The importance of cellular networks continuously increases as we assume ubiquitous connectivity in our daily lives. As a result, the underlying core telecom systems have very high reliability and availability requirements, that are sometimes hard to meet. This study presents a proactive approach that could aid satisfying these high requirements on reliability and availability by predicting future base station alarms. A data set containing 231 internal performance measures from cellular (4G) base stations is correlated with a data set containing base station alarms. Next, two experiments are used to investigate (i) the alarm prediction performance of six machine learning models, and (ii) how different predict-ahead times (ranging from 10 min to 48 hours) affect the predictive performance. A 10-fold cross validation evaluation approach and statistical analysis suggested that the Random Forest models showed best performance. Further, the results indicate the feasibility of predicting severe alarms one hour in advance with a precision of 0.812 (±0.022, 95 % CI), recall of 0.619 (±0.027) and F ₁ -score of 0.702 (±0.022). A model interpretation package, ELI5, was used to identify the most influential features in order to gain model insight. Overall, the results are promising and indicate the potential of an early-warning system that enables a proactive means for achieving high reliability and availability requirements.

Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, \(F_1\)-score and Jaccard index when compared to k-means clustering of the sessions.

The development of Quality of Experience (QoE) models using Machine Learning (ML) is challenging, since it can be difficult to share datasets between research entities to protect the intellectual property of the ML model and the confidentiality of user studies in compliance with data protection regulations such as General Data Protection Regulation (GDPR). This makes distributed machine learning techniques that do not necessitate sharing of data or attribute names appealing. One suitable use case in the scope of QoE can be the task of mapping QoE indicators for the perception of quality such as Mean Opinion Scores (MOS), in a distributed manner. In this article, we present Distributed Ensemble Learning (DEL), and Vertical Federated Learning (vFL) to address this context. Both approaches can be applied to datasets that have different feature sets, i.e., split features. The DEL approach is ML model-agnostic and achieves up to $12\%$ accuracy improvement of ensembling various generic and specific models. The vFL approach is based on neural networks and achieves on-par accuracy with a conventional Fully Centralized machine learning model, while exhibiting statistically significant performance that is superior to that of the Isolated local models with an average accuracy improvement of $26\%$. Moreover, energy-efficient vFL with reduced network footprint and training time is obtained by further tuning the model hyper-parameters.

With emerging ML techniques showing great potential in prediction performance, there is an inclination towards ML for network management. Although modern mobile network deployments have access to essential data storage and computational resources, they would benefit remarkably from more sustainable and cost-effective approaches for ML applications. Data-efficient AI allows network vendors to train ML models on less data, which relieves memory and computational requirements. To explore a solution to these problems, this paper dives deeper into SNN, a promising data-efficient ML technique. Furthermore, to improve data efficiency, an extension of the SNN framework is proposed by applying a memory-based continual learning algorithm, Experience Replay. Empirical evaluations are conducted using several datasets, including a dataset from real live mobile networks, to illustrate the potential of SNNs and to highlight the contribution to sustainable AI for mobile networks. The findings show that in this context, SNNs can outperform standard classification neural networks, under both the standard multi-class classification case and the continual learning case, while trained on significantly less data.

Distributed intelligence (DI) is becoming increasingly important in mobile networks due to privacy and regulatory restrictions on data movement. This trend is fueled by a surge in data volume required for training machine learning models, and increasing computational and storage capabilities of devices, and network nodes near data collection points. Techniques such as Federated and Split Learning, applied in horizontal and vertical feature spaces, enable efficient training of machine learning models while maintaining data privacy. Despite the adoption of DI techniques in mobile networks, there are still gaps and areas in mobile network architecture where DI is either not applied or not taking advantage of its full potential. To that end, this paper provides a state-of-the-art overview of the adoption of DI in mobile networks from the perspective of standardization. We identify the importance of machine learning model lifecycle management (ML LCM) and position DI as an extension of that. From this overview, gaps and limitations are identified and presented. To overcome these limitations, we perform a thought experiment which lowers ML LCM with DI capability from Core Network (CN) to Radio Access Networks (RAN) which are distributed by nature. In that setting a set of Key Performance Indicators (KPIs) to quantify the performance of different DI techniques is introduced. These KPIs aim at quantifying computational overhead, memory/storage requirements, communication footprint, model efficiency and robustness, flexibility and privacy. We evaluate these KPIs on a RAN use case, that of secondary carrier prediction and show the interplay between model efficiency and computational footprint which are important metrics to consider when migrating from a centralized to a distributed setting. Finally, we provide additional enhancements to improve these KPIs and thus further promote the adoption of DI techniques.