Optimization Strategies for Mobile Network Performance
Summary
Mobile network optimisation encompasses a suite of techniques designed to enhance coverage, capacity, energy efficiency and user satisfaction across evolving cellular infrastructures. Traditional planning methods, centred on antenna downtilt, transmit power and frequency assignment, have given way to data-driven frameworks that integrate real-time measurement and analytics. Machine learning algorithms—including reinforcement learning, fuzzy neural networks and deep Q-learning—enable self-organising networks to adapt autonomously to fluctuating traffic demands and interference patterns. Digital twin models increasingly serve as virtual replicas of live networks, allowing safe exploration of configuration changes via Monte Carlo Tree Search and other sequential decision-making tools. Quality of Experience-driven approaches at the Radio Link Control layer shift the optimisation objective from network-centric metrics towards end-user perception, balancing throughput and latency for video streaming and file transfer services. Combined with advances in edge computing and network slicing, these optimisation strategies support the stringent performance requirements of 5G and lay the groundwork for future 6G deployments.
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Optimization Strategies for Mobile Network Performance publication trend
The graph below shows the total number of articles in optimization strategies for mobile network performance across all publications each year (not limited to Nature Index journals).
Technical terms
Digital twin: A virtual representation of the physical network used to simulate and evaluate optimisation actions safely.
Monte Carlo Tree Search: An algorithm that explores decision trees by random sampling to identify high-value configuration strategies.
Self-Organizing Network (SON): An architecture that automates parameter configuration and network optimisation to reduce operational complexity.
Quality of Experience (QoE): A measure of user satisfaction reflecting perceived service quality rather than purely technical metrics.
Radio Link Control (RLC) layer: A protocol layer responsible for reliable data delivery and flow control in mobile communication systems.
References
- Safe Online Mobile Network Optimization Through Digital Twin-Enhanced Monte Carlo Tree Search. IEEE Transactions on Cognitive Communications and Networking (2025).
- Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning. EURASIP Journal on Wireless Communications and Networking (2014).
- Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learning. EURASIP Journal on Wireless Communications and Networking (2020).
- A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets. Radioengineering (2016).
- QoE Optimization in a Live Cellular Network through RLC Parameter Tuning. Sensors (2021).
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