TY - GEN
T1 - Low-complexity channel tracking in fast-varying MIMO environments
AU - Wang, Ruoxu
AU - Peng, Wei
AU - Jiang, Tao
AU - Adachi, Fumiyuki
N1 - Funding Information:
This work is supported in part by National Science Foundation of China with grant numbers 61771214, 61771216 and 61531011, National Science Foundation for Distinguished Young Scholars of China with grant number 61325004, the Innovative Project of Shenzhen city in China with grant number JCYJ20170307171931096, the open research fund of National Mobile Communications Research Laboratory, Southeast University with grant number 2018D10, and the Fundamental Research Funds for the Central Universities with Grant number 2015ZDTD012.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - In this paper, channel tracking in fast-varying MIMO environments is considered. Firstly, the high-dimension channel vector of each user is decomposed into a semi-static factor load matrix (FLM) and a low-dimension factor coefficient vector (FCV). Thereby, channel tracking is replaced by the infrequent FLM tracking and low-complexity FCV tracking. Secondly, channel variations are represented by local polynomial modeling, based on which a recursive least square (RLS) algorithm with optimal forgetting factor (FF) is proposed. It is verified that the proposed algorithm has higher accuracy, better tracking ability and lower computational complexity than the existing methods.
AB - In this paper, channel tracking in fast-varying MIMO environments is considered. Firstly, the high-dimension channel vector of each user is decomposed into a semi-static factor load matrix (FLM) and a low-dimension factor coefficient vector (FCV). Thereby, channel tracking is replaced by the infrequent FLM tracking and low-complexity FCV tracking. Secondly, channel variations are represented by local polynomial modeling, based on which a recursive least square (RLS) algorithm with optimal forgetting factor (FF) is proposed. It is verified that the proposed algorithm has higher accuracy, better tracking ability and lower computational complexity than the existing methods.
KW - Channel tracking
KW - Fast-varying
KW - Low-complexity
KW - MIMO
KW - Optimal forgetting factor
KW - Recursive least square
UR - http://www.scopus.com/inward/record.url?scp=85073907927&partnerID=8YFLogxK
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U2 - 10.1109/IWCMC.2019.8766471
DO - 10.1109/IWCMC.2019.8766471
M3 - Conference contribution
AN - SCOPUS:85073907927
T3 - 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
SP - 1339
EP - 1343
BT - 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019
Y2 - 24 June 2019 through 28 June 2019
ER -