A Novel and Low-Complexity Algorithm of EDAS for Spatial Modulation Systems

One of the techniques that helps to reduce the error probability for communication systems is transmit antenna selection (TAS). TAS with maximizing the minimum Euclidean distance, called Euclidean Distance Antenna Selection (EDAS), achieves high transmit diversity for spatial modulation (SM) systems...

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Vydáno v:IEEE communications letters Ročník 24; číslo 12; s. 2922 - 2925
Hlavní autor: Altin, Gokhan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
antenna selection
Antennas
Codes
Communications systems
Complexity
Complexity theory
computational complexity
EDAS
eigendecomposition
Eigenvectors
Error reduction
Euclidean distance
Euclidean geometry
Mathematical analysis
Matrix methods
Modulation
Optimization
Quadrature amplitude modulation
Receiving antennas
Spatial modulation
Transmitting antennas
ISSN:1089-7798, 1558-2558
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Popis
Shrnutí:One of the techniques that helps to reduce the error probability for communication systems is transmit antenna selection (TAS). TAS with maximizing the minimum Euclidean distance, called Euclidean Distance Antenna Selection (EDAS), achieves high transmit diversity for spatial modulation (SM) systems. This letter focuses on a novel, very simple and low complexity antenna selection algorithm for EDAS. The main idea of this work is based on using eigenvectors obtained from the eigendecomposition of the matrix composed from the transmit SM signal difference vector. However, due to the characteristics of the SM, only certain eigenvectors are sufficient to achieve the Euclidean distance instead of all possible combinations of transmitted symbols. In addition, an algorithm is given in this study to find the required eigenvectors without the need for eigendecomposition. Simulation results show that our proposed method exhibits same symbol error rate performance as conventional optimum EDAS, but having lower complexity.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.3016066