Thulitha Senevirathna

Thulitha Senevirathna, Vinh Hoa La, Samuel Marchal et al.

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are immensely popular in service layer applications and have been proposed as essential enablers in many aspects of 5G and beyond networks, from IoT devices and edge computing to cloud-based infrastructures. However, existing 5G ML-based security surveys tend to emphasize AI/ML model performance and accuracy more than the models' accountability and trustworthiness. In contrast, this paper explores the potential of Explainable AI (XAI) methods, which would allow stakeholders in 5G and beyond to inspect intelligent black-box systems used to secure next-generation networks. The goal of using XAI in the security domain of 5G and beyond is to allow the decision-making processes of ML-based security systems to be transparent and comprehensible to 5G and beyond stakeholders, making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as ORAN, zero-touch network management, and end-to-end slicing, this survey emphasizes the role of XAI in them that the general users would ultimately enjoy. Furthermore, we presented the lessons from recent efforts and future research directions on top of the currently conducted projects involving XAI.

Thulitha Senevirathna, Bathiya Thennakoon, Tharindu Sankalpa et al.

Source traffic prediction is one of the main challenges of enabling predictive resource allocation in machine type communications (MTC). In this paper, a Long Short-Term Memory (LSTM) based deep learning approach is proposed for event-driven source traffic prediction. The source traffic prediction problem can be formulated as a sequence generation task where the main focus is predicting the transmission states of machine-type devices (MTDs) based on their past transmission data. This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices. Knowledge of such a causal relationship can enable event-driven traffic prediction. The performance of the proposed approach is studied using data regarding events from MTDs with different ranges of entropy. Our model outperforms existing baseline solutions in saving resources and accuracy with a margin of around 9%. Reduction in Random Access (RA) requests by our model is also analyzed to demonstrate the low amount of signaling required as a result of our proposed LSTM based source traffic prediction approach.