ML-Based Data Transmission Configuration for URLLC
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Solution Overview
Problem
Current wireless communication systems, particularly in ultra-reliable low-latency communication (URLLC) applications, face challenges in optimizing data transmission configurations to meet stringent reliability and latency requirements due to varying channel conditions, which existing multiple-access technologies struggle to address effectively.
Innovation Solution
The implementation of a machine-learning based algorithm that utilizes state indices to determine optimal data transmission configurations by mapping channel condition parameters to state indices, allowing base stations to predict and adjust transmission settings for improved reliability and latency through feedback reports from user equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multiple-access technologies are used for data transmission, then system implementation is straightforward, but the system cannot meet stringent reliability and latency requirements in URLLC applications due to varying channel conditions
Solution Approach 1:
The patent implements dynamic adaptation by using machine learning algorithms that continuously learn from channel condition feedback and adjust transmission configurations in real-time. The system transitions from static, pre-configured parameters to dynamic, learned configurations that adapt to varying channel conditions, thereby improving reliability without sacrificing adaptability.
Solution Approach 2:
The system changes transmission parameters (modulation schemes, coding rates, resource allocation) based on learned channel states. By mapping channel conditions to discrete states and using these states to select optimal parameter configurations, the system achieves both high reliability and adaptability to changing conditions.
2Reliability
If machine-learning based algorithms are implemented to optimize transmission configurations, then reliability and latency requirements are met, but system complexity increases
Solution Approach 1:
The patent segments the continuous channel condition space into discrete states, which simplifies the machine learning problem. By dividing the complex channel state into manageable discrete categories, the system reduces computational complexity while maintaining the ability to meet reliability requirements through state-based configuration selection.
Solution Approach 2:
The patent introduces state indices as an intermediary between channel conditions and transmission configurations. This intermediary layer simplifies the relationship between complex channel states and transmission parameters, making the system more manageable and reducing overall complexity while preserving reliability through the structured state-to-configuration mapping.
3Measurement precision
If detailed channel condition parameters are monitored and reported, then transmission optimization is improved, but feedback overhead and latency increase
Solution Approach 1:
The patent extracts only the essential features of channel conditions by mapping them to discrete state indices. Instead of reporting all detailed channel parameters, the system extracts the most relevant information into compact state representations, reducing feedback overhead and transmission time while maintaining sufficient precision for optimization.
Solution Approach 2:
The patent applies different levels of measurement precision to different aspects of channel conditions. By identifying which channel parameters are most critical for transmission optimization and focusing measurement efforts on those specific aspects, the system achieves adequate precision where needed while reducing overall feedback requirements and associated time losses.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for configuring data transmission. Aspects relate to determining a data transmission configuration utilizing a machine-learning based algorithm, such as a data transmission configuration for ultra-reliable low-latency communication (URLLC) applications. A method that may be performed by a base station (BS) includes receiving a feedback report from a user equipment (UE) including an indication of a first state corresponding to a plurality of channel condition parameters and determining one or more actions based, at least in part, on the first state. The BS may determining the one or more actions utilizing a machine learning algorithm that uses a second state, where the second state is based, at least in part, on the first state.