A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine type communication (mMTC) applications. […]
Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
Source traffic prediction is one of the main challenges of enabling predictive resource allocation in machine-type communications (MTC). In this paper, a […]
Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications
Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this […]
Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
Ensuring an effective coexistence of conventional broadband cellular users with machine type communications (MTCs) is challenging due to the interference from MTCs […]