18148601. CARRIER AGGREGATION OPTIMIZATION USING MACHINE LEARNING simplified abstract (QUALCOMM Incorporated)
Contents
- 1 CARRIER AGGREGATION OPTIMIZATION USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CARRIER AGGREGATION OPTIMIZATION USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Abstract Explanation
- 1.6 Bullet Points
- 1.7 Potential Applications
- 1.8 Problems Solved
- 1.9 Benefits
- 1.10 Original Abstract Submitted
CARRIER AGGREGATION OPTIMIZATION USING MACHINE LEARNING
Organization Name
Inventor(s)
Madhup Chandra of San Diego CA (US)
CARRIER AGGREGATION OPTIMIZATION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18148601 titled 'CARRIER AGGREGATION OPTIMIZATION USING MACHINE LEARNING
Simplified Explanation
Abstract Explanation
The patent application is about wireless communication and specifically focuses on an apparatus for a user equipment (UE) that uses a neural network to predict radio frequency channel conditions or user context. The apparatus determines a set of inputs for the neural network, which includes historical data related to the wireless environment, communication patterns, or behavior patterns of the UE. Using the neural network and the set of inputs, the apparatus determines the optimal number of aggregated carriers to maximize power or performance parameters. The UE then communicates using this optimal number of aggregated carriers.
Bullet Points
- The patent application is about an apparatus for a user equipment (UE) that uses a neural network to predict radio frequency channel conditions or user context.
- The apparatus determines a set of inputs for the neural network, including historical data related to the wireless environment, communication patterns, or behavior patterns of the UE.
- Using the neural network and the set of inputs, the apparatus determines the optimal number of aggregated carriers to maximize power or performance parameters.
- The UE then communicates using this optimal number of aggregated carriers.
Potential Applications
- This technology can be applied in wireless communication systems to improve the efficiency and performance of user equipment (UE).
- It can be used in various wireless devices such as smartphones, tablets, laptops, and IoT devices to optimize their communication capabilities.
- The technology can be implemented in cellular networks to enhance the overall network capacity and user experience.
Problems Solved
- The technology solves the problem of determining the optimal number of aggregated carriers for a UE to maximize power or performance parameters.
- It addresses the challenge of predicting radio frequency channel conditions and user context accurately.
- The technology helps in optimizing the communication capabilities of wireless devices in different environments and scenarios.
Benefits
- The use of a neural network allows for accurate prediction of radio frequency channel conditions and user context, leading to improved decision-making for the UE.
- By determining the optimal number of aggregated carriers, the technology maximizes power and performance parameters, resulting in enhanced communication efficiency.
- The implementation of this technology in wireless devices and cellular networks can lead to improved network capacity, user experience, and overall performance.
Original Abstract Submitted
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an apparatus of a user equipment (UE) may determine a set of inputs to a neural network configured to predict radio frequency channel conditions or a user context associated with the UE. In some aspects, the set of inputs includes historical data related to a wireless environment, a communication pattern, or a behavior pattern associated with the UE. The apparatus of the UE may determine, using the neural network and based at least in part on the set of inputs, an optimal number of aggregated carriers to maximize one or more of a power parameter or a performance parameter. The apparatus of the UE may communicate using the optimal number of aggregated carriers. Numerous other aspects are described.
