Blind Equalization under Noisy Environment using Bias-compensated RLS method

被引：0
|
作者：
Zhang, Zhen [1 ]
Jia, Lijuan [1 ]
Kanae, Shunshoku [2 ]
Yang, Zi-Jiang [3 ]
机构：
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[2] Junshin Gakuen Univ, Dept Med Engn, Fac Hlth Sci, Fukuoka 8158510, Japan
[3] Ibaraki Univ, Dept Intelligent Syst Engn, Ibaraki 3168511, Japan
来源：
2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年
基金：
中国国家自然科学基金;
关键词：
Blind Equalization; Recursive Least Squares Algorithm; Variance Estimation; Bias-compensated Method; SELF-RECOVERING EQUALIZATION; ADAPTIVE IDENTIFICATION; ALGORITHM; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a new blind adaptive equalization algorithm under noisy environment is proposed. We consider a practical case where the noise of each transmission channel is unknown. By oversampling the channel output at twice the symbol rate, a single-input double-output channel can be obtained. We apply the recursive-least-squares (RLS) to tackle the blind equalization problem. With the noise-induced bias, RLS algorithm is biased. In order to eliminate the bias, we present a bias-compensated RLS (BCRLS) algorithm that can estimate the unknown additive noise online and the noise-induced bias can be therefore removed. The unbiased estimate of the channel characteristics obtained can be used for channel equalization. Simulations results are presented to demonstrate the performance of the proposed algorithm.
引用
收藏
页码：3127 / 3131
页数：5
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