Reinforcement Learning for Real-time Wireless Scheduling and Edge Caching: Theory and Algorithm Design

Project Details

Description

Machine Learning methods are used to solve radio resource management problems. In wireless communication networks, resource allocation decisions are often based on the solving optimization problems assuming a static network state and complete information about the current system state. The situation changes, if the objective of network management becomes to proactively take into account changes in network state when making resource allocation decisions. In this project, we address real-time scheduling, as well as edge and user caching decisions in situations with dynamically changing network state. The problems will be formulated as Markov Decision Processes, and reinforcement learning methods will be used to find solutions.
AcronymRILREW
StatusActive
Effective start/end date01/01/202231/12/2024

Collaborative partners

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure

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