数字通信中抗脉冲噪声的软输出检测稳健度量
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"这篇文章主要探讨了在存在Class-A噪声的信道中进行软输出检测的稳健度量方法。研究从信息论的角度出发，分析了这种信道模型对通信性能的限制，并介绍了两种实用的通信方案，重点是提出了一种简单且稳健的检测策略，该策略不需要估计脉冲噪声的统计特性。"
在数字通信中，尤其是在受到脉冲噪声影响的信道上传输时，噪声的存在会严重降低信号的传输质量和可靠性。Class-A噪声模型是一种广泛用于描述具有显著脉冲性质干扰的统计模型，常见于功率分配网络和某些移动通信场景。该模型考虑了噪声的突发性和不规则性，这对通信系统的性能评估和设计提出了挑战。
文章首先从信息论的角度讨论了这种信道模型下的性能极限，即在给定的信道条件下，可以达到的最大传输信息速率（achievable information rate）。这有助于理解在脉冲噪声环境下，通信系统理论上的最大潜力以及实际实现中的限制因素。
接着，作者描述并比较了两种采用强效信道编码和迭代解码的通信方案，这两种方案旨在提高系统在恶劣信道条件下的鲁棒性。其中，迭代解码技术如低密度奇偶校验(LDPC)码和涡轮码(Turbo码)能够有效地纠正传输错误，提高错误率性能。
文章的重点在于介绍了一种非常简单且稳健的软输出检测方法，这种方法的一个显著优点是它不依赖于对脉冲噪声统计特性的精确估计。传统的检测方法往往需要噪声统计信息来优化检测过程，但这种方法简化了这一要求，降低了实现复杂性，同时保持了良好的检测性能。
软输出检测在现代通信系统中起着关键作用，因为它不仅提供关于接收符号最可能状态的信息，还提供了关于这些估计的不确定性，这对于迭代解码尤其重要。这种额外的输出信息可以改善解码性能，特别是在存在不确定性和非高斯噪声的情况下。
这篇论文提供了一种在脉冲噪声环境下进行有效且稳健通信的新思路，对于设计能够抵抗此类噪声影响的通信系统具有重要的理论和实践价值。通过采用这种检测策略，即使在难以预测的噪声条件下，也能实现更高效、可靠的通信。
36 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 57, NO. 1, JANUARY 2009
A Robust Metric for Soft-Output Detection
in the Presence of Class-A Noise
Dario Fertonani, Student Member, IEEE, and Giulio Colavolpe, Member, IEEE
Abstract—Digital communications over channels impaired by
impulse noise are considered. We first address the problem from
an information-theoretical viewpoint, discussing the performance
limits imposed by the channel model. Then, we describe and
compare a couple of practical communication schemes employing
powerful channel codes and iterative decoding, with focus on a
very simple and robust detection scheme that does not require
the estimation of the statistics of the impulse noise.
Index Terms—Impulse noise, soft-output detection, achievable
information rate.
I. INTRODUCTION
T
HE power delivery networks and some mobile radio
scenarios are often characterized by interferences that
exhibit a significant impulsive nature. Among the various
statistical models for these phenomena, generally referred to as
“impulse noise”, the most widely used in the literature is the
class-A model [1], which is adopted also in this letter. The
performance of such systems is generally studied under the
assumption of ideal knowledge of the statistical properties of
the impulse noise [2]–[4]. These statistics, which are actually
unknown to the receiver, can be effectively estimated [5], but
the estimation algorithms unfortunately affect the complexity
of the system and cannot properly cope with time-varying
channels [5]–[8]. A blind approach, based on detection metrics
that do not require the knowledge of the channel parameters
nor their estimation, is thus of great interest.
We first resort to information-theoretical arguments and dis-
cuss the ultimate performance limits imposed by the channel,
then we consider practical communication schemes employing
powerful codes and iterative decoding [9], [10]. In particular,
we propose a detection scheme that does not require to know
nor to estimate the statistics of the impulse noise, and compare
it with an ideal receiver that perfectly knows such statistics
and with the soft-limiting receivers [11], which are usually
considered as a reference benchmark for robust detection over
class-A channels. These comparisons prove the effectiveness
of the proposed solution, which performs practically as the
ideal one and much better than the classical soft-limiting
receivers.
II. C
HANNEL MODEL
A sequence c
K
1
= {c
k
}
K
k=1
of M-ary complex-valued
symbols, possibly obtained by properly encoding a sequence
Paper approved by T. M. Duman, the Editor for Coding Theory and
Applications of the IEEE Communications Society. Manuscript received April
12, 2007; revised October 8, 2007.
The authors are with the Department of Information Engineering, Uni-
versity of Parma, Viale G. P. Usberti 181/A, 43100 Parma, Italy (e-mail:
fertonani@tlc.unipr.it; giulio@unipr.it).
This work was presented in part at the IEEE Global Communications
Conference (GLOBECOM’07), Washington, DC, USA, November 2007.
Digital Object Identifier 10.1109/TCOMM.2009.0901.070041
of information bits, is linearly modulated and transmitted over
an additive white Gaussian noise (AWGN) channel that also
introduces impulse noise.
1
Assuming ideal synchronization
and absence of intersymbol interference, we can write the
received samples as [1]
y
k
= c
k
+ n
k
,k∈{1, 2,...,K} (1)
where n
K
1
is a sequence of independent and identically
distributed noise samples. At each time epoch k, the statistical
properties of the sample n
k
are completely definedbythe
channel state s
k
, which belongs to the set of the non-negative
integers N, and assumes the value i ∈ N with probability [1]
P
i
=
e
−A
A
i
i!
(2)
where A is a positive parameter characterizing the channel,
generally referred to as “impulsive index”. In particular,
the sample n
k
is a complex circularly-symmetric Gaussian
random variable with variance depending on s
k
, so that the
probability density function (PDF) of n
k
conditioned to s
k
can be written as [1]
p(n
k
|s
k
= i)=
1
2πσ
2
i
exp
−
|n
k
|
2
2σ
2
i
,i∈ N (3)
where σ
2
i
is the variance per component of the noise samples
when s
k
= i. Hence, the PDF of the generic sample n
k
results
p(n
k
)=
∞
i=0
P
i
p(n
k
|s
k
= i)=
∞
i=0
P
i
2πσ
2
i
exp
−
|n
k
|
2
2σ
2
i
.
(4)
The variances {σ
2
i
} can be written as
σ
2
i
=
1+
i
AΓ
σ
2
0
,i∈ N (5)
where σ
2
0
can be interpreted as the variance per component of
the background Gaussian noise, while Γ is a positive parameter
describing the power of the impulse noise [1]. Namely, since
the average power of the noise samples is
E{|n
k
|
2
} =2
∞
i=0
P
i
σ
2
i
=2σ
2
0
+
2σ
2
0
Γ
(6)
the channel introduces, in addition to the background Gaussian
noise with average power 2σ
2
0
, an impulsive contribution with
average power 2σ
2
0
/Γ.
By properly setting the values of the parameters A and Γ,
a large variety of channels with different statistical proper-
ties can be described [1], [2]. In this work, we focus on
scenarios where the presence of impulsive noise, that is the
event {s
k
> 0}, is relatively infrequent with respect to the
1
For any sequence {v
k
}, we denote the subsequence {v
k
}
n
2
k=n
1
by v
n
2
n
1
.
0090-6778/09$25.00
c
2009 IEEE
Authorized licensed use limited to: University of New Brunswick. Downloaded on October 1, 2009 at 16:31 from IEEE Xplore. Restrictions apply.
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