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A variational inference-based detection method for repetition coded generalized spatial modulation

journal contribution
posted on 2019-03-01, 00:00 authored by Jinho ChoiJinho Choi
In this paper, we consider a simple coding scheme for spatial modulation, where the same set of active transmit antennas is repeatedly used over consecutive multiple transmissions. Based on a Gaussian approximation, an approximate maximum likelihood (ML) detection problem is formulated to detect the indices of active transmit antennas. We show that the solution to the approximate ML detection problem can achieve a full coding gain. Furthermore, we develop a low-complexity iterative algorithm to solve the problem with low complexity based on a well-known machine learning approach, i.e., variational inference. Simulation results show that the proposed algorithm can have a near ML performance. A salient feature of the proposed algorithm is that its complexity is independent of the number of active transmit antennas, whereas an exhaustive search for the ML problem requires a complexity that grows exponentially with the number of active transmit antennas.

History

Journal

IEEE transactions on communications

Volume

67

Issue

3

Pagination

2569 - 2579

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

ISSN

0090-6778

eISSN

1558-0857

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE