https://scholars.lib.ntu.edu.tw/handle/123456789/632606
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Tsai, Wen Chiao | en_US |
dc.contributor.author | Chen, Chi Wei | en_US |
dc.contributor.author | Teng, Chieh Fang | en_US |
dc.contributor.author | AN-YEU(ANDY) WU | en_US |
dc.date.accessioned | 2023-06-12T06:32:03Z | - |
dc.date.available | 2023-06-12T06:32:03Z | - |
dc.date.issued | 2022-08-01 | - |
dc.identifier.issn | 10897798 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/632606 | - |
dc.description.abstract | Intelligent reflecting surface (IRS) can enhance the wireless communication environment by smartly reflecting the incident signal toward desired directions. However, the acquisition of channel state information (CSI) is challenging since IRS usually consists of a massive number of passive elements that have no capabilities of sensing and processing the pilot signals. Although by exploiting the sparsity of the angular domain channel, the huge pilot overhead can be reduced with conventional compressive sensing algorithms, such as approximate message passing (AMP), these algorithms cannot achieve satisfactory channel estimation performance. Based on the learned AMP (LAMP) network, we propose a hypernetwork-assisted LAMP (HN-LAMP) network with dynamic shrinkage parameters to improve the channel estimation accuracy. Furthermore, a recurrent architecture is adopted to reduce the large memory overhead arising from the LAMP network. Simulation results show that the proposed HN-LAMP network can improve the channel estimation accuracy or reduce the computational complexity under satisfactory estimation performance. Moreover, the proposed hypernetwork-assisted recurrent LAMP (HNR-LAMP) architecture can effectively reduce 50% memory overhead by sharing learnable weights. | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.relation.ispartof | IEEE Communications Letters | en_US |
dc.subject | channel estimation | compressive sensing | deep learning | hypernetwork | Intelligent reflecting surface (IRS) | en_US |
dc.title | Low-Complexity Compressive Channel Estimation for IRS-Aided mmWave Systems With Hypernetwork-Assisted LAMP Network | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1109/LCOMM.2022.3178188 | - |
dc.identifier.scopus | 2-s2.0-85136953631 | - |
dc.identifier.isi | WOS:000841808700040 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85136953631 | - |
dc.relation.pages | 1883 | en_US |
dc.relation.journalvolume | 26 | en_US |
dc.relation.journalissue | 8 | en_US |
dc.relation.pageend | 1887 | en_US |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Electronics Engineering | - |
crisitem.author.dept | Intel-NTU Connected Context Computing Center | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0003-4731-8633 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: International Research Centers | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。