Neural Network Power Control Parameter Selection
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently controlling transmission power, leading to interference and high power consumption in mobile terminals, which affects transmission quality and battery life, and requires more tailored power control mechanisms, especially for sensitive services like ultra-reliable low-latency communications (URLLC).
Innovation Solution
A method using a neural network to determine optimal uplink power control parameters based on radio access network parameters, traffic types, and conditions, allowing for dynamic adjustment of power control settings to maximize network performance and minimize interference and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transmission power is increased to improve transmission quality, then transmission quality is improved, but power consumption increases and interference to other transmissions increases
Solution Approach 1:
The patent implements dynamic power control where the network element continuously monitors transmission conditions, channel quality, and service requirements to adjust power control parameters in real-time. This allows the system to use higher power only when necessary for maintaining transmission quality, rather than maintaining constantly high power levels, thus resolving the contradiction between transmission quality and power consumption.
Solution Approach 2:
The system changes power control parameters (such as power offset, pathloss compensation factor) based on detected transmission conditions and service types. By dynamically adjusting these parameters, the system optimizes the balance between transmission quality and power consumption, ensuring adequate quality only when required while minimizing overall power usage.
2Reliability
If transmission power is increased to improve transmission quality, then transmission quality is improved, but interference to other transmissions increases
Solution Approach 1:
The patent applies different power control strategies and parameter settings for different services and user equipment. By tailoring power control to local conditions and service requirements (such as prioritizing URLLC services), the system maintains transmission quality for critical services while minimizing interference generated by unnecessary high power transmissions in other contexts.
Solution Approach 2:
The system dynamically adjusts power levels based on real-time channel conditions, service requirements, and network load. This allows transmission quality to be maintained when necessary while reducing power and interference when conditions permit, resolving the contradiction between quality and interference.
3Reliability
If power control parameters are optimized for specific services like URLLC, then service reliability is improved, but system complexity increases
Solution Approach 1:
The system implements service-specific power control parameter optimization where different parameter sets are configured for different service types (e.g., URLLC, eMBB, mMTC). The network element detects the service type and applies appropriate parameters, improving service reliability without requiring complete redesign of the power control system for each service.
Solution Approach 2:
The patent segments the power control system into service-specific configurations while maintaining a unified control framework. This allows optimized parameters for critical services like URLLC to be implemented separately from general power control mechanisms, improving service reliability while managing system complexity through modular organization.
Data Source
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AI summary
As solution for selection of power control parameters is presented. The solution comprises determining (300), from a given set of radio access network parameters, a selection of radio access network parameters which have an effect on uplink power control, and training (302) a neural network to determine uplink power control parameters, utilising as an input the selection of radio access network parameter, and utilising (304) the trained neural network, with as an input the selection of radio access network parameters, obtain as an output a set of initial uplink power control parameters.