5G Base Station Control Parameter Optimization via Deep Reinforcement Learning
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Solution Overview
Problem
The complexity of managing a vast number of control parameters in 5G base stations, particularly in geographically distributed setups, makes manual tuning insufficient for optimizing data flows across various IoT devices and service applications, leading to inefficiencies in data analytics pipelines.
Innovation Solution
A deep reinforcement-based machine learning model is used to dynamically determine and update control parameter values based on current network state data, optimizing spectral efficiency and performance at base stations within 5G and 6G telecommunication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tuning of control parameters is used in 5G base stations, then operational simplicity is maintained, but optimization performance deteriorates due to the vast number of parameters and geographic distribution
Solution Approach 1:
The system implements self-service through autonomous machine learning models that automatically tune control parameters without human intervention. The deep reinforcement learning model continuously learns from network state data and autonomously determines optimal parameter settings, enabling the base station to self-optimize its performance while handling the vast number of parameters and geographic distribution challenges.
Solution Approach 2:
The patent replaces manual mechanical tuning processes with automated computational systems. Machine learning algorithms and deep reinforcement learning models substitute human operators, automatically analyzing network conditions and adjusting control parameters through software-based decision-making, thereby resolving the contradiction between operational simplicity and optimization performance.
2Adaptability or versatility
If the number of control parameters is increased to accommodate various IoT devices and service applications, then system adaptability improves, but system complexity increases
Solution Approach 1:
The system dynamically changes control parameters based on network conditions and service requirements. The machine learning model continuously adjusts parameter values rather than maintaining fixed settings, allowing the system to adapt to various IoT devices and service applications while managing complexity through intelligent, condition-based parameter selection rather than maintaining all parameters simultaneously.
Solution Approach 2:
The patent introduces dynamics into parameter management by implementing real-time or near-real-time updates of control parameters based on current network state. The system transitions from static parameter configurations to dynamic adjustment mechanisms, where parameters are continuously optimized according to changing conditions, enabling versatility without proportionally increasing management complexity.
3Productivity
If real-time updates of control parameters are implemented, then performance optimization improves, but computational complexity and processing requirements increase
Solution Approach 1:
The system implements periodic action by updating control parameters at specific time intervals (real-time or near-real-time) rather than continuously. The machine learning model processes network state data periodically and adjusts parameters at these discrete moments, achieving performance optimization while managing computational complexity through time-based segmentation of processing tasks.
Data Source
AI summary
Systems and methods are provided for determining a set of control parameter data associated with a base station of a 5G multi-access edge computing and core network. In particular, the disclosed technology is directed to use a deep reinforcement-based learning (DRL) model to iteratively reinforce and improve the set of control parameter data at the base station. The DRL model determines the set of control parameter data as action based on a current set of network state data as state, according a set of target conditions used as rewards. A DRL server periodically receives network state data from the base station through a radio access network intelligent controller (RIC). Given the network state data, the DRL model determines control parameter data as output. The DRL server transmits the control parameter data to the base station via RIC. The periodic reinforcement-based learning dynamically improves a network performance of the base station.