ZHOU Enzhi, HAO Li. On the Detection of Multiple-Access Spatial Modulations[J]. Chinese Journal of Electronics, 2017, 26(1): 172-178. doi: 10.1049/cje.2016.08.018
 Citation: ZHOU Enzhi, HAO Li. On the Detection of Multiple-Access Spatial Modulations[J]. Chinese Journal of Electronics, 2017, 26(1): 172-178.

# On the Detection of Multiple-Access Spatial Modulations

##### doi: 10.1049/cje.2016.08.018
Funds:  This work is supported by the National Basic Research Program of China (973 Program) (No.2012CB316100), and the National Natural Science Foundation of China (NSFC) (No.61271245).
• Corresponding author: HAO Li (corresponding author) received the Ph.D. degree in communication and information systems from Southwest Jiaotong University, China. She is now a professor in the School of Information Science and Technology. Her research interests include MIMO transmissions and high mobility wireless communications. (Email:lhao@home.swjtu.edu.cn)
• Rev Recd Date: 2015-01-26
• Publish Date: 2017-01-10
• The detection problem for the Multiple-access Spatial modulations (M-SM) is investigated in this paper, where multiple transmitters adopting spatial modulation communicate to the receiver at the same time. The optimal Maximum-likelihood (ML) detection suffers from the high computational complexity, while Sphere decoding (SD) can not reduce the complexity effectively because of the multiple active antennas in M-SM. In order to avoid the high complexity, a Space-alternating generalized expectation-maximization (SAGE) algorithm aided List-projection (S-LP) detector is proposed and applied to M-SM systems. The received signal vector is firstly projected onto the subspaces spanned by the columns of channel matrices corresponding to the possible active antennas. Then some combinations of antenna indices with the largest projections are selected as candidate index sets, based on which, a modified SAGE algorithm is applied to update the candidate symbols. Both analysis and simulation results show that the proposed S-LP detector achieves a near-optimum performance with a significantly reduced complexity compared with ML and SD detection.
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