We introduce a noisy channel approach for language model prompting in
few-shot text classification. Instead of computing the likelihood of the label
given the input (referred as direct models), channel models compute the
conditional probability of the input given the label, and are thereby required
to explain every word in the input. We use channel models for recently proposed
few-shot learning methods with no or very limited updates to the language model
parameters, via either in-context demonstration or prompt tuning. Our
experiments show that, for both methods, channel models significantly
outperform their direct counterparts, which we attribute to their stability,
i.e., lower variance and higher worst-case accuracy. We also present extensive
ablations that provide recommendations for when to use channel prompt tuning
instead of other competitive methods (e.g., direct head tuning): channel prompt
tuning is preferred when the number of training examples is small, labels in
the training data are imbalanced, or generalization to unseen labels is
required.

介绍了一种嘈杂通道方法来进行少样本文本分类中的语言模型提示。实验表明，使用嘈杂通道模型可显著优于直接模型，特别适用于训练样本小，训练数据标签不平衡或需要推广到未见标签的情况。

基于无噪声通道语言模型的少样本文本分类

Noisy Channel Language Model Prompting for Few-Shot Text Classification

Millimeter-wave (mmWave) communication is one of the cornerstone innovations
of fifth-generation (5G) wireless networks, thanks to the massive bandwidth
available in these frequency bands. To correctly assess the performance of such
systems, however, it is essential to have reliable channel models, based on a
deep understanding of the propagation characteristics of the mmWave signal. In
this respect, ray tracers can provide high accuracy, at the expense of a
significant computational complexity, which limits the scalability of
simulations. To address this issue, in this paper we present possible
simplifications that can reduce the complexity of ray tracing in the mmWave
environment, without significantly affecting the accuracy of the model. We
evaluate the effect of such simplifications on link-level metrics, testing
different configuration parameters and propagation scenarios.

本文研究了毫米波通信系统频段的信道模型，提出了一种简化射线追踪过程的方法，并测试了不同的配置参数和传播场景，以评估其对链路级指标的影响。

毫米波信道的简化光线追踪：性能评估

Simplified Ray Tracing for the Millimeter Wave Channel: A Performance  Evaluation

This paper provides an analysis of radio wave scattering for frequencies
ranging from the microwave to the Terahertz band (e.g., 1 GHz - 1 THz), by
studying the scattering power reradiated from various types of materials with
different surface roughnesses. First, fundamentals of scattering and reflection
are developed and explained for use in wireless mobile radio, and the effect of
scattering on the reflection coefficient for rough surfaces is investigated.
Received power is derived using two popular scattering models - the directive
scattering (DS) model and the radar cross section (RCS) model through
simulations over a wide range of frequencies, materials, and orientations for
the two models, and measurements confirm the accuracy of the DS model at 140
GHz. This paper shows that scattering can become a prominent propagation
mechanism as frequencies extend to millimeter-wave (mmWave) and beyond, but at
other times can be treated like simple reflection. Knowledge of scattering
effects is critical for appropriate and realistic channel models, which further
support the development of massive multiple input-multiple output (MIMO)
techniques, localization, ray tracing tool design, and imaging for future 5G
and 6G wireless systems.

本文分析了从微波到太赫兹频段（例如，1 GHz - 1 THz）的射频散射，通过研究不同表面粗糙度的材料所重新辐射的散射功率。文章表明，散射可以成为一种突出的传播机制，但有其他时间可以像简单的反射一样处理，认识到散射效应对于适当和现实的通道模型至关重要，可以进一步支持未来 5G 和 6G 无线系统的大规模多输入多输出（MIMO）技术、本地化、射线追踪工具设计和成像。

太赫兹无线通信的散射机制与建模

Scattering Mechanisms and Modeling for Terahertz Wireless Communications

We consider detection based on deep learning, and show it is possible to
train detectors that perform well without any knowledge of the underlying
channel models. Moreover, when the channel model is known, we demonstrate that
it is possible to train detectors that do not require channel state information
(CSI). In particular, a technique we call a sliding bidirectional recurrent
neural network (SBRNN) is proposed for detection where, after training, the
detector estimates the data in real-time as the signal stream arrives at the
receiver. We evaluate this algorithm, as well as other neural network (NN)
architectures, using the Poisson channel model, which is applicable to both
optical and molecular communication systems. In addition, we also evaluate the
performance of this detection method applied to data sent over a molecular
communication platform, where the channel model is difficult to model
analytically. We show that SBRNN is computationally efficient, and can perform
detection under various channel conditions without knowing the underlying
channel model. We also demonstrate that the bit error rate (BER) performance of
the proposed SBRNN detector is better than that of a Viterbi detector with
imperfect CSI as well as that of other NN detectors that have been previously
proposed. Finally, we show that the SBRNN can perform well in rapidly changing
channels, where the coherence time is on the order of a single symbol duration.

该研究利用深度学习进行检测，通过提出一种双向递归神经网络（SBRNN）的技术，能够在不知道通道模型的情况下进行检测，且在分子通信系统中表现出良好的性能。