Network Optimization and Automation
Multi-Objective RL for Adaptive 5G Network Self-Optimisation
Multi-objective reinforcement learning applied to 5G network self-optimisation, combining operations research methods with RL for adaptive network management.
- Type
- Industry research
- Focus
- 5G network self-optimisation
- Period
- 2022 to 2024
Research Area
5G networks introduce complex, dynamic resource allocation challenges that are difficult to solve with static rule-based approaches. Network self-optimisation, which enables networks to automatically adjust their own configuration in response to changing conditions, is an active area of research at the intersection of operations research and machine learning.
This collaboration investigated how multi-objective reinforcement learning can be applied to 5G network self-optimisation, framing network management decisions as a sequential decision-making problem with competing objectives. Operations research methods were used in conjunction with RL to address the multi-objective nature of the problem.
Research Output
The collaboration produced two peer-reviewed publications between 2022 and 2024, contributing to the research community's understanding of RL-based approaches to network self-optimisation in 5G environments.
Interested in RL for Network Optimisation?
Get in touch to discuss how reinforcement learning and operations research can address multi-objective network management challenges.
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