AWGN信道下的非数据辅助ML SNR估计：确定性干扰下的逼近优化

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本文主要探讨了在存在确定性干扰的阿贝耳高斯信道(AWGN, Additive White Gaussian Noise)下，非数据辅助最大似然(ML, Maximum Likelihood)信噪比(SNR, Signal-to-Noise Ratio)估计的问题。作者是Fangjiong Chen、Yabing Kang、Hua Yu 和 Fei Ji，他们在2014年发表于《欧洲无线通信与网络》期刊(EURASIP Journal on Wireless Communications and Networking)的一篇研究论文中提出了这一创新算法。在传统的通信系统中，信号不仅受到环境噪声的影响，还可能面临确定性的干扰，如同步问题或设备间的固定干扰。为了提高在这样的复杂环境下信噪比估计的准确性，论文提出了一种非数据辅助的ML SNR估计算法。首先，作者们从实值模型出发，构建了一个迭代方法，这个方法以近似的闭形式估计作为初始值，确保了算法的稳定性和收敛性。这种方法避免了依赖额外的数据信息来优化估计过程，提高了系统的效率和实用性。此外，文章还讨论了克雷默-劳埃德不等式(Cramer-Rao Bound, CRB)的应用，这是一种理论上的性能极限，用来衡量任何估计器的性能下限。通过基于脉冲幅度调制(Pulse Amplitude Modulation, PAM)和正交幅度调制(Quadrature Amplitude Modulation, QAM)信号的计算机仿真，研究结果表明，他们提出的算法能够接近理论上的最优性能，即克雷默-劳埃德不等式的界限，显示出其在实际应用中的高效性和有效性。总结来说，这篇研究论文对于处理具有确定性干扰的AWGN信道中的SNR估计问题提供了创新的解决方案，通过非数据辅助的ML方法实现了与理论极限相近的性能。这对于设计抗干扰能力强、实时性能优良的通信系统具有重要的理论价值和实际意义。

Chen et al. EURASIP Journal on Wireless Communications and Networking

2014, 2014:45

http://jwcn.eurasipjournals.com/content/2014/1/45

RESEARCH Open Access

Non-data-aided ML SNR estimation for AWGN

channels with deterministic interference

Fangjiong Chen, Yabing Kang, Hua Yu

and Fei Ji

Abstract

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.

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

1 Introduction

Besides ambient noise, the communication channels may

suffer from deterministic interference. In terrestrial wire-

less s ystems, the competing users sharing the same spec-

trum resource, or simply the dr ift of the system’s baseline,

may introduce some so rt of deterministic interference

[1,2]. When a direct conversion receiver is applied, the

demodulator output is usually impaired by a direct cur-

rent (DC) offset due to self-mixing [3], which might be

considered as some sort of deterministic interference.

More recently, based on an experimental underwater

communication system, Wang et al. [4] observed that

unknown users transmitting multiple sonar waveforms

in the same environment may lead to significant perfor-

mance degradation. In [4], the deterministic interference

is modelled as known waveform with unknown param-

eters. Interference reconstr uction and cancelation was

appliedtoimprovethesystemperformance.

The signal-to-noise ratio (SNR), defined as the ratio of

the signal power to the noise power, is f requently used

as the system performance measure [5-8]. In ca ses where

interference is present, the interference can be estimated

and removed from the estimated signal [2,3]. In [1,6,7], the

signal-to-interference-plus-noise ratio (SINR), instead of

*Correspondence: yuhua@scut.edu.cn

School of Electronic and Information Engineering, South China University of

Technology, Guangzhou 510640, China

SNR, is applied as the system measure. In [1], a non-data-

aided (NDA) algorithm, base d on fourth-order statistics,

was proposed for SINR estimation, where the interference

was modeled as a constant. In [6] and [7], SINR estimation

in cellular systems is investigated, where the interference

stems from competing users in other cells .

In [6,7], the interference was modelled as zero-mean

random variable. The algor ithms indeed cannot deal with

deterministic interference. The fourth-order statistics-

based algorithm in [1] re quires a large quantity of samples

(more than 1,000). It may not be effective to assume that

the interference is constant during thousands of symb ols.

The maximum likelihood (ML) algorithm in [2] needs

only tens of samples. However, the algorithm assumes no

attenuation of the source signal and hence is not applica-

ble to SNR estimation.

OurgoalinthispaperistodevelopaNDASNResti-

mation algorithm which provides satisfactory estimates

with a small size of samples (e.g., tens of samples). We

assume a slowly time-varying channel such that over the

observation interval, the channel gain and the interference

can be assumed to be constant . In natural, therefore, this

is an additive noise channel model with attenuation fac-

tor and de ter ministic interference dur ing the observation

interv al.

Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction

in any medium, provided the original work is properly cited.

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