Multi-User Semantic Communications System with Spectrum Scarcity

Romano Fantacci;Benedetta Picano
{"title":"Multi-User Semantic Communications System with Spectrum Scarcity","authors":"Romano Fantacci;Benedetta Picano","doi":"10.23919/JCIN.2022.10005215","DOIUrl":null,"url":null,"abstract":"Nowadays, the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems. In particular, focusing on spectrum scarcity, expected to afflict the upcoming sixth generation (6G) networks, this paper analyses the semantic communications behavior in the context of a cell-dense scenario, in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability. In such a context, artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm. As a consequence, a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework. Finally, extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"7 4","pages":"375-382"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communications and Information Networks","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10005215/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Nowadays, the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems. In particular, focusing on spectrum scarcity, expected to afflict the upcoming sixth generation (6G) networks, this paper analyses the semantic communications behavior in the context of a cell-dense scenario, in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability. In such a context, artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm. As a consequence, a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework. Finally, extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.
频谱稀缺的多用户语义通信系统
当前,语义通信范式的出现为提高新一代通信系统的传输可靠性和效率提供了一个诱人的机会。本文特别针对即将到来的第六代(6G)网络所面临的频谱稀缺性问题,分析了蜂窝密集场景下的语义通信行为,在这种场景下,属于不同小型基站区域的用户可能被分配在同一信道上,从而产生不可忽略的干扰,严重影响通信可靠性。在这种情况下,为了加速从传统通信到新的语义通信范式的转换,人工智能方法至关重要。因此,基于深度卷积神经网络的编码器-解码器架构在本文所提出的语义通信框架的定义中得到了利用。最后,进行了大量的数值模拟,以测试所提出的框架在不同干扰场景下的优势,并与不同的传统或语义替代方案进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信