Enhanced Frequency-Domain Signal Detection for Single-Carrier Spatial Modulation Systems

Yinman Lee, Sok Ian Sou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is shown that spatial modulation (SM) can be effectively combined with single-carrier (SC) transmission to obtain the advantages in both aspects for broadband wireless communications. However, the signal detection methods for the cyclic-prefix (CP) based SC-SM system are generally not sophisticated enough to provide good performance in many scenarios. In this paper, to lower the frequency-domain equalization complexity, we first consider employing a special recursive method to invert the Gram matrix in the minimum mean-squared-error (MMSE) weight calculation. Second, energy detection is used to locate the active transmit antennas for each SM symbol. Simple single-symbol maximum likelihood (ML) detection can then be used to extract the transmitted modulated symbols in those active transmit antennas with negligible loss in the error-rate performance. On the other hand, M -algorithm is applied for enhancing the error rate to various extents. Results show that a better compromise between the computational complexity and the error rate can.

Original languageEnglish
Title of host publication2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-267
Number of pages5
ISBN (Print)9781538663509
DOIs
Publication statusPublished - 2018 Sep 11
Event3rd International Conference on Computer and Communication Systems, ICCCS 2018 - Nagoya, Japan
Duration: 2018 Apr 272018 Apr 30

Publication series

Name2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018

Other

Other3rd International Conference on Computer and Communication Systems, ICCCS 2018
CountryJapan
CityNagoya
Period18-04-2718-04-30

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Artificial Intelligence

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