z-logo
open-access-imgOpen Access
Sparse K ‐best detector for generalised space shift keying in large‐scale multiple‐input–multiple‐output systems
Author(s) -
Peng Xiaoqing,
Wu Weimin,
Sun Jun,
Liu Yingzhuang
Publication year - 2015
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2014.0906
Subject(s) - detector , transmitter , matching pursuit , algorithm , computer science , decoding methods , keying , compressed sensing , signal (programming language) , mathematics , telecommunications , channel (broadcasting) , programming language
In this study, the authors propose a low complexity detector for the generalised space shift keying (GSSK) in large‐scale multiple‐input–multiple‐output systems. To be concrete, they propose a sparse K ‐best (SK) detector based on the breadth‐first category of sphere detector (referred to K ‐best sphere decoding). The author's detector is inspired by the fact that the GSSK signal is naturally a sparse zero‐one vector since only a few antennas are activated at the transmitter. Different with the conventional K ‐best detector searching all the transmit antennas, their proposed SK detector investigates only a few promising candidates which are activated antennas at the transmitter. Overall, their proposed SK detector exploits not only the sparsity of the GSSK signal but also the constraint on its non‐zero values. Therefore the restricted isometry property‐based performance analysis shows that is effective in detecting the GSSK signal. Moreover, the empirical results show that their detector performs much better than the sparse algorithms‐based normalised compressive sensing (NCS) detectors while exhibits only slightly higher complexity than the latter (the low‐complexity orthogonal matching pursuit‐based NCS detector).

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here