AI-Driven Network Configuration for Wireless Terminals
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Solution Overview
Problem
Current network configuration methods are inadequate in efficiently matching radio resource usage with diverse application demands due to a lack of detailed knowledge about traffic flows, leading to suboptimal radio resource allocation and energy inefficiency.
Innovation Solution
A method that employs AI-driven network configuration, where a determination application, potentially using machine learning algorithms, analyzes historical data and application-specific requirements to select optimal network configurations for terminals, minimizing radio resources and energy consumption by dynamically controlling network settings based on data transfer needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network configuration methods are used, then network coverage and basic connectivity are maintained, but radio resource allocation is suboptimal and energy inefficiency occurs due to lack of detailed traffic flow knowledge
Solution Approach 1:
The system performs preliminary analysis of traffic flow characteristics and application requirements before configuring network parameters. Historical data is analyzed in advance to predict future traffic patterns, allowing the network to pre-configure optimal resource allocation settings rather than reacting to current conditions, thereby improving allocation efficiency and reducing energy waste.
Solution Approach 2:
The system continuously monitors actual traffic flow patterns and compares them with predicted patterns, using this feedback to refine the determination application's predictions and adjust network configurations dynamically. This closed-loop approach enables progressive optimization of radio resource allocation based on real-world performance data, addressing the inefficiencies of traditional static configuration methods.
2Productivity
If detailed analysis of traffic flows is performed, then network configuration optimization is achieved, but system complexity increases due to the need for AI-driven determination applications
Solution Approach 1:
The determination application serves as an intermediary layer between raw traffic flow data and network configuration decisions. It encapsulates the complex AI analysis logic in a separate, dedicated component that processes historical data, predicts traffic patterns, and recommends optimizations. This modular approach allows detailed analysis to be performed centrally without proportionally increasing the complexity of individual network nodes.
Solution Approach 2:
Instead of implementing complex analysis capabilities in every network element, the system creates a centralized determination application that can be replicated or instantiated as needed. This allows the same optimized configuration logic to be applied across multiple network nodes without duplicating the underlying complexity, effectively distributing optimization benefits while maintaining manageable system architecture.
3Productivity
If network configurations are dynamically adjusted based on application demands, then spectrum efficiency increases, but configuration complexity and signaling overhead increase
Solution Approach 1:
The system optimizes spectrum efficiency by dynamically adjusting key network parameters such as resource block allocations, modulation and coding schemes, and transmission power levels based on predicted traffic patterns. Rather than fundamentally redesigning network architecture, the approach modifies existing parameters within defined ranges, achieving efficiency improvements while limiting configuration complexity to manageable parameter adjustments rather than structural changes.
Data Source
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AI summary
A method in a wireless communication network, the wireless communication network comprising a base station and a terminal associated with a device application, the method comprising: the base station providing a plurality of possible network configurations to a determination application, each possible network configuration associated with transfer of device application data between the terminal and the base station; the determination application determining at least one preferred network configuration from the plurality of possible network configurations and reporting the at least one preferred network configuration to the base station; and the base station selecting a network configuration from the at least one preferred network configuration and transmitting the selected network configuration to the terminal.