Open AccessMatching pursuit‐based singular vectors estimation for large MIMO beamforming
Author(s) -
Wang Mengyao,
Cheng Xiantao,
Zhu Xiaodong
Publication year - 2015
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/el.2014.3197
Subject(s) - beamforming , singular value decomposition , mimo , precoding , singular value , computer science , channel (broadcasting) , algorithm , matching pursuit , overhead (engineering) , control theory (sociology) , transmission (telecommunications) , matrix (chemical analysis) , mathematics , telecommunications , eigenvalues and eigenvectors , artificial intelligence , compressed sensing , physics , materials science , control (management) , quantum mechanics , composite material , operating system
In multiple‐input–multiple‐output (MIMO) systems, beamforming can maximise the signal‐to‐noise ratio by using the principal right and left singular vectors of the channel matrix as transmit and receive beam vectors, respectively. A matching pursuit (MP)‐based singular vectors estimation (SVE) scheme is proposed, referred to as MP‐SVE, for beamforming transmission and detection in large MIMO systems. By judiciously exploiting the specific properties of large MIMO channel matrix, the proposed MP‐SVE is able to determine the optimal beamforming vector pair while circumventing the burdensome channel estimation and singular value decomposition. The simulations verify that with the same training overhead, the proposed MP‐SVE remarkably outperforms the state‐of‐the‐art counterparts.