Open AccessFully connected layer‐shared network architecture for massive MIMO CSI feedback
Author(s) -
Zhang Boyuan,
Li Haozhen,
Liang Xin,
Gu Xinyu,
Zhang Lin
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12418
Subject(s) - mimo , computer science , duplex (building) , architecture , channel state information , user equipment , network architecture , layer (electronics) , division (mathematics) , state (computer science) , channel (broadcasting) , real time computing , computer architecture , computer network , electronic engineering , base station , telecommunications , engineering , wireless , algorithm , dna , art , chemistry , arithmetic , mathematics , organic chemistry , biology , genetics , visual arts
In frequency division duplex (FDD) massive multiple‐input multiple‐output (MIMO) system, deep learning‐based channel state information (CSI) feedback approach improves CSI reconstruction accuracy. However, different models need to be trained and stored for different scenarios, which causes huge memory cost, especially at the user equipment (UE) side. In this letter, a fully connected layer‐shared (FCS) feedback architecture is proposed to simplify the feedback network at the UE. As a case study, the FCS architecture is applied on CsiNet and CRNet in the indoor and outdoor scenarios. Experimental results indicate that the network using the FCS architecture is able to achieve comprehensive performance with impressive storage saving.