The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Distributed BS Transmit Power Control for Utility Maximization in Small-Cell Networks

소형 셀 환경에서 유틸리티 최대화를 위한 분산화된 방법의 기지국 전송 전력 제어

  • 이창식 (KAIST 전기 및 전자공학과 네트워크시스템 연구실) ;
  • 김지환 (KAIST 전기 및 전자공학과 네트워크시스템 연구실) ;
  • 곽정호 (KAIST 전기 및 전자공학과 네트워크시스템 연구실) ;
  • 김은경 ;
  • 정송 (KAIST 전기 및 전자공학과 네트워크시스템 연구실)
  • Received : 2013.10.21
  • Accepted : 2013.12.19
  • Published : 2013.12.31
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Abstract

Small cells such as pico or femto cells are promising as a solution to cope with higher traffic explosion and the large number of users. However, the users within small cells are likely to suffer severe inter-cell interference (ICI) from neighboring base stations (BSs). To tackle this, several papers suggest BS transmit power on/off control algorithms which increase edge user throughput. However, these algorithms require centralized coordinator and have high computational complexity. This paper makes a contribution towards presenting fully distributed and low complex joint BS on/off control and user scheduling algorithm (FDA) by selecting on/off pattern of BSs. Throughput the extensive simulations, we verify the performance of our algorithm as follows: (i) Our FDA provides better throughput performance of cell edge users by 170% than the algorithm without the ICI management. (ii) Our FDA catches up with the performance of optimal algorithm by 88-96% in geometric average throughput and sufficiently small gap in edge user throughput.

최근 많은 수의 모바일 유저와 과도한 트래픽 증가를 해결하기 위한 솔루션으로 피코 혹은 펨토 셀과 같은 소형 셀을 설치하는 방법이 주목 받고 있다. 그러나 소형 셀에 속한 엣지 유저들은 주변 기지국으로부터 극심한 셀간 간섭을 받기 때문에 낮은 평균 전송률을 얻게 되고, 이를 해결하기 위해서는 셀 간 간섭을 효과적으로 관리하는 방법이 필요하다. 최근의 많은 연구들은 셀 간 간섭 관리를 위해 기지국의 전송 전력을 제어하는 알고리즘을 제시하였지만, 제시된 방법들은 높은 복잡도를 가지고 중앙 기지국의 도움을 필요로 한다는 단점이 있다. 본 연구에서는 기지국간의 경쟁을 기반으로 하여, 낮은 복잡도를 가지는 분산화된 방법의 기지국 전송 전력 on/off 제어 및 유저 스케줄링 알고리즘을 제안한다. 시뮬레이션 결과를 통해, 제안하는 방법이 셀 간 간섭 관리를 하지 않는 방법에 비해 셀 엣지 유저의 경우 170%의 성능 개선을 보이고, 최적의 알고리즘과 비교 했을 때 88-96%에 달하는 geometric average throughput (GAT) 성능 및 매우 근접한 average edge user throughput (AET) 성능을 보임을 검증한다.

Keywords

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