In this paper we optimize the pilot overhead for ultra-reliable short-packet
transmission and investigate the dependence of this overhead on packet size and
error probability. In particular, we consider a point-to-point communication in
which one sensor sends messages to a central node, or base-station, over AWGN
with Rayleigh fading channel. We formalize the optimization in terms of
approximate achievable rates at a given block length, pilot length, and error
probability. This leads to more accurate pilot overhead optimization.
Simulation results show that it is important to take into account the packet
size and the error probability when optimizing the pilot overhead.

本研究旨在优化无线电中超可靠短数据包传输中的导频开销，探索该开销与数据包大小和误差概率之间的关系，并在单个传感器和基站之间的通信中考虑了在给定块长度、导频长度和误差概率下的近似可实现速率。研究表明，在优化导频开销时考虑数据包大小和误差概率非常重要。

针对超可靠短数据包传输优化飞行员开销

Optimizing Pilot Overhead for Ultra-Reliable Short-Packet Transmission

We study the problem of communication over an additive white Gaussian noise
(AWGN) channel with an AWGN feedback channel. When the feedback channel is
noiseless, the classic Schalkwijk-Kailath (S-K) scheme is known to achieve
capacity in a simple sequential fashion, while attaining reliability superior
to non-feedback schemes. In this work, we show how simplicity and reliability
can be attained even when the feedback is noisy, provided that the feedback
channel is sufficiently better than the feedforward channel. Specifically, we
introduce a low-complexity low-delay interactive scheme that operates close to
capacity for a fixed bit error probability (e.g. $10^{-6}$). We then build on
this scheme to provide two asymptotic constructions, one based on high
dimensional lattices, and the other based on concatenated coding, that admit an
error exponent significantly exceeding the best possible non-feedback exponent.
Our approach is based on the interpretation of feedback transmission as a
side-information problem, and employs an interactive modulo-lattice solution.

研究 AWGN 通道反馈通道下的通信问题，提出了一种低延迟低复杂度的交互式方案，以及两种渐进构造方法，该方法基于反馈传输的解释和交互模 lattice 解决方案。

带噪声反馈的 AWGN 信道交互式方案

Interactive Schemes for the AWGN Channel with Noisy Feedback

In this paper, the problem of de-noising of an image contaminated with
Additive White Gaussian Noise (AWGN) is studied. This subject is an open
problem in signal processing for more than 50 years. Local methods suggested in
recent years, have obtained better results than global methods. However by more
intelligent training in such a way that first, important data is more effective
for training, second, clustering in such way that training blocks lie in
low-rank subspaces, we can design a dictionary applicable for image de-noising
and obtain results near the state of the art local methods. In the present
paper, we suggest a method based on global clustering of image constructing
blocks. As the type of clustering plays an important role in clustering-based
de-noising methods, we address two questions about the clustering. The first,
which parts of the data should be considered for clustering? and the second,
what data clustering method is suitable for de-noising.? Then clustering is
exploited to learn an over complete dictionary. By obtaining sparse
decomposition of the noisy image blocks in terms of the dictionary atoms, the
de-noised version is achieved. In addition to our framework, 7 popular
dictionary learning methods are simulated and compared. The results are
compared based on two major factors: (1) de-noising performance and (2)
execution time. Experimental results show that our dictionary learning
framework outperforms its competitors in terms of both factors.

本文针对加性白噪声对图像造成的污染问题，提出并比较了 7 种字典学习方法，并采用全局聚类方法进行图像构建块的聚类去噪，得到了在降噪效果和执行时间两方面皆优的结果。