Open AccessStructured compressive sensing based superimposed pilot design in downlink large‐scale MIMO systems
Author(s) -
Gao Zhen,
Dai Linglong,
Wang Zhaocheng
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.0985
Subject(s) - mimo , telecommunications link , compressed sensing , computer science , channel state information , overhead (engineering) , multi user mimo , channel (broadcasting) , 3g mimo , wireless , precoding , electronic engineering , computer network , telecommunications , engineering , algorithm , operating system
Large‐scale multiple‐input multiple‐output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large‐scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, a spectrum‐efficient superimposed pilot design for downlink large‐scale MIMO scenarios is proposed, where frequency‐domain pilots of different transmit antennas occupy completely the same subcarriers in the frequency domain. Meanwhile, spatial–temporal common sparsity of large‐scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realised by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.