多输入多输出系统中低误码率信号检测的改进算法
|
摘要
为了进一步提高信号检测的准确度,基于最小均方误差检测算法,提出一种改进的可靠性较高的多输入多输出信号检测算法。改进算法在检测过程中引入排序机制的排序串行干扰抵消算法,优先估计可靠性高的检测分量,结合最大似然检测算法修正可靠性低的分量。结果表明:改进后的检测算法复杂度提升不大,在误码率一定时,信噪比相比与原算法提高了约3 d B;在信噪比一定时,误码率低于原算法,性能得到优化。该研究可以为多输入多输出系统中信号的检测提供参考。
This paper highlights a reliable algorithm underlying multi-input and multi-output signal detection-an improved detection algorithm building on the minimum mean square error detection algorithm in order to achieve an improved accuracy of signal detection. The improved algorithm works by introducing into the detection process the ordered successive interference cancellation algorithm of the sorting mechanism; providing a preferential estimate of the highly reliable detection component; correcting the low reliability component combined with the maximum likelihood detection algorithm; and thereby achieving the better detection. The results demonstrate that the improved algorithm exhibits little improvement in complexity; given the fixed bit error rate,it provides an about 3 d B higher signal-to-noise ratio than the original algorithm; and given the fixed signal-to-noise ratio,it offers the lower bit error rate than the original algorithm,ensuring an optimized performance. The study could serve as a reference for the detection of signals in MIMO systems.
This paper highlights a reliable algorithm underlying multi-input and multi-output signal detection-an improved detection algorithm building on the minimum mean square error detection algorithm in order to achieve an improved accuracy of signal detection. The improved algorithm works by introducing into the detection process the ordered successive interference cancellation algorithm of the sorting mechanism; providing a preferential estimate of the highly reliable detection component; correcting the low reliability component combined with the maximum likelihood detection algorithm; and thereby achieving the better detection. The results demonstrate that the improved algorithm exhibits little improvement in complexity; given the fixed bit error rate,it provides an about 3 d B higher signal-to-noise ratio than the original algorithm; and given the fixed signal-to-noise ratio,it offers the lower bit error rate than the original algorithm,ensuring an optimized performance. The study could serve as a reference for the detection of signals in MIMO systems.
引文
[1]Zhou S,Zhao M,Xu X,et al.Distributed wireless communication system:A new architecture for future public wireless access[J].IEEE Communication Magazine,2003,41(3):108-113.
[2]Sun H,Wang X,You X.Close-formed bound on generalized distributed antenna system capacity[J].Journal of Southeast University,2007,23(4):479-483.
[3]陶小峰,戴佐俊,唐超,等.广义蜂窝网络结构及切换模型-群小区及群切换[J].电子学报,2004,32(12):114-117.
[4]廖文兵.多小区多天线干扰抑制技术研究[D].北京:北京邮电大学,2018.
[5]Arun K,Piyush V,Manisha G.Design,analysis of 4×4 and 8×8 MIMO system with ZF,MMSE and BF techniques[J].The Scientific Bulletin of Electrical Engineering Faculty,2017,17(2):16-19.
[6]柴璐璐.大规模MIMO系统预编码算法研究[J].中国传媒大学学报(自然科学版),2018,25(4):69-73.
[7]张中旺.大规模MIMO系统中信道估计技术研究[D].成都:电子科技大学,2018.
[8]黄兆成,曹海燕,谢时埸,等.多小区Massive MIMO系统中低复杂度MMSE线性检测算法研究[J].无线互联科技,2017(19):108-111.
[9]门宏志,金明录.低复杂度空间调制MPSK信号的最优检测[J].通信学报,2015,36(8):118-124.
[10]刘金铸,薛婷.一种低复杂度近最佳的MIMO信号检测算法[J].南京信息工程大学学报(自然科学版),2013,5(2):167-170.
[2]Sun H,Wang X,You X.Close-formed bound on generalized distributed antenna system capacity[J].Journal of Southeast University,2007,23(4):479-483.
[3]陶小峰,戴佐俊,唐超,等.广义蜂窝网络结构及切换模型-群小区及群切换[J].电子学报,2004,32(12):114-117.
[4]廖文兵.多小区多天线干扰抑制技术研究[D].北京:北京邮电大学,2018.
[5]Arun K,Piyush V,Manisha G.Design,analysis of 4×4 and 8×8 MIMO system with ZF,MMSE and BF techniques[J].The Scientific Bulletin of Electrical Engineering Faculty,2017,17(2):16-19.
[6]柴璐璐.大规模MIMO系统预编码算法研究[J].中国传媒大学学报(自然科学版),2018,25(4):69-73.
[7]张中旺.大规模MIMO系统中信道估计技术研究[D].成都:电子科技大学,2018.
[8]黄兆成,曹海燕,谢时埸,等.多小区Massive MIMO系统中低复杂度MMSE线性检测算法研究[J].无线互联科技,2017(19):108-111.
[9]门宏志,金明录.低复杂度空间调制MPSK信号的最优检测[J].通信学报,2015,36(8):118-124.
[10]刘金铸,薛婷.一种低复杂度近最佳的MIMO信号检测算法[J].南京信息工程大学学报(自然科学版),2013,5(2):167-170.