Data driven optimization and machine learning based performance diagnostics
of radio access networks entails significant challenges arising not only from
the nature of underlying data sources but also due to complex spatio-temporal
relationships and interdependencies between cells due to user mobility and
varying traffic patterns. We discuss how to study these configuration and
performance management data sets and identify relationships between cells in
terms of key performance indicators using multivariate analysis. To this end,
we leverage a novel framework based on canonical correlation analysis (CCA),
which is a highly effective method for not only dimensionality reduction but
also for analyzing relationships across different sets of multivariate data. As
a case study, we discuss energy saving use-case based on cell shutdown in
commercial cellular networks, where we apply CCA to analyze the impact of
capacity cell shutdown on the KPIs of coverage cell in the same sector. Data
from LTE Network is used to analyzed example case. We conclude that CCA is a
viable approach for identifying key relationships not only between network
planning and configuration data, but also dynamic performance data, paving the
way for endeavors such as dimensionality reduction, performance analysis, and
root cause analysis for performance diagnostics.

该研究旨在基于机器学习和多元分析的诊断方法优化电信网络的性能，通过经典相关性分析法，识别网络中的关键指标及其内部联系，解决数据源的多样性带来的挑战，提高网络规划和配置数据的处理效率。

基于规范相关分析的无线接入网络性能分析框架

A canonical correlation-based framework for performance analysis of radio access networks

In this paper, we propose a framework for cross-layer optimization to ensure
ultra-high reliability and ultra-low latency in radio access networks, where
both transmission delay and queueing delay are considered. With short
transmission time, the blocklength of channel codes is finite, and the Shannon
Capacity cannot be used to characterize the maximal achievable rate with given
transmission error probability. With randomly arrived packets, some packets may
violate the queueing delay. Moreover, since the queueing delay is shorter than
the channel coherence time in typical scenarios, the required transmit power to
guarantee the queueing delay and transmission error probability will become
unbounded even with spatial diversity. To ensure the required
quality-of-service (QoS) with finite transmit power, a proactive packet
dropping mechanism is introduced. Then, the overall packet loss probability
includes transmission error probability, queueing delay violation probability,
and packet dropping probability. We optimize the packet dropping policy, power
allocation policy, and bandwidth allocation policy to minimize the transmit
power under the QoS constraint. The optimal solution is obtained, which depends
on both channel and queue state information. Simulation and numerical results
validate our analysis, and show that setting packet loss probabilities equal is
a near optimal solution.

本文提出一个跨层优化的框架，以确保在无线接入网络中实现极高的可靠性和极低的延迟，其中考虑了传输延迟和排队延迟等因素。我们引入了一种主动丢包机制，对数据包丢失率、功率分配和带宽分配进行优化，以在限制服务质量的前提下最小化发送功率。仿真和实验表明，设定丢包概率相等是一种近似最优的解决方案。

超可靠低时延无线接入网络的跨层优化

Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access  Networks

Recent works have validated the possibility of improving energy efficiency in
radio access networks (RANs), achieved by dynamically turning on/off some base
stations (BSs). In this paper, we extend the research over BS switching
operations, which should match up with traffic load variations. Instead of
depending on the dynamic traffic loads which are still quite challenging to
precisely forecast, we firstly formulate the traffic variations as a Markov
decision process. Afterwards, in order to foresightedly minimize the energy
consumption of RANs, we design a reinforcement learning framework based BS
switching operation scheme. Furthermore, to avoid the underlying curse of
dimensionality in reinforcement learning, a transfer actor-critic algorithm
(TACT), which utilizes the transferred learning expertise in historical periods
or neighboring regions, is proposed and provably converges. In the end, we
evaluate our proposed scheme by extensive simulations under various practical
configurations and show that the proposed TACT algorithm contributes to a
performance jumpstart and demonstrates the feasibility of significant energy
efficiency improvement at the expense of tolerable delay performance.

本文研究了如何通过基站开关操作来降低无线电接入网络的能量消耗。为此，我们根据马尔科夫决策过程对流量变化进行建模，并设计了一个基于强化学习的开关操作方案，其中利用了历史周期或邻近区域中的传递学习专业知识的转移演员 - 评论家算法。通过在各种实际配置下的广泛模拟评估，我们证明了所提出的算法可以显著提高能源效率，而延迟性能的牺牲是可以接受的。