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Cascade AOA Estimation Algorithm Based on FMCCA Antenna

FMCCA 안테나 기반 캐스케이드 도래각 추정 알고리즘

  • 김태윤 (조선대학교 전자공학과) ;
  • 황석승 (조선대학교 전자공학부)
  • Received : 2021.10.26
  • Accepted : 2021.12.17
  • Published : 2021.12.31

Abstract

The modern wireless communication system employes the beamforming technique based on a massive array antenna with a number of elements, for supporting the smooth communication services. A reliable beamforming technology requires the Angle-of-Arrival(: AOA) information for the signal incident to the receiving antenna, which is generally estimated by the high-resolution AOA estimation algorithm such as Multiple Signal Classification(: MUSIC). Although the MUSIC algorithm has the excellent estimation performance, it is difficult to estimate AOA in real time for the massive array antenna due to the extremely high computational complexity. In order to enhance this problem, in this paper, we propose the cascade AOA estimation algorithm based on a Flexible Massive Concentric Circular Array(: FMCCA) antenna with the On/Off function for antenna elements. The proposed cascade algorithm consists of the CAPON algorithm using some elements among entire antenna elements and the Beamspace MUSIC algorithm using entire elements. We provide computer simulation results for various scenarios to demonstrate the AOA estimation performance of the proposed approach.

현대 무선통신 시스템은 대규모의 안테나 요소가 장착된 메시브 배열 안테나를 사용하여 다수의 사용자에게 원활한 통신 서비스를 지원하기 위해 빔형성 기술을 활용한다. 신뢰도 높은 빔형성 기술은 안테나로 입사되는 신호에 대한 도래각(Angle-of-Arrival : AOA) 정보가 필수적으로 요구되는데, 일반적으로 도래각 정보는 고분해 성능을 가지는 MUSIC(: Multiple Signal Classification)과 같은 도래각 추정 알고리즘을 통해 추정된다. MUSIC 알고리즘은 우수한 추정성능을 갖지만, 메시브 배열 안테나 사용 시 알고리즘의 급격한 복잡도 증가로 인해 실시간 도래각 추정이 어렵다. 이와 같은 문제점을 개선하기 위해, 본 논문은 안테나 요소 ON/OFF 기능을 가지는 FMCCA(: Flexible Massive Concentric Circular Array) 안테나 기반의 캐스케이드 도래각 추정 알고리즘을 제안한다. 제안된 캐스케이드 AOA 추정 알고리즘은 전체 안테나 요소 중 일부 안테나 요소를 사용하는 CAPON 알고리즘과 전체 안테나 요소를 사용하는 Beamspace MUSIC 알고리즘으로 구성되며, 다양한 시나리오를 가정한 컴퓨터 시뮬레이션을 통해 알고리즘의 도래각 추정 성능을 검증한다.

Keywords

Acknowledgement

이 논문은 2018년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. NRF-2018R1D1A1B07041644). 이 논문은 2021년도 조선대학교 연구비의 지원을 받아 연구되었음.

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