半盲最小二乘恒模算法研究  

Analysis of the semi-blind LSCM algorithm

在线阅读下载全文

作  者:刘胜美[1] 潘甦[1] 朱琦[1] 酆广增[1] 赵春明[1] 

机构地区:[1]南京邮电大学移动通信实验室,江苏南京210003

出  处:《电路与系统学报》2008年第6期119-123,共5页Journal of Circuits and Systems

基  金:国家自然科学基金项目(60772061);973项目(2007CB310607);江苏省教育厅指导性项目(06KJD510137)

摘  要:恒模算法(CMA)是一种广泛应用于阵列处理、均衡、多用户检测中的盲算法。最小二乘恒模算法(LSCMA)由于其全局收敛性及稳定性受到关注。本文针对CDMA系统下行链路,基站知道小区内用户码字而小区外干扰用户码字未知的情形,提出一种适用的半盲LSCM多用户检测(MUD)算法。它将非盲多用户检测(本文中选用解相关MUD)与盲多用户检测技术(本文中选用LSCM检测器)相结合,首先根据小区内已知用户的信息,利用解相关MUD抵消小区内其它用户的干扰,接着利用LSCM算法抵消剩余的干扰。文中将SB-LSCM算法与已经提出的半盲解相关算法、LSCM算法和解相关算法进行了复杂度、SIR和BER性能的比较,并对SB-LSCM算法的SIR性能进行了理论分析。仿真结果表明SB-LSCM算法能够获得与半盲解相关相当的SIR的性能,但是其复杂度更低且在系统负荷大时能够获得优于半盲解相关算法的BER性能。另外,SB-SLCM算法能够获得较LSCM算法更快的收敛速度以及更加优良的性能。The constant modulus algorithm(CMA) is a blind algorithm. It is applied in the array processing, equalization and multiuser detection widely. The least square CMA is a popular CMA because of its global convergence and stability. In this paper, a semi-blind multi-user detection (MUD) algorithm for CDMA systems is proposed where the codes of intra-cell users are known but the codes of inter-cell users are unknown. This semi-blind Least Square Constant Modulus algorithm (SB-LSCMA) is a joint MUD algorithm of decorrelating algorithm and LSCM algorithm. The proposed approach fully utilizes all known users' information, performs non-blind detector to suppress the interferers within the cell, and exploits blind LSCM detector to suppress the remain interferences. The performance of the semi-blind LSCM detector is compared to that of the non-blind decorrelating detector and blind LSCM detector, and it is shown that the semi-blind detector can achieve a considerable performance improvement. Moreover, the performance of the SB-LSCM algorithm is close to that of the semi-blind decorrelating algorithm with much lower complexity.

关 键 词:多用户检测 最小二乘恒模算法 半盲最小二乘恒模算法 

分 类 号:TN919[电子电信—通信与信息系统]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象