Lattice-Reduction-Aided Preceding Using Seysen's Algorithm for Multi-User MIMO Systems

다중 사용자 다중 입출력 시스템에서 Seysen 기법을 이용한 격자 감소 기반 전부호화 기법

  • 송형준 (연세대학교 전기전자공학부) ;
  • 홍대식 (연세대학교 전기전자공학부)
  • Published : 2009.06.25

Abstract

We investigate lattice-reduction-aided precoding techniques for multi-user multiple-input multiple-output (MIMO) channels. When assuming full knowledge of the channel state information only at the transmitter, a vector perturbation (VP) is a promising precoding scheme that approaches sum capacity and has simple receiver. However, its encoding is nondeterministic polynomial time (NP)-hard problem. Vector perturbation using lattice reduction algorithms can remarkably reduce its encoding complexity. In this paper, we propose a vector perturbation scheme using Seysen's lattice reduction (VP-SLR) with simultaneously reducing primal basis and dual one. Simulation results show that the proposed VP-SLR has better bit error rate (BER) and larger capacity than vector perturbation with Lenstra-Lenstra-Lovasz lattice reduction (VP-LLL) in addition to less encoding complexity.

본 논문에서는 다중 사용자 다중 입출력 시스템을 위해 격자 감소 기법 기반 전부호화(preceding) 기법에 대해 연구하였다. 송신 단에서 완벽한 채널 상태 정보(CSI : Channel state information)를 이용할 수 있을 때, 벡터 분산 기법(VP : vector perturbation)은 큰 채널 전송 용량(sum capacity)을 얻을 수 있으면서 간단한 수신기로 구현될 수 있다. 그러나 VP 기법의 부호화는 비결정적 난해(NP-hard : nondeterministic polynomial time-hard) 문제이다. 이에 반해 격자 감소 기법 기반의 VP 기법은 부호화 복잡도를 크게 줄일 수 있다. 본 논문에서는 기본 및 이중 베이시스의 동시 감소를 통한 Seysen 격자 감소 기반 VP 기법(VP-SLR : vector perturbation with Seysen's lattice reduction)을 제안한다. 모의실험 결과는 LLL 기반 VP기법(VP-LLL : vector perturbation with Lenstra-Lenstra-Lovasz lattice reduction)에 비해 제안된 VP-SLR 기법이 더 낮은 비트 오류율(BER : bit error rate)과 더 큰 전송 용량을 가짐을 보여준다.

Keywords

References

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