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A Low-Complexity ML Detector for Generalized Spatial Modulation Based on Priority

GSM을 위한 우선순위 기반 저복잡도 ML 검출 기법

  • Lee, Man Hee (Department of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Shin, Soo Young (Kumoh National Institute of Technology)
  • Received : 2016.12.14
  • Accepted : 2017.01.24
  • Published : 2017.04.30

Abstract

In this paper, we proposed a modified ML detector for generalized spatial modulation which is a method among Multiple-input Multiple-output. This proposed method detects signal applying modified channel statement information based on priority. Complexity in conventional methods increases as increasing the number of active antennas. To solve this problem, we proposed a new ML method using static channel information decided by the number of transmit antennas and the number of receive antennas. This method detects active antennas one by one through priority. The proposed method has proved benefit on complexity compared with conventional method through simulations. When the number of transmit antennas is equal to 10, there is approximately 45% complexity reduction.

본 논문은 Multiple-input Multiple-output (MIMO) 기법 중에 하나인 Generalized Spatial Modulation (GSM)을 위해 변형된 ML 검출 기법을 제안한다. 기존의 일반적인 ML 검출 기법은 활성 안테나의 수가 증가함에 따라 채널 정보의 크기가 커지기 때문에 높은 복잡도를 가지는 문제가 있다. 이를 해결하기 위하여 송수신 안테나의 수로 고정되는 채널 정보를 이용하고 우선순위를 정하여 그에 따라 활성 안테나 인덱스를 하나씩 검출함으로써 낮은 복잡도를 가지는 기법을 제안한다. 본 논문에서는 모의실험을 통해서 기존의 ML 검출 기법과 제안하는 기법의 복잡도와 Bit Error Rate (BER)을 통해 성능을 평가하고 비교하였다. 복잡도 계산을 위한 모의실험에서 제안하는 검출기법으로 송신 안테나의 수가 10개일 때, 복잡도가 약 45% 감소하는 것을 확인하였다.

Keywords

References

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