17816297. COMPOSITE BEAM GENERATION USING MACHINE LEARNING simplified abstract (Samsung Electronics Co., Ltd.)

COMPOSITE BEAM GENERATION USING MACHINE LEARNING

Organization Name

Samsung Electronics Co., Ltd.

Inventor(s)

Jianhua Mo of Allen TX (US)

Boon Loong Ng of Plano TX (US)

Vutha Va of Plano TX (US)

Ahmad Alammouri of Garland TX (US)

COMPOSITE BEAM GENERATION USING MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17816297 titled 'COMPOSITE BEAM GENERATION USING MACHINE LEARNING

Simplified Explanation

The patent application describes a method for generating composite beams using machine learning. This involves identifying a narrow beam for transmission and then creating a composite beam that includes the narrow beam. The method also includes determining beamforming weights for transmitting the composite beam, which are determined using machine learning algorithms.

• Identifying a narrow beam for transmission
• Creating a composite beam that includes the narrow beam
• Determining beamforming weights for transmitting the composite beam using machine learning algorithms
• Generating a composite beam codebook that includes information about sets of beamforming weights for different composite beam indexes
• Updating the parameters of the machine learning algorithm separately for different coverage regions of the composite beams

Potential applications of this technology:

• Wireless communication systems
• Cellular networks
• Satellite communication systems
• Radar systems

Problems solved by this technology:

• Efficient generation of composite beams for transmission
• Improved signal quality and coverage
• Adaptability to different coverage regions

Benefits of this technology:

• Enhanced communication performance
• Increased data rates
• Improved network capacity
• Reduced interference

Original Abstract Submitted

Methods, apparatuses, systems, and computer-readable media for a composite beam generation using machine learning. A method includes identifying a narrow beam for a transmission, identifying a composite beam including the narrow beam based on an association between one or more composite beams and one or more narrow beams, identifying one or more beamforming weights for transmitting the composite beam, and transmitting the composite beam using the one or more beamforming weights. The one or more beamforming weights are determined based on machine learning. In some embodiments, the method includes generating a composite beam codebook including information indicating sets of one or more beamforming weights corresponding to composite beam indexes, respectively. The sets of one or more beamforming weights are determined using respective parameters of a machine learning algorithm that are separately updated for coverage regions of composite beams corresponding to the composite beam indexes, respectively.