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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 4539470, 6 pages
Research Article

Normalized Structured Compressed Sensing Based Signal Detection in Spatial Modulation 3D-MIMO Systems

College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Correspondence should be addressed to Guan Gui

Received 15 September 2017; Revised 5 November 2017; Accepted 15 November 2017; Published 6 December 2017

Academic Editor: Nan Zhao

Copyright © 2017 Wei Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Signal detection is one of the fundamental problems in three-dimensional multiple-input multiple-output (3D-MIMO) wireless communication systems. This paper addresses a signal detection problem in 3D-MIMO system, in which spatial modulation (SM) transmission scheme is considered due to its advantages of low complexity and high-energy efficiency. SM based signal transmission typically results in the block-sparse structure in received signals. Hence, structured compressed sensing (SCS) based signal detection is proposed to exploit the inherent block sparsity information in the received signal for the uplink (UL). Moreover, normalization preprocessing is considered before iteration process with the purpose of preventing the noise from being overamplified by the column vector with inadequately large elements. Simulation results are provided to show the stable and reliable performance of the proposed algorithm under both Gaussian and non-Gaussian noise, in comparison with methods such as compressed sensing based detectors, minimum mean square error (MMSE), and zero forcing (ZF).