基于MBER准则的变阶长自适应均衡器  被引量:1

Adaptive variable tap-length equalizers based on MBER criterion

在线阅读下载全文

作  者:张文秋[1] 丁文锐[2] 刘春辉[2,3] ZHANG Wenqiu;DING Wenrui;LIU Chunhui(School of Electronics and Information Engineering, Beihang University, Beijing 100191, China;Institute of Unmanned Aerial Vehicle, Beihang University, Beijing 100191, China;School of Computer Science and Engineering, Beihang University, Beijing 100191, China)

机构地区:[1]北京航空航天大学电子信息工程学院,北京100191 [2]北京航空航天大学无人驾驶飞行器设计研究所,北京100191 [3]北京航空航天大学计算机学院,北京100191

出  处:《计算机工程与应用》2017年第8期87-91,234,共6页Computer Engineering and Applications

基  金:国家高技术研究发展计划(863)(No.2013AA122101);总装备部预先研究基金

摘  要:提出了基于最小误比特率(MBER)准则的变阶长自适应均衡算法——FT-MBER算法。变阶长自适应均衡是未知多径信道均衡的重要技术,准确估计自适应均衡器最佳阶长能同时实现低复杂度和较好的均衡性能,而传统的最小均方误差(MMSE)算法稳态误比特率性能不理想。FT-MBER算法以最小化BER为代价函数,把不同阶长均衡器产生的误比特率之差作为因子调节伪分数阶长,当伪分数阶长变化大于阈值时更新阶长。仿真结果表明该算法比MMSE算法能更有效抑制码间干扰并能准确估计MBER准则下的均衡器最佳阶长。A variable tap-length adaptive equalizer based on the Minimum Bit Error Rate(MBER)approach is proposed,named FT-MBER algorithm.Variable tap-length equalization is important to the problem of unknown multipath channel equalization.Accurate optimum tap-length estimation of the adaptive equalizer can complete the low complexity and good performance of the adaptive algorithm,but the Bit Error Rate(BER)performance of traditional equalizers based on Minimum Mean Square Error(MMSE)criterion isn’t ideal.FT-MBER algorithm takes the difference of the BER between adaptive equalizers with different tap-lengths as a factor to adjust the pseudo fractional tap-length,and only if the pseudo fractional tap-length is bigger than the threshold value,the tap-length is updated.Simulation results show the BER performance of FT-MBER algorithm is much better than traditional equalizers,can successfully track and estimate the optimum tap-length.

关 键 词:自适应均衡器 最小误比特率准则 分数阶长算法 误比特率 

分 类 号:TN911.72[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