Lei Jiao

TL;DR: A data-driven architecture for enhancing personalized QoE is proposed for 5G networks and specifically proposes a two-stepQoE modeling approach to capture the strength of the relationship between users and services.

TL;DR: A novel energy-efficientDeep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication is proposed.

TL;DR: This paper introduces a text categorization approach that leverages the recently introduced Tsetlin Machine to address this accuracy-interpretability challenge, and shows its capacity for producing human-interpretable rules, while at the same time achieving acceptable accuracy.

TL;DR: Numerical results show that channel bonding/aggregation does not increase achieved system capacity and it leads to higher blocking probability, but lower forced termination probability is obtained when this technique is used.

TL;DR: Numerical results demonstrate that the integration of queues can further increase the capacity of the secondary network and spectrum utilization while decreasing blocking probability and forced termination probability.