Radio Access Networks (RANs) for telecommunications represent large
agglomerations of interconnected hardware consisting of hundreds of thousands
of transmitting devices (cells). Such networks undergo frequent and often
heterogeneous changes caused by network operators, who are seeking to tune
their system parameters for optimal performance. The effects of such changes
are challenging to predict and will become even more so with the adoption of
5G/6G networks. Therefore, RAN monitoring is vital for network operators. We
propose a self-supervised learning framework that leverages self-attention and
self-distillation for this task. It works by detecting changes in Performance
Measurement data, a collection of time-varying metrics which reflect a set of
diverse measurements of the network performance at the cell level. Experimental
results show that our approach outperforms the state of the art by 4% on a
real-world based dataset consisting of about hundred thousands timeseries. It
also has the merits of being scalable and generalizable. This allows it to
provide deep insight into the specifics of mode of operation changes while
relying minimally on expert knowledge.

本研究提出一种基于自我监督学习框架的方法，利用自我关注和自我蒸馏技术检测 Performance Measurement 数据中的变化，达到 RAN 网络监测的目的，并在真实数据集上实现了 4% 的性能提升。

自监督变压器架构用于无线接入网络变化检测

Self-Supervised Transformer Architecture for Change Detection in Radio  Access Networks

Gradient-based methods for value estimation in reinforcement learning have
favorable stability properties, but they are typically much slower than
Temporal Difference (TD) learning methods. We study the root causes of this
slowness and show that Mean Square Bellman Error (MSBE) is an ill-conditioned
loss function in the sense that its Hessian has large condition-number. To
resolve the adverse effect of poor conditioning of MSBE on gradient based
methods, we propose a low complexity batch-free proximal method that
approximately follows the Gauss-Newton direction and is asymptotically robust
to parameterization. Our main algorithm, called RANS, is efficient in the sense
that it is significantly faster than the residual gradient methods while having
almost the same computational complexity, and is competitive with TD on the
classic problems that we tested.

本文研究了值估计的梯度方法在强化学习中速度较慢的原因，提出了一种基于 Gauss-Newton 方向的低复杂度非批量化近端方法，并介绍了主要算法 RANS 及其在经典问题中的表现。