenjun Zhang;
      Pages: 33 - 48
      Abstract: Complexity reduction is a commonly used method to deal with complicated video coding standards, such as High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC). But the unstable performance under different video contents and Quantization Parameters (QPs) makes it difficult to precisely specify the target encoding time of every single sequence, which limits the practical use of the encoder. Inspired by rate control, in this paper, encoding time budget is regarded as a resource. We incorporate the hierarchical Group of Picture (GOP) encoding structure, the non-accelerated proportion, and block content variation within the frame, and design a top-down allocation and bottom-up feedback scheme, to achieve precise control of encoding complexity by controlling the maximum depth of QuadTree with nested Multi-type Tree (QTMT). In the scheme, Temporal ID (Tid)-based, Sum of Absolute Transformed Difference (SATD)-based methods and Linear (L) Model are designed to facilitate weighted allocation and feedback. The relationship between Planar Cost and encoding time is exploited as a Time-Cost (T-C) model, which guides the selection of Largest Coding Unit (LCU) encoding strategies in I-frames. For B-frames, we investigate a switching suppression assisted status-based method, to efficiently decide the encoding strategy of each LCU. Through the collaboration of the proposed technologies in the scheme, given any encoding time target achievable, encoding strategies will be automatically switched to help the actual encoding time gradually approach the target. To the best of our knowledge, this work is the first work on VVC-based encoding complexity control. The proposed scheme supports directly specifying the target encoding time or target Frame Per Second (FPS), and accurately realizing it within one pass. According to experimental results, under the target encoding time ratio of 80%, 60% and 40%, the average encodi-g time error is kept under 0.24%, 0.03% and 0.02% by our method, outperforming state-of-the-art works base on HEVC. These results prove the advantage of the proposed scheme.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • PreSR: Neural-Enhanced Adaptive Streaming of VBR-Encoded Videos With
           Selective Prefetching

    • Free pre-print version: Loading...

      Authors: Gangqiang Zhou;Zhenxiao Luo;Miao Hu;Di Wu;
      Pages: 49 - 61
      Abstract: Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Prediction-Oriented Disparity Rectification Model for Geometry-Based Light
           Field Compression

    • Free pre-print version: Loading...

      Authors: Xinpeng Huang;Yilei Chen;Ping An;Liquan Shen;
      Pages: 62 - 74
      Abstract: The geometry-based light field compression approach aims to use the inherent disparity cue provided by the light field to reduce the redundancy of data and improve immersive media broadcasting. Some methods, including our previous one, transmit explicit disparity maps along with sparsely selected views and then exploit disparity-based view prediction to remove correlations in the angular domain. However, these methods neglect two important issues: the 4D correspondence between the disparity array and view array and the practical non-uniform disparity distribution in captured light fields. As a result, the prediction potentiality of disparity maps is heavily restricted, and the limited disparity-based prediction accuracy degrades the final compression performance. In this paper, we focus on these two issues of estimated disparity maps and propose a prediction-oriented disparity rectification (PoDR) model. First, based on our previous disparity estimation, we propose to utilize the 4D structural prior of light fields to refine the estimated disparity map. The 4D correspondence between a disparity array and a view array is enhanced, leading to higher prediction accuracy and lower bit costs. Second, for the refined disparity array, we propose a variable stride-based determination algorithm to obtain the practical non-uniform disparity distribution in captured light fields. Specifically, the angular distance between each pair of disparity maps is efficiently derived by the approximate solution based on the gradient variation of the prediction errors. By combining these two modules, the proposed PoDR model improves the overall compression performance compared with our previous work. Furthermore, we verify that the proposed method can obtain better light field fundamental capability (e.g., refocusing) than state-of-the-art methods.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Variable-Bit-Rate Video Frame-Size Prediction by the Extended Kalman
           Filter Using Levenberg–Marquardt Algorithm

    • Free pre-print version: Loading...

      Authors: Shih Yu Chang;Hsiao-Chun Wu;Kun Yan;
      Pages: 75 - 84
      Abstract: It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in practice. In this work, we propose a new expectation-maximization (EM) based extended Kalman filter (EKF) to predict the bit-rates, where the EKF state-transition models will be optimized by the Levenberg–Marquardt algorithm (LMA). The main advantages of our proposed novel EKF-based bit-rate prediction approach are given as follows. First, our proposed EKF-based predictor can optimally estimate the bit-rates in the presence of uncertainty and/or noise. Second, our proposed novel EKF-based bit-rate prediction approach does not require a separate classifier to determine the individual frame-types as the conventional approach so our approach would be more robust than the conventional approach. Numerical evaluation of bit-rate (frame-size) prediction is also conducted over three movies encoded by the MPEG-4 standard. Compared to the existing Kalman-filter based bit-rate prediction methods, our proposed new LMA-EKF predictor can achieve much better performance in terms of the normalized mean square error (NMSE) and the inverse of signal-to-noise-ratio (SNR).
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Live360: Viewport-Aware Transmission Optimization in Live 360-Degree Video
           Streaming

    • Free pre-print version: Loading...

      Authors: Jinyu Chen;Zhenxiao Luo;Zelong Wang;Miao Hu;Di Wu;
      Pages: 85 - 96
      Abstract: Live 360-degree video streaming is emerging as the next disruptive innovation in the coming era. However, compared to conventional live video streaming, it is much more challenging to provide high-quality live 360-degree video streaming due to high bandwidth demand, stringent latency requirement, and diverse user viewports. In this paper, we propose a viewport-aware live 360-degree video streaming framework called Live360 to optimize end-to-end video stream transmission. To make the problem tractable, we decompose the transmission optimization problem into two sub-problems, and optimize upstream and downstream transmission in an asynchronous way. We prioritize 360-degree cameras according to users’ real-time viewing interests and upload attractive contents with a higher bitrate. We also re-define the metric of QoE (Quality-of-Experience) for live 360-degree video viewers, and solve the optimization problems efficiently using dynamic programming. Extensive experiments show that Live360 is lightweight and significantly outperforms other state-of-the-art methods in terms of various QoE metrics. The average QoE of Live360 is twice that of other baseline methods.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Why No Reference Metrics for Image and Video Quality Lack Accuracy and
           Reproducibility

    • Free pre-print version: Loading...

      Authors: Margaret H. Pinson;
      Pages: 97 - 117
      Abstract: This article provides a comprehensive overview of no reference (NR) metrics for image quality analysis (IQA) and video quality analysis (VQA). We examine 26 independent evaluations of NR metrics (previously published) and analyze 32 NR metrics on six IQA datasets and six VQA datasets (new results). Where NR metric developers claim Pearson correlation values between 0.66 and 0.99, our measurements range from 0.0 to 0.63. None of the NR metrics we analyzed are accurate enough to be deployed by industry. Performance evaluations that indicate otherwise are based on insufficient data and highly inaccurate. We will examine development strategies, tools, datasets, root cause analysis, and our baseline metric for collaboration, Sawatch.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Self-Supervised Representation Learning for Video Quality Assessment

    • Free pre-print version: Loading...

      Authors: Shaojie Jiang;Qingbing Sang;Zongyao Hu;Lixiong Liu;
      Pages: 118 - 129
      Abstract: No-reference (NR) video quality assessment (VQA) is a challenging problem due to the difficulty in model training caused by insufficient annotation samples. Previous work commonly utilizes transfer learning to directly migrate pre-trained models on the image database, which suffers from domain inadaptation. Recently, self-supervised representation learning has become a hot spot for the independence of large-scale labeled data. However, existing self-supervised representation learning method only considers the distortion types and contents of the video, there needs to investigate the intrinsic properties of videos for the VQA task. To amend this, here we propose a novel multi-task self-supervised representation learning framework to pre-train a video quality assessment model. Specifically, we consider the effects of distortion degrees, distortion types, and frame rates on the perceived quality of videos, and utilize them as guidance to generate self-supervised samples and labels. Then, we optimize the ability of the VQA model in capturing spatio-temporal differences between the original video and the distorted version using three pretext tasks. The resulting framework not only eases the requirements for the quality of the original video but also benefits from the self-supervised labels as well as the Siamese network. In addition, we propose a Transformer-based VQA model, where short-term spatio-temporal dependencies of videos are modeled by 3D-CNN and 2D-CNN, and then the long-term spatio-temporal dependencies are modeled by Transformer because of its excellent long-term modeling capability. We evaluated the proposed method on four public video quality assessment databases and found that it is competitive with all compared VQA algorithms.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Spatiotemporal Feature Hierarchy-Based Blind Prediction of Natural Video
           Quality via Transfer Learning

    • Free pre-print version: Loading...

      Authors: Weizhi Xian;Mingliang Zhou;Bin Fang;Xingran Liao;Cheng Ji;Tao Xiang;Weijia Jia;
      Pages: 130 - 143
      Abstract: In this paper, we propose a pyramidal spatiotemporal feature hierarchy (PSFH)-based no-reference (NR) video quality assessment (VQA) method using transfer learning. First, we generate simulated videos by a generative adversarial network (GAN)-based image restoration model. The residual maps between the distorted frames and simulated frames, which can capture rich information, are utilized as one input of the quality regression network. Second, we use 3D convolution operations to construct a PSFH network with five stages. The spatiotemporal features incorporating the shared features transferred from the pretrained image restoration model are fused stage by stage. Third, with the guidance of the transferred knowledge, each stage generates multiple feature mapping layers that encode different semantics and degradation information using 3D convolution layers and gated recurrent units (GRUs). Finally, five approximate perceptual quality scores and a precise prediction score are obtained by fully connected (FC) networks. The whole model is trained under a finely designed loss function that combines pseudo-Huber loss and Pearson linear correlation coefficient (PLCC) loss to improve the robustness and prediction accuracy. According to the extensive experiments, outstanding results can be obtained compared with other state-of-the-art methods. Both the source code and models are available online.1
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Attentional Feature Fusion for End-to-End Blind Image Quality Assessment

    • Free pre-print version: Loading...

      Authors: Mingliang Zhou;Shujun Lang;Taiping Zhang;Xingran Liao;Zhaowei Shang;Tao Xiang;Bin Fang;
      Pages: 144 - 152
      Abstract: In this paper, an end-to-end blind image quality assessment (BIQA) model based on feature fusion with an attention mechanism is proposed. We extracted the multilayer features of the image and fused them based on the attention mechanism; the fused features are then mapped into score, and the image quality assessment without reference is realized. First, because the human visual perception system hierarchically approaches the input information from local to global, we used three different neural networks to extract physically meaningful image features, and we use modified VGG19 and modified VGG16 to extract the substrate texture information and the local information of the edges, respectively. Meanwhile, we use the resNet50 to extract high-level global semantic information. Second, to take full advantage of multilevel features and avoid monotonic addition in hierarchical feature fusion, we adopt an attention-based feature fusion mechanism that combines the global and local contexts of the features and assigns different weights to the features to be fused, so that the model can perceive richer types of distortion. Experimental findings on six standard databases show that our approach yields improved performance.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • OVQE: Omniscient Network for Compressed Video Quality Enhancement

    • Free pre-print version: Loading...

      Authors: Liuhan Peng;Askar Hamdulla;Mao Ye;Shuai Li;Zengbin Wang;Xue Li;
      Pages: 153 - 164
      Abstract: How to use information from temporal, spatial, and frequency domain dimensions is crucial for the quality enhancement of compressed video. The state-of-the-art methods generally design powerful networks to fuse the spatiotemporal information of the videos. But the spatiotemporal information of the entire video is not fully utilized and effectively fused, resulting in the learned context information that is not closely related to the target frame. In addition, various compressed videos have varying degrees of frequency domain information loss. The previous methods ignored the non-uniform distortion of compressed video in different frequency domains and did not design unique algorithms for different frequency domains, so the real texture details of the video could not be restored. In this paper, we propose an omniscient network, which learns video spatiotemporal and omni-frequency information more effectively. The omniscient network consists of two novel components: a Spatio-Temporal Feature Fusion (STFF) module and an Omni-Frequency Adaptive Enhancement (OFAE) block. The former aims to capture spatiotemporal information in adjacent frames, while the latter aims to adaptively recover different frequency domains of compressed video. The information is designed to be bidirectionally propagated in a grid manner such that the omni-enhanced results can be applied. Extensive experiments show that our method outperforms the state-of-the-art method in terms of objective metrics, subjective visual effects, and model complexity.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Speeding Up Subjective Video Quality Assessment via Hybrid Active Learning

    • Free pre-print version: Loading...

      Authors: Xiaochen Liu;Wei Song;Qi He;Mario Di Mauro;Antonio Liotta;
      Pages: 165 - 178
      Abstract: Subjective video quality assessment (VQA) is the most reliable way to get accurate quality scores, providing first-hand data on the research of quality of experience (QoE). However, traditional subjective VQA has the disadvantage of being cumbersome and time-consuming. In this paper, we propose an efficient subjective VQA framework based on hybrid information and active learning, named HA-SVQA. Built on the principle of active learning for data annotation, HA-SVQA allows iterative assessments on the most valuable or informative videos, which are selected based on hybrid information from the subject’s prior decisions and the objective quality predictions. By eliminating the redundant (or less valuable) videos to be assessed, HA-SVQA can speed up the process of subjective VQA. Concretely, our framework starts with a few quality-known videos to initialize dual-regression models. It then uses a scoring-difference stratified sampling strategy to iteratively select the next group of videos to be assessed, which contain high quality uncertainty. The newly scored videos are used to continually update every part of our framework. By this way, subjective VQA can be stopped early while meeting the acceptable goal of a full-time subjective study. We conducted simulation experiments on three different datasets: LIVE, LIVE VQC, and an underwater video quality dataset. The results show that HA-SVQA can effectively speed up the process of subjective VQA and reduce about 1/3 of the human workload or time cost in the presence of data redundancy. In order to investigate the effectiveness of HA-SVQA in more depth, we conducted a field experiment with deep-sea videos. We found that HA-SVQA is still effective in reducing about 1/3 of the overall human workload, which is consistent with the conclusion of simulation experiments. Finally, we discussed some of the factors that potentially affect the QoE modelling and the subjective VQA.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • A QoE Model for Mulsemedia TV in a Smart Home Environment

    • Free pre-print version: Loading...

      Authors: Lana Jalal;Roberto Puddu;Maria Martini;Vlad Popescu;Maurizio Murroni;
      Pages: 179 - 190
      Abstract: The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to ad-hoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user’s Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • ViViD: View Prediction of Online Video Through Deep Neural Network-Based
           Analysis of Subjective Video Attributes

    • Free pre-print version: Loading...

      Authors: Saikat Sarkar;Spandan Basu;Angshuman Paul;Dipti Prasad Mukherjee;
      Pages: 191 - 200
      Abstract: Popularity of a video in an online platform may be defined by its number of views. The total view count of a video may change throughout its presence in an online platform. However, in most cases the view count tends to saturate after a certain time. We propose a method to predict the total view of a video at saturation. We have modeled the task of finding the view count of a video at saturation as a joint classification and regression problem, which is solved via a deep neural network. The network has a classification and a regression head. The classification head decides the view band among a set of available bands, whereas the regression head outputs a tolerance view count within each band. We consider four video attributes as the input to the network, namely, the thumbnail associated with the video, the title, the audio and the video itself for the view prediction task. The attributes are fused in a hierarchical fashion in the deep neural network. We propose a custom mismatch loss function and a penalty loss function for the joint training of the classification and regression heads of the network. Experimental results show that our method is 6.47% better in view prediction than the competitive methods.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR
           Systems

    • Free pre-print version: Loading...

      Authors: Nadezhda Chukhno;Olga Chukhno;Dmitri Moltchanov;Anna Gaydamaka;Andrey Samuylov;Antonella Molinaro;Yevgeni Koucheryavy;Antonio Iera;Giuseppe Araniti;
      Pages: 201 - 214
      Abstract: Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G) New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast beams to serve multicast users.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • A Multi-Antenna Pilot Predistortion Scheme and Channel Estimation
           Algorithm for LTE-Based Terrestrial Broadcast System

    • Free pre-print version: Loading...

      Authors: Haoyang Li;Ning Yang;Yihang Huang;Dazhi He;Hanjiang Hong;Yin Xu;Yunfeng Guan;
      Pages: 215 - 223
      Abstract: Multi-Antenna technology is a physical layer technique widely used in mobile communication and terrestrial broadcast systems. For fully utilizing the antenna diversity, conventional Multi-Antenna pilots usually meet the characteristic of orthogonality in terms of the pilot arrangement to avoid inter-pilot interference between different transmitting antennas. However, the scheme with orthogonality leads to the doubling of the pilot overhead, which seriously reduces the transmission efficiency. The latest LTE-based terrestrial broadcast system, Further evolved Multimedia Broadcast Multicast Service (FeMBMS), is probably to adopt the Multi-Antenna technique in the future, but no corresponding pilot scheme has been provided. This paper proposes a novel pilot pre-distortion scheme and Channel Estimation (CHE) algorithm for FeMBMS. The pre-distortion scheme does not require the pilot on different antennas to meet the restriction of orthogonality, so to address the problem of pilot overhead doubling. Meanwhile, it deals with the issue of inter-pilot interference by converting the interference into the white noise spreading over the whole OFDM symbol. Furthermore, the proposed CHE algorithm effectively eliminates the whitened interference by utilizing the processing of DFT-based windowing and Iterative Interference Cancellation (IIC). Simulation shows that the pre-distortion scheme and CHE algorithm could provide a CHE performance with accuracy same or close to that of the orthogonality scheme while significantly improving the transmission efficiency.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • RIS-Aided LDM System: A New Prototype in Broadcasting System

    • Free pre-print version: Loading...

      Authors: Yizhe Zhang;Wen He;Dazhi He;Yin Xu;Yunfeng Guan;Wenjun Zhang;
      Pages: 224 - 235
      Abstract: Layer-division multiplexing (LDM) system has developed with significant improvement in spectrum efficiency and service diversity. LDM technology, that superposes multiple signals with different transmit powers, has been adopted by ATSC 3.0 to deliver robust high-definition services. However, due to the high mobility of devices, the wide signal coverage, and the uneven geographical channel condition, there exists under-utilized energy and significant potential of enhancing communication capacity in the LDM system. In this paper, we employ reconfigurable intelligent surface (RIS) to improve the reception quality in the LDM system, especially the SINR performance of the terminals in the edge area. We formulate a problem of maximizing the minimum SINR among all the terminals by jointly optimizing the power control and pairing mechanism, which is a Mixed-integer Nonlinear Programming (MINLP) problem. To solve it, we use the convex-concave procedure (CCCP). We further explore the high-mobility scenario with the limited channel state information (CSI) and the missing knowledge of detailed prediction of terminals’ mobility. We use Brownian Motion to model the possible variation of the terminals’ positions in the high mobility scenario to guarantee the successful reception probability. The analysis shows the RIS can bring obvious SINR gain to the LDM system, and the mobility modification could greatly help improve the successful reception probability.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Performance Analysis and Power Allocation for Spatial Modulation-Aided
           MIMO-LDM With Finite Alphabet Inputs

    • Free pre-print version: Loading...

      Authors: Shunlei Zhang;Kewu Peng;Jian Song;
      Pages: 236 - 245
      Abstract: In this paper, we study a spatial modulation (SM) aided multiple-input multiple-output (MIMO) layered division multiplexing (LDM) system for broadcast/multicast service delivery in future broadcast/multicast systems. Comprehensive performance analysis and the injection level (IL) optimization are investigated for the SM-aided MIMO-LDM system over correlated Rayleigh fading channels. First, the symbol detection pairwise error probability (PEP) and the average symbol error rate (SER) union bound under joint maximum-likelihood (ML) detection are derived. Second, the spectral efficiency (SE) of the SM-aided MIMO-LDM system with finite alphabet inputs is analyzed. Since the theoretical SE lacks closed-form expression and involves prohibitive computational complexity, we then derive the closed-form lower bounds and tight approximations for the theoretical SE, in which the computational complexity is relieved by several orders of magnitude compared to direct calculation of the theoretical SE. Third, based on the SE analysis, a weighted sum (WS) SE maximization problem with quality of service (QoS) constraints is formulated to optimize IL for the SM-aided MIMO-LDM system. We first demonstrate that WS SE is a unimodal function of IL, and then develop an efficient golden section search (GSS) based algorithm. Simulation results are provided to validate the theoretical analysis. It is shown that our derived SER union bound well matches the simulated SER in the high SNR region. The tightness of the derived closed-form lower bounds and approximations for theoretical SE and the effectiveness of the developed IL optimization algorithm are also verified by simulation results.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Priority-Aware Resource Allocation for 5G mmWave Multicast Broadcast
           Services

    • Free pre-print version: Loading...

      Authors: Pan-Yang Su;Kuang-Hsun Lin;Yi-Yun Li;Hung-Yu Wei;
      Pages: 246 - 263
      Abstract: 5G Multicast Broadcast Services (MBS) are viewed as a promising 5G New Radio (NR) application, as standardization begins in 3GPP Release 17. With MBS, one next generation Node B (gNB) delivers data to multiple user equipments (UE) simultaneously, thus improving spectrum efficiency. Millimeter wave (mmWave) beamforming further enhances system performance by focusing signals in a dedicated direction. However, despite the advantages, we identify three issues of multicast with beamforming techniques. First, link directionality causes the gNB to transmit data over the beams sequentially, resulting in a combinatorial resource allocation problem. Confronting this beam scheduling issue, we develop an optimization algorithm that obtains an optimal solution in polynomial time. Second, UEs may falsely report their valuations over beam resources to gain more utility. Under this scenario, the gNB cannot allocate the resources to those in need due to the lack of accurate UE information. Therefore, we propose a Vickrey–Clarke–Groves (VCG) auction-based mechanism to incentivize the UEs to reveal their valuations over resources truthfully. This mechanism guarantees solution efficiency and maximizes social welfare. Third, as 3GPP standards allow for different priorities for different multicast flows, and video content providers distinguish between ordinary and premium UEs, we take UE priority into account. In this regard, we extend the valuation-based mechanism to a multi-priority one. Finally, the mathematical analysis validates some desirable properties of the proposed scheme, such as incentive-compatibility. Simulation results also justify our superior performance in the 5G MBS system compared with other resource allocation schemes.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • 3-D Hybrid Beamforming for Terahertz Broadband Communication System With
           Beam Squint

    • Free pre-print version: Loading...

      Authors: Yezeng Wu;Guochao Song;Hui Liu;Lixia Xiao;Tao Jiang;
      Pages: 264 - 275
      Abstract: In this paper, the three-dimensioned (3D) hybrid beamforming is designed for the terahertz (THz) based broadband broadcasting communication system with beam squint. Specifically, the full-dimensional THz massive multiple input multiple output (M-MIMO) channel model and the array gain of the uniform planar array (UPA) are first analyzed in the context of the beam squint effect. Then, a 3D hybrid beamforming architecture is proposed by leveraging two-tier true time delay (TTD), which is able to combat the beam squint effect from the horizontal and vertical directions. To further reduce hardware cost and power consumption, a low-cost 3D hybrid beamforming architecture with a two-tier TTD and phase shifter combination (TPC) is designed. Simulation results are shown that the performance of the proposed 3D hybrid beamforming architectures is capable of approaching that of the full-digital beamforming counterpart in the face of beam squint, despite relying on reduced implementation cost.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Joint Visible Light Sensing and Communication Using m-CAP
           Modulation

    • Free pre-print version: Loading...

      Authors: Lina Shi;Bastien Béchadergue;Luc Chassagne;Hongyu Guan;
      Pages: 276 - 288
      Abstract: As 5G devices and networks continue to roll out, new broadcasting services and capabilities have been introduced to the entire ecosystem, opening up additional new applications and granular business opportunities, where indoor joint communication and sensing are critical. Under this trend, in this paper, we propose a new system using multi-band carrierless amplitude and phase $(m$ -CAP) modulation associated with received signal strength (RSS)-based trilateration to achieve visible light sensing and communication from the same signal. The architecture of this system is first detailed, with an emphasis on how the light source limitations in terms of dynamic range and modulation bandwidth may be taken into account. The proposed set-up is then shown through simulations to provide an illuminance between 300 and 500 lux over the whole room, positioning with an error lower than 7.17 cm in 90% of the cases, and a continuous data connectivity at 32 Mbps. The influence of several parameters, including that of the main $m$ -CAP settings, on this performance is studied in order to define some general rules for the design of such a system.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Visible Light Positioning With Lens Compensation for Non-Lambertian
           Emission

    • Free pre-print version: Loading...

      Authors: Ben Meunier;John Cosmas;Kareem Ali;Nawar Jawad;Geoffrey Eappen;Xun Zhang;Hongxiu Zhao;Wei Li;Hequn Zhang;
      Pages: 289 - 302
      Abstract: With greater demands for cost-effective, reliable, and highly accurate positioning, indoor wireless localisation using Visible Light Positioning (VLP) is a promising solution for future networks. One can expect VLP solutions to appear in all environments, from homes to industry; however, the existing literature primarily considers Visible Light Communication (VLC) sources with purely Lambertian emission patterns. To facilitate greater versatility within VLP solutions, this paper considers non-Lambertian sources. It evaluates practical Received Signal Strength Indicator (RSSI) data obtained during the Internet of Radio Light (IoRL) 5G Measurement Campaign conducted in a home environment using non-Lambertian Total Internal Reflection (TIR) lenses, which produce a halo lighting effect. The initial analysis explores the calibration of Lambertian source parameters against datasheet values leading to reductions in the average Positioning Error (PE) of 17% and 3% for averaged and individual RSSI measurement sets, respectively. While this highlights improvements from correct calibration, the Lambertian model proved to be unsuitable for non-Lambertian sources. In the absence of any existing non-Lambertian models, the authors proposed the Halo Lens Compensation (HLC) method to calibrate the considered non-Lambertian TIR sources correctly. The HLC further reduced PE in the calibrated results by 50% and 39%, with mean PE of 3.1 cm and 4.6 cm for averaged and individual RSSI measurement sets, respectively. In conclusion, for VLP using non-Lambertian sources, the existing Lambertian model is unsuitable. However, the proposed HLC is highly effective and achieves positioning accuracy comparable to existing literature using Lambertian sources.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Channel-Aided Transmission Parameter Signalling Detection for DTMB-A

    • Free pre-print version: Loading...

      Authors: Yunchuan Huang;Chao Zhang;Changyong Pan;
      Pages: 303 - 312
      Abstract: For reliable synchronization and transmission parameter signalling (TPS) detection, the preamble based on distance detection (PBDD) is adopted in the digital terrestrial television multimedia broadcasting - advanced (DTMB-A) standard. However, the conventional signalling detection methods do not take the channel information into consideration and will undergo severe performance loss over frequency-selective channels, especially those with strong and long delay echos. In this paper, a novel two-stage TPS detection method is proposed based on the preamble for DTMB-A system, which utilizes the channel information to enhance the detection accuracy. After coarse distance evaluation, the equalization will be performed on the possible candidate subcarriers to reconstruct the OFDM signalling block, hence a more reliable correlation statistic can be obtained to achieve more accurate signalling detection. As demonstrated in the simulations, the detection accuracy can be improved remarkably by compensating the impact of the transmission channel compared with the traditional detection schemes, while the increment of the computational complexity is acceptable.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Optimization of Partial Transmit Sequences for PAPR Reduction of OFDM
           Signals Without Side Information

    • Free pre-print version: Loading...

      Authors: The Khai Nguyen;Ha H. Nguyen;J. Eric Salt;Colin Howlett;
      Pages: 313 - 321
      Abstract: This paper develops a novel optimization of partial transmit sequences (PTS) with phase quantization to reduce the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) signals and enable data detection without side information. Since the formulated optimization problem is non-convex and has exponential time complexity, we employ a convex relaxation method to convert the original optimization problem into a series of convex programming whose solutions converge to a sub-optimal point satisfying the Karush-Kuhn-Tucker (KKT) conditions in polynomial time. Moreover, the obtained phase factors are quantized to enable the maximum likelihood (ML) phase estimation at the receiver, hence removing the need of sending side information. Analytical and numerical results are provided to show that our proposed PTS design achieves better PAPR reduction over existing PTS methods, while no performance degradation is incurred in data detection when the number of phase factors is properly chosen.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
  • Out-of-Band Digital Predistortion for Power Amplifiers With Strong
           Nonlinearity

    • Free pre-print version: Loading...

      Authors: Ying Liu;Zixuan Long;Mengyao Zhang;Xiaozheng Wei;Xiangjie Xia;Shihai Shao;Youxi Tang;
      Pages: 322 - 337
      Abstract: Conventional full-band digital predistortion (DPD) suppresses the inband (IB) distortion to improve the error vector magnitude (EVM) performance and simultaneously suppresses the out-of-band (OOB) distortion to reduce the adjacent-channel interference. However, its performance is very limited when the nonlinearity is strong, especially in terms of suppressing the OOB distortion. This paper proposes an OOB DPD scheme for strong nonlinearity with a special focus on suppressing the OOB distortion to reduce the adjacent-channel interference. The proposed OOB DPD is featured by specifying an arbitrary OOB-distortion band for linearization and controlling the improvement level of EVM performance. Experiments demonstrate that the proposed OOB DPD can achieve superior performance in suppressing the OOB distortion when the nonlinearity is strong. We also present analyses to show that in strong nonlinearity cases with high compression, focusing on suppressing the OOB distortion at a specific band is an effective way to improve the suppression level of the target distortion. The proposed OOB DPD can further improve the transmission power under the assurance of low adjacent-channel interference. It can be applied in communications with urgent needs in high transmission power, high efficiency, and low adjacent-channel interference, such as military communications that usually adopt low-order modulations with low EVM requirements.
      PubDate: March 2023
      Issue No: Vol. 69, No. 1 (2023)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.237.32.15
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-