Non-data-aided ML SNR estimation for AWGN channels with deterministic interference

被引：3

作者：

Chen, Fangjiong ^{[1
]}

Kang, Yabing ^{[1
]}

Yu, Hua ^{[1
]}

Ji, Fei ^{[1
]}

机构：

[1] S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Guangdong, Peoples R China

来源：

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING | 2014年

基金：

中国国家自然科学基金;

关键词：

SNR estimation; Maximum likelihood estimation; Non-data aided estimation; Cramer-Rao bound; SINR;

D O I：

10.1186/1687-1499-2014-45

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Communication channels not only suffer from ambient noise but also from deterministic interference. In this paper, we consider signal-to-noise ratio (SNR) estimation in the presence of constant deterministic interference. A maximum likelihood (ML) non-data-aided algorithm is proposed for SNR estimation. We first consider a real-valued model and then extend this to a complex-valued model. The proposed algorithm applies an iterative approach initialized with approximate closed form estimates so as to guarantee stability and convergence. Furthermore, the Cramer-Rao bound (CRB) is also derived as the theoretical limit of the jitter variance. Computer simulations based on pulse-amplitude modulation (PAM) and quadrature amplitude modulation (QAM) sources show that the performance of the proposed algorithm is close to the CRB.

引用

页数：10

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