Browsing by Author "Ralha, Celia G."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Abnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Cellular NetworksDe Almeida, J. M.; Pontes, C. F. T.; DaSilva, Luiz A.; Both, C. B.; Gondim, J. J. C.; Ralha, Celia G.; Marotta, M. A. (IEEE, 2021-01-01)Abnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.
- AI-driven F-RANs: Integrating Decision Making Considering Different Time GranularitiesDeAlmeida, Jonathan M.; DaSilva, Luiz A.; Both, Cristiano Bonato; Ralha, Celia G.; Marotta, Marcelo A. (IEEE, 2021-06-07)Cloud and fog-based networks are promising paradigms for vehicular and mobile networks. Fog Radio Access Networks (F-RANs), in particular, can offload computation tasks to the network edge and reduce the latency. Artificial Intelligence (AI) techniques can be used in F-RANs to achieve, for example, enhanced energy efficiency and increased throughput. Nonetheless, the appropriate technique selection must consider the different time granularities at which decision-making occurs in F-RANs. We discuss the benefits and challenges of implementing an AI-driven F-RAN considering different timescales, highlighting key Machine Learning (ML) techniques for each granularity. Finally, we discuss the challenges and opportunities to integrate different ML solutions in F-RANs.