Radio Node Power Control Using ML User Distribution Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional wireless communications systems waste energy by maintaining high RF power levels regardless of user presence and distribution, leading to unnecessary energy consumption.
Innovation Solution
Implement a sensor network and machine learning service to detect user presence and distribution, optimizing transmit power levels for radio nodes based on actual user distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radio nodes maintain high RF power levels to ensure sufficient signal strength at coverage area edges, then signal coverage quality is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic power adjustment by continuously monitoring user distribution and sensory data, then adapting RF power levels in real-time. The system transitions from static high-power operation to dynamic power scaling, where power levels are adjusted based on current user presence and distribution patterns, resolving the contradiction between maintaining reliable coverage and reducing energy consumption.
Solution Approach 2:
The system changes the power parameter of radio nodes based on detected user distribution and sensory data. By modifying the RF power level parameter dynamically according to actual conditions, the system achieves both adequate signal coverage where users are present and reduced energy consumption when user density is low, directly addressing the technical contradiction.
2Area of stationary object
If radio nodes radiate high RF energy to cover the entire coverage area, then coverage area is improved, but energy waste increases
Solution Approach 1:
The patent applies local quality by adjusting RF power levels based on local user distribution conditions within the coverage area. Instead of uniform high-power transmission across the entire area, the system selectively increases power only in regions where users are detected and maintains or reduces power in areas with low or no user presence, thereby expanding effective coverage where needed while minimizing energy waste in unused areas.
Solution Approach 2:
The system uses sensor networks and machine learning services to autonomously detect user presence and distribution, then automatically adjusts radio node power levels without manual intervention. This self-service mechanism enables the system to optimize coverage area and reduce energy waste simultaneously by making real-time decisions based on actual usage patterns.
3Loss of energy
If sensor network and machine learning service are deployed to detect user presence and optimize power levels, then energy optimization is improved, but device complexity increases
Solution Approach 1:
The patent introduces sensor networks and machine learning services as intermediary components between user presence detection and radio node power control. These intermediaries process sensory data and generate optimization decisions, enabling sophisticated energy management while keeping the core radio node functionality relatively simple. The complexity is distributed across the sensor network, processing system, and control algorithm rather than concentrated in individual radio nodes.
Data Source
AI summary
Machine learning (ML) assisted energy saving optimization in a wireless communications system (WCS) is provided. The WCS includes multiple radio nodes (RNs) each configured to provide radio frequency (RF) coverage in a coverage area. In a conventional approach, each RN emits high RF power to maintain sufficient signal strength at a respective edge of the coverage area, regardless of whether users (stationary and mobile) are present and how users are distributed in the coverage area. To help reduce potential energy waste, the WCS is configured to utilize a sensor network and invoke an ML service to help detect user presence, determine user distribution, and optimize transmit power in the coverage area. As a result, it is possible to configure each RN to radiate an appropriate amount of RF energy based on actual user distribution in the coverage area, thus helping to reduce unnecessary energy waste in the WCS.