{"title":"利用反向散射自共轭元表面实现无赠予随机存取","authors":"Davide Dardari;Marina Lotti;Nicolò Decarli;Gianni Pasolini","doi":"10.1109/TCCN.2024.3449648","DOIUrl":null,"url":null,"abstract":"Recently, grant-free random access (GFRA) schemes have received significant attention by the research community as a solution for extremely low-latency and short packet transmissions in new industrial Internet-of-Things and digital twins applications. However, implementing such schemes in the mmWave and THz frequency bands is challenging due to the need for multiple-input multiple-output (MIMO) links to counteract the high path loss and provide sufficient spatial filtering. This results in unacceptable signaling overhead for channel estimation, slow beam alignment procedures between the access point (AP) and the sensors, as well as high sensor complexity and energy consumption. In this paper, we propose the adoption of a self-conjugating metasurface (SCM) at the sensor side, where the signal sent by the AP is backscattered after being conjugated and phase-modulated according to the data to be transmitted by the sensor. We introduce a novel SCM-based GFRA protocol enabling the detection of new sensors and the establishment of parallel MIMO uplink communications with extremely low latency. This is achieved in a blind manner, eliminating the need for radiofrequency chains and digital processing at the sensor side, as well as explicit channel estimation and time-consuming beam alignment schemes.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"10 5","pages":"1620-1634"},"PeriodicalIF":7.4000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670115","citationCount":"0","resultStr":"{\"title\":\"Grant-Free Random Access With Backscattering Self-Conjugating Metasurfaces\",\"authors\":\"Davide Dardari;Marina Lotti;Nicolò Decarli;Gianni Pasolini\",\"doi\":\"10.1109/TCCN.2024.3449648\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, grant-free random access (GFRA) schemes have received significant attention by the research community as a solution for extremely low-latency and short packet transmissions in new industrial Internet-of-Things and digital twins applications. However, implementing such schemes in the mmWave and THz frequency bands is challenging due to the need for multiple-input multiple-output (MIMO) links to counteract the high path loss and provide sufficient spatial filtering. This results in unacceptable signaling overhead for channel estimation, slow beam alignment procedures between the access point (AP) and the sensors, as well as high sensor complexity and energy consumption. In this paper, we propose the adoption of a self-conjugating metasurface (SCM) at the sensor side, where the signal sent by the AP is backscattered after being conjugated and phase-modulated according to the data to be transmitted by the sensor. We introduce a novel SCM-based GFRA protocol enabling the detection of new sensors and the establishment of parallel MIMO uplink communications with extremely low latency. This is achieved in a blind manner, eliminating the need for radiofrequency chains and digital processing at the sensor side, as well as explicit channel estimation and time-consuming beam alignment schemes.\",\"PeriodicalId\":13069,\"journal\":{\"name\":\"IEEE Transactions on Cognitive Communications and Networking\",\"volume\":\"10 5\",\"pages\":\"1620-1634\"},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670115\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cognitive Communications and Networking\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10670115/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cognitive Communications and Networking","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10670115/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Grant-Free Random Access With Backscattering Self-Conjugating Metasurfaces
Recently, grant-free random access (GFRA) schemes have received significant attention by the research community as a solution for extremely low-latency and short packet transmissions in new industrial Internet-of-Things and digital twins applications. However, implementing such schemes in the mmWave and THz frequency bands is challenging due to the need for multiple-input multiple-output (MIMO) links to counteract the high path loss and provide sufficient spatial filtering. This results in unacceptable signaling overhead for channel estimation, slow beam alignment procedures between the access point (AP) and the sensors, as well as high sensor complexity and energy consumption. In this paper, we propose the adoption of a self-conjugating metasurface (SCM) at the sensor side, where the signal sent by the AP is backscattered after being conjugated and phase-modulated according to the data to be transmitted by the sensor. We introduce a novel SCM-based GFRA protocol enabling the detection of new sensors and the establishment of parallel MIMO uplink communications with extremely low latency. This is achieved in a blind manner, eliminating the need for radiofrequency chains and digital processing at the sensor side, as well as explicit channel estimation and time-consuming beam alignment schemes.
期刊介绍:
The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.