Wireless Interference Identification and Power Optimization
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Solution Overview
Problem
Wireless communication systems face challenges in maximizing performance due to intra-frequency interference among neighbor cells sharing the same frequency band, and traditional solutions fail to adapt to changing environments effectively.
Innovation Solution
A device measures interference using a trained model to identify interfering cells and transmits messages to adjust scheduling, while a core device collects performance information to determine optimal transmission power parameters for all devices, using machine learning to maximize overall system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If frequency re-use is implemented to increase utilization efficiency of the available frequency band, then frequency band utilization efficiency is improved, but intra-frequency interference increases among different cells
Solution Approach 1:
The system implements interference measurement and feedback mechanisms where the first device measures interference on frequency resources and feeds back interference information to identify interfering devices. This feedback loop enables dynamic adjustment of scheduling decisions to mitigate intra-frequency interference while maintaining frequency re-use benefits
Solution Approach 2:
A trained model acts as an intermediary between interference measurement and scheduling decisions. The model processes interference data and scheduling information to identify interfering devices, enabling intelligent mediation between frequency re-use objectives and interference reduction goals
2Adaptability or versatility
If traditional interference reduction solutions are used, then interference management is simplified, but the system fails to adapt to changing environments effectively
Solution Approach 1:
The system transitions from static interference management to dynamic adaptation by continuously measuring interference in scheduling intervals and updating the trained model with new data. This enables the system to adapt to changing environmental conditions while managing complexity through structured measurement and modeling approaches
Solution Approach 2:
The system performs preliminary actions by training the model with historical interference data and scheduling information before deployment. This pre-training enables faster adaptation to new environments without requiring complex real-time computations, balancing adaptability with manageable system complexity
3Reliability
If transmission power is increased to improve signal quality, then signal quality is improved, but overall system performance deteriorates due to increased interference
Solution Approach 1:
The system changes transmission power parameters dynamically based on interference measurements and model predictions. Instead of fixed high power transmission, the system adjusts power levels according to identified interfering devices and scheduling decisions, maintaining signal quality while preventing overall system performance degradation
Solution Approach 2:
The system implements feedback mechanisms where performance information from multiple devices is collected and used to determine optimal transmission power parameters. This feedback loop ensures that power adjustments consider overall system performance, not just individual signal quality, preventing interference-induced performance degradation
Data Source
AI summary
Embodiments of the present disclosure relate to solutions for reducing interference and optimizing parameter. A first device measures interference on a frequency resource in a scheduling interval. If strength of the interference exceeds a threshold, the first device determines an interfering device by using a model trained with strength of previous interference and previous scheduling information of a plurality of candidate devices. In this way, the interfering device may be identified accurately and quickly and the interference may be reduced accordingly. In addition, a second device determines and transmits performance information to a third device. Then, the second device receives a parameter for adjusting transmission power from a third device. The parameter is determined based on respective performance information of a plurality of devices comprising the second device to maximum overall performance of the plurality of devices. In this way, the overall performance of the communication is improved.