COMM

The 5th Generation (5G) mobile networks support a wide range of services that impose diverse and stringent QoS requirements. This will be further exacerbated with the evolution towards 6th Generation mobile networks. Inevitably, 5G and beyond mobile ...

Managing service provider infrastructures (SPI) is ever more challenging with increasing scale and complexity. Network and container orchestration systems alleviate some manual tasks, but they are generally narrow solutions, with controllers for ...

In this paper, we propose FedRAN, a mobile edge, federated learning system that incorporates differential privacy to improve the privacy integrity of sensitive edge information, preventing adversarial entities from exploiting the network interactions ...

Increasing complexity in management of immense number of network elements and their dynamically changing environment necessitates machine learning based recommendation models to guide human experts in setting appropriate network configurations to ...

We present an approach that uses Reinforcement Learning (RL) with the Random Neural Network (RNN) acting as an adaptive critic, to route traffic in a SDN network, so as to minimize a composite Goal function that includes both packet delay and energy ...

Time-Sensitive Networking (TSN) is designed for real-time applications, usually pertaining to a set of Time-Triggered (TT) data flows. TT traffic generally requires low packet loss and guaranteed upper bounds on end-to-end delay. To guarantee the end-to-...

Network traffic classification (NTC) has attracted considerable attention in recent years. The importance of traffic classification stems from the fact that data traffic in modern networks is extremely complex and ever-evolving in different aspects, ...

Rapid change in sensitive behaviour and profile of distributed mobile network elements necessitates privacy preserving distributed learning mechanism such as Federated Learning. Moreover, this mechanism needs to be robust that seamlessly sustains the ...

The capacity in a communication network is restricted by the famous Shannon-Hartley theorem, which establishes a relationship between maximum achievable capacity, channel bandwidth, and signal-to-noise ratio of the channel. The state-of-the-art in ...