The conventional design of wireless communication systems typically relies on
established mathematical models that capture the characteristics of different
communication modules. Unfortunately, such design cannot be easily and directly
applied to future wireless networks, which will be characterized by large-scale
ultra-dense networks whose design complexity scales exponentially with the
network size. Furthermore, such networks will vary dynamically in a significant
way, which makes it intractable to develop comprehensive analytical models.
Recently, deep learning-based approaches have emerged as potential alternatives
for designing complex and dynamic wireless systems. However, existing
learning-based methods have limited capabilities to scale with the problem size
and to generalize with varying network settings. In this paper, we propose a
scalable and generalizable neural calibration framework for future wireless
system design, where a neural network is adopted to calibrate the input of
conventional model-based algorithms. Specifically, the backbone of a
traditional time-efficient algorithm is integrated with deep neural networks to
achieve a high computational efficiency, while enjoying enhanced performance.
The permutation equivariance property, carried out by the topological structure
of wireless systems, is furthermore utilized to develop a generalizable neural
network architecture. The proposed neural calibration framework is applied to
solve challenging resource management problems in massive multiple-input
multiple-output (MIMO) systems. Simulation results will show that the proposed
neural calibration approach enjoys significantly improved scalability and
generalization compared with the existing learning-based methods.

本文提出了一种可扩展和通用的神经校准框架，其中采用神经网络来校准传统基于模型的算法的输入，应用于解决大规模 MIMO 系统中的资源管理问题，并表明与现有的基于学习的方法相比，提出的神经校准方法具有更好的可伸缩性和普适性。

神经校准通讯的学习：可扩展性与泛化

Learn to Communicate with Neural Calibration: Scalability and  Generalization

It is often observed that the probabilistic predictions given by a machine
learning model can disagree with averaged actual outcomes on specific subsets
of data, which is also known as the issue of miscalibration. It is responsible
for the unreliability of practical machine learning systems. For example, in
online advertising, an ad can receive a click-through rate prediction of 0.1
over some population of users where its actual click rate is 0.15. In such
cases, the probabilistic predictions have to be fixed before the system can be
deployed.
In this paper, we first introduce a new evaluation metric named field-level
calibration error that measures the bias in predictions over the sensitive
input field that the decision-maker concerns. We show that existing post-hoc
calibration methods have limited improvements in the new field-level metric and
other non-calibration metrics such as the AUC score. To this end, we propose
Neural Calibration, a simple yet powerful post-hoc calibration method that
learns to calibrate by making full use of the field-aware information over the
validation set. We present extensive experiments on five large-scale datasets.
The results showed that Neural Calibration significantly improves against
uncalibrated predictions in common metrics such as the negative log-likelihood,
Brier score and AUC, as well as the proposed field-level calibration error.

本文提出了一种新的评估指标，称为 “field-level calibration error”，用于衡量决策者关注的敏感输入领域中预测偏差，提出了一种名为神经校准的后续校准方法，使用验证集中的领域感知信息进行校准，并通过实验证明其对常见度量（如负对数似然、布里尔分数和 AUC）以及所提出的 “field-level calibration error” 指标的校准性能得到了显著提高。