Hybrid Unicast/Multicast Massive MIMO Precoding for 5G Mixed Mode

IF 3.2 1区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Broadcasting Pub Date : 2024-07-09 DOI:10.1109/TBC.2024.3405313
Fei Qi;Lei Liu;Weiliang Xie
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Abstract

This paper studies the realization of wireless video transmission by leveraging 5G mixed mode with multimedia broadcast multicast services (MBMS). In particular, it investigates a number of key elements, such as physical layer modeling and precoding strategies, for MBMS implementation with large-scale multi-input multi-output (MIMO). A novel hybrid 5G mixed mode system is proposed to seamlessly integrate unicast and multicast transmissions, wherein system architecture, user grouping strategies, interference mitigation techniques, and optimized multicast beamforming approach are comprehensively elucidated. The performance of our proposed system is assessed through comprehensive simulations and analysis. The results indicate significant improvements in coding and spectral efficiencies while combining MIMO with layer division multiplexing (LDM).
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面向 5G 混合模式的混合单播/多播大规模 MIMO 精确编码
本文研究了利用 5G 混合模式与多媒体广播组播服务(MBMS)实现无线视频传输的问题。特别是,它研究了一些关键要素,如物理层建模和预编码策略,以实现大规模多输入多输出(MIMO)的 MBMS。本文提出了一种新型混合 5G 混合模式系统,以无缝集成单播和组播传输,并全面阐释了系统架构、用户分组策略、干扰缓解技术和优化的组播波束成形方法。我们通过全面的模拟和分析评估了所提系统的性能。结果表明,在将多输入多输出(MIMO)与层分复用(LDM)相结合的同时,编码效率和频谱效率都得到了显著提高。
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来源期刊
IEEE Transactions on Broadcasting
IEEE Transactions on Broadcasting 工程技术-电信学
CiteScore
9.40
自引率
31.10%
发文量
79
审稿时长
6-12 weeks
期刊介绍: The Society’s Field of Interest is “Devices, equipment, techniques and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.” In addition to this formal FOI statement, which is used to provide guidance to the Publications Committee in the selection of content, the AdCom has further resolved that “broadcast systems includes all aspects of transmission, propagation, and reception.”
期刊最新文献
Table of Contents 2024 Scott Helt Memorial Award for the Best Paper Published in the IEEE Transactions on Broadcasting IEEE Transactions on Broadcasting Publication Information IEEE Transactions on Broadcasting Information for Authors Enhancing Channel Estimation in Terrestrial Broadcast Communications Using Machine Learning
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