Huawei technologies co., ltd. (20240106508). ARTIFICIAL INTELLIGENCE-ENABLED LINK ADAPTATION simplified abstract
Contents
- 1 ARTIFICIAL INTELLIGENCE-ENABLED LINK ADAPTATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ARTIFICIAL INTELLIGENCE-ENABLED LINK ADAPTATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ARTIFICIAL INTELLIGENCE-ENABLED LINK ADAPTATION
Organization Name
Inventor(s)
ARTIFICIAL INTELLIGENCE-ENABLED LINK ADAPTATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240106508 titled 'ARTIFICIAL INTELLIGENCE-ENABLED LINK ADAPTATION
Simplified Explanation
The abstract of the patent application describes a method to reduce signaling resource overhead associated with communication link adaptation mechanisms by using machine learning to select modulation and coding schemes based on channel state information.
- Machine learning used to train a module at the first device with channel state information as input and modulation and coding scheme parameters as output.
- Trained machine learning modules at both devices allow for modulation and coding scheme selection without continuous feedback of channel state information.
Potential Applications
This technology can be applied in wireless communication systems, IoT devices, and network infrastructure to improve communication efficiency and reduce signaling overhead.
Problems Solved
1. Reducing signaling resource overhead associated with communication link adaptation mechanisms. 2. Improving scheduling performance by using machine learning for modulation and coding scheme selection.
Benefits
1. Enhanced communication efficiency. 2. Reduced feedback requirements for modulation and coding scheme selection. 3. Improved overall system performance.
Potential Commercial Applications
Optimized wireless communication systems, IoT devices with improved connectivity, and network infrastructure with enhanced efficiency can benefit from this technology.
Possible Prior Art
Prior art may include traditional communication link adaptation mechanisms that rely on continuous feedback of channel state information for modulation and coding scheme selection.
Unanswered Questions
How does this technology impact battery life in wireless devices?
The abstract does not mention the impact of this technology on battery life in wireless devices. It would be interesting to know if the reduction in signaling overhead leads to energy savings.
Are there any limitations to the use of machine learning in this context?
The abstract does not discuss any potential limitations or challenges associated with using machine learning for modulation and coding scheme selection. It would be important to understand any constraints or drawbacks of this approach.
Original Abstract Submitted
signaling resource overhead associated with current communication link adaptation mechanisms can be quite large and such mechanisms typically rely upon a channel state information (csi) feedback process that can result in poor scheduling performance. embodiments are disclosed in which a first device channel state information characterizing a wireless communication channel between the first device and a second device, and trains a machine learning (ml) module of the first device using the csi as an ml module input and one or more modulation and coding scheme (mcs) parameters as an ml module output to satisfy a training target. by applying the concepts disclosed herein, overhead associated with feedback for mcs selection may be reduced compared to conventional link adaptation procedures, because, once ml modules at a pair of devices have been trained, the mcs selection by the ml modules can be done without requiring the ongoing feedback of csi.
