{"title":"在基于大规模多输入多输出的 5G 蜂窝网络中选择发射天线以实现能效","authors":"Janmoni Borah, Smriti Baruah, Ganjiguntla Bhargavi, Palagiri Durgaprasad, Boya Damodar","doi":"10.3103/s0735272723020048","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this study, two dynamic algorithms are verified for transmit antenna selection aimed at achieving the energy efficiency in massive MIMO, namely: Random User Selection (RUS) and Maximum Channel Gain (MCG). Massive MIMO setups are investigated over <i>N</i> radio cables via centralized massive base station (BS), which uses RUS and MCG to provide management to <i>U</i> clients. Massive MIMO networks are shown for 8×8, 16×16, 25×25, and 50×50 setups using RUS and MCG. The proficiency of the proposed scheme on setup network has been investigated with RUS and MCG, and then the effect on performance characteristics, such as sumrate, user throughput and energy efficiency has been analyzed. The power efficiency has also been improved by increasing the number of Massive MIMO antennas. Compared to RUS, algorithm MCG provides a better service to users located far from the base station antenna.</p>","PeriodicalId":52470,"journal":{"name":"Radioelectronics and Communications Systems","volume":"202 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transmit Antenna Selection for Achieving Energy Efficiency in Massive MIMO Based 5G Cellular Network\",\"authors\":\"Janmoni Borah, Smriti Baruah, Ganjiguntla Bhargavi, Palagiri Durgaprasad, Boya Damodar\",\"doi\":\"10.3103/s0735272723020048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Abstract</h3><p>In this study, two dynamic algorithms are verified for transmit antenna selection aimed at achieving the energy efficiency in massive MIMO, namely: Random User Selection (RUS) and Maximum Channel Gain (MCG). Massive MIMO setups are investigated over <i>N</i> radio cables via centralized massive base station (BS), which uses RUS and MCG to provide management to <i>U</i> clients. Massive MIMO networks are shown for 8×8, 16×16, 25×25, and 50×50 setups using RUS and MCG. The proficiency of the proposed scheme on setup network has been investigated with RUS and MCG, and then the effect on performance characteristics, such as sumrate, user throughput and energy efficiency has been analyzed. The power efficiency has also been improved by increasing the number of Massive MIMO antennas. Compared to RUS, algorithm MCG provides a better service to users located far from the base station antenna.</p>\",\"PeriodicalId\":52470,\"journal\":{\"name\":\"Radioelectronics and Communications Systems\",\"volume\":\"202 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radioelectronics and Communications Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3103/s0735272723020048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radioelectronics and Communications Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s0735272723020048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
摘要
摘要 本研究验证了两种旨在实现大规模多输入多输出（MIMO）能效的发射天线选择动态算法，即随机用户选择（RUS）和最大信道增益（MCG）：随机用户选择 (RUS) 和最大信道增益 (MCG)。研究通过集中式大规模基站（BS）在 N 条无线电电缆上进行大规模 MIMO 设置，该基站使用 RUS 和 MCG 为 U 客户端提供管理。演示了使用 RUS 和 MCG 的 8×8、16×16、25×25 和 50×50 设置的大规模 MIMO 网络。利用 RUS 和 MCG 研究了所提方案在设置网络上的熟练程度，然后分析了对总和率、用户吞吐量和能效等性能特征的影响。通过增加 Massive MIMO 天线的数量，功率效率也得到了提高。与 RUS 相比，MCG 算法能为远离基站天线的用户提供更好的服务。
Transmit Antenna Selection for Achieving Energy Efficiency in Massive MIMO Based 5G Cellular Network
Abstract
In this study, two dynamic algorithms are verified for transmit antenna selection aimed at achieving the energy efficiency in massive MIMO, namely: Random User Selection (RUS) and Maximum Channel Gain (MCG). Massive MIMO setups are investigated over N radio cables via centralized massive base station (BS), which uses RUS and MCG to provide management to U clients. Massive MIMO networks are shown for 8×8, 16×16, 25×25, and 50×50 setups using RUS and MCG. The proficiency of the proposed scheme on setup network has been investigated with RUS and MCG, and then the effect on performance characteristics, such as sumrate, user throughput and energy efficiency has been analyzed. The power efficiency has also been improved by increasing the number of Massive MIMO antennas. Compared to RUS, algorithm MCG provides a better service to users located far from the base station antenna.
期刊介绍：
Radioelectronics and Communications Systems covers urgent theoretical problems of radio-engineering; results of research efforts, leading experience, which determines directions and development of scientific research in radio engineering and radio electronics; publishes materials of scientific conferences and meetings; information on scientific work in higher educational institutions; newsreel and bibliographic materials. Journal publishes articles in the following sections:Antenna-feeding and microwave devices;Vacuum and gas-discharge devices;Solid-state electronics and integral circuit engineering;Optical radar, communication and information processing systems;Use of computers for research and design of radio-electronic devices and systems;Quantum electronic devices;Design of radio-electronic devices;Radar and radio navigation;Radio engineering devices and systems;Radio engineering theory;Medical radioelectronics.
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