Open AccessLow‐complexity near‐optimal signal detection for uplink large‐scale MIMO systems
Author(s) -
Gao Xinyu,
Dai Linglong,
Ma Yongkui,
Wang Zhaocheng
Publication year - 2014
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.2014.0713
Subject(s) - minimum mean square error , mimo , telecommunications link , algorithm , computational complexity theory , inversion (geology) , detection theory , computer science , mathematics , matrix (chemical analysis) , convergence (economics) , mathematical optimization , telecommunications , statistics , detector , materials science , structural basin , estimator , composite material , biology , economics , economic growth , channel (broadcasting) , paleontology
The minimum mean square error (MMSE) signal detection algorithm is near‐optimal for uplink multi‐user large‐scale multiple‐input–multiple‐output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large‐scale MIMO is symmetric positive definite, based on which a low‐complexity near‐optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from ( K 3 ) to ( K 2 ), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near‐optimal performance of the classical MMSE algorithm.