Dynamic Modulation Coding Adjustment for Intercell Interference
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Solution Overview
Problem
The increasing demand for data transmission in advanced communication networks leads to increased intercell interference, which affects network efficiency and user experience, particularly in 5G and future generations of wireless communication.
Innovation Solution
A method that uses network equipment with a processor to identify patterns in unsuccessful transmissions from user equipment, determine modulation coding adjustment recommendations based on this feedback, and convey these recommendations to network nodes to optimize future transmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple equipment transmit data substantially at the same time to meet explosive demand for mobility networks, then network traffic capacity increases, but intercell interference increases
Solution Approach 1:
The system performs preliminary actions by analyzing user equipment feedback and identifying patterns of unsuccessful transmissions before interference becomes severe. The network equipment proactively determines modulation coding adjustment recommendations and conveys them to network nodes in advance, preventing interference degradation rather than merely reacting to it.
Solution Approach 2:
The system implements a feedback mechanism where user equipment provides feedback information about transmission success/failure and signal quality. The network equipment analyzes this feedback to identify patterns of unsuccessful transmissions, uses machine learning to determine modulation coding adjustments, and conveys recommendations back to network nodes, creating a closed-loop control system that continuously optimizes transmission parameters.
2Reliability
If dynamic predictive modulation coding adjustment is implemented to mitigate intercell interference, then user equipment quality of service improves, but device complexity increases
Solution Approach 1:
The system enables self-service by implementing automated pattern recognition and modulation coding adjustment determination. The network equipment autonomously analyzes user feedback, identifies transmission patterns, applies machine learning algorithms to determine optimal adjustments, and conveys recommendations without requiring manual intervention, reducing operational complexity while maintaining high reliability.
Solution Approach 2:
The system replaces traditional mechanical/manual modulation coding adjustment mechanisms with automated electronic processing. Machine learning algorithms substitute for manual analysis and decision-making, automatically processing user feedback and determining optimal modulation coding adjustments, thereby reducing device complexity while improving service reliability.
Data Source
AI summary
Facilitating dynamic predictive modulation coding adjustment for intercell interference in advanced communication networks is provided herein. A method includes identifying a pattern associated with unsuccessful transmissions sent to the user equipment via a network node. The method also includes, based on the pattern, determining modulation coding adjustment recommendations for future transmissions to the user equipment via the network node. Further, the method includes facilitating a conveyance of the modulation coding adjustment recommendations to the network node.