Common Value Function for Mobile Network Parameter Optimization
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Solution Overview
Problem
Current methods for optimizing control parameters in mobile communication networks, such as those using reinforcement learning, face challenges in efficiently adapting to varying conditions across different areas within a network, leading to suboptimal performance and increased complexity in managing multiple agents and value functions.
Innovation Solution
A parameter setting apparatus that employs reinforcement learning to optimize control parameters in mobile communication networks by using a common value function across multiple areas, allowing agents to select and execute optimization operations based on state variables and rewards, thereby updating the value function to improve network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple agents with individual value functions are used to optimize control parameters in different areas, then the optimization can be performed locally adapted to each area, but the device complexity and management complexity increase significantly
Solution Approach 1:
The patent combines multiple individual value functions into a single common value function that serves all agents across different areas. This merging approach maintains the ability to perform localized optimization while eliminating the complexity of managing multiple separate value functions, as the common value function is updated collectively based on rewards from all areas.
Solution Approach 2:
The common value function serves as a universal learning mechanism for all agents in different areas, replacing the need for area-specific individual value functions. This universal approach allows the system to maintain adaptability across diverse areas while simplifying the overall architecture through a single shared value function.
2Adaptability or versatility
If multiple individual value functions are maintained for different areas, then each area can be optimized independently, but the learning efficiency decreases due to redundant learning across agents
Solution Approach 1:
By merging the learning mechanisms into a single common value function, the system eliminates redundant learning that occurs when multiple agents independently maintain separate value functions. The common value function is updated based on rewards from all areas, allowing learning effects to be shared and propagated across the entire system, thereby improving overall learning efficiency.
Solution Approach 2:
The system implements a centralized feedback mechanism where rewards from all areas are collected and used to update the common value function. This feedback approach ensures that learning experiences from any area contribute to the overall knowledge base, enabling more efficient learning compared to independent learning processes.
3Device complexity
If a common value function is used across multiple areas, then learning efficiency is enhanced and complexity is reduced, but the ability to adapt to area-specific conditions may be compromised
Solution Approach 1:
The system maintains area-specific adaptation capabilities despite using a common value function by allowing each agent to observe local state variables and receive area-specific rewards. The common value function is updated based on these localized experiences, enabling the system to adapt to area-specific conditions while benefiting from shared learning across all areas.
Data Source
AI summary
A parameter setting apparatus includes a memory, and a processor that executes a procedure in the memory, the procedure including, selecting and executes one of a plurality of optimization operations to optimize a control parameter of a mobile communication network in accordance with a common value function, in response to a state variable in each of a plurality of different areas in the mobile communication network, the common value function determining an action value of each optimization operation responsive to the state variable of the mobile communication network, determining a reward responsive to the state variable in each of the plurality of areas, and performing reinforcement learning to update the common value function in response to the reward determined on each area.