Huawei Technologies Co., Ltd. (20240235629). CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY simplified abstract
Contents
CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY
Organization Name
Inventor(s)
Armaghan Eshaghi of Kanata (CA)
CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240235629 titled 'CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY
The abstract describes a patent application for a codebook-based beamforming architecture that utilizes a random forest tree classification machine learning approach to eliminate the need for a channel sensing step in the design process. The random forest tree is represented by a matrix, and a content addressable memory (CAM) is used to implement the codebook matrix-based beamforming architecture, allowing for the selection of near-optimal codewords with uniform delay. A photonics-based CAM can be employed to take advantage of the high bandwidth and low power consumption of optical components.
- Codebook-based beamforming architecture
- Utilizes random forest tree classification machine learning approach
- Eliminates the need for channel sensing step
- CAM used to implement codebook matrix-based beamforming architecture
- Photonics-based CAM for high bandwidth and low power consumption
Potential Applications: - Wireless communication systems - Radar systems - Satellite communication
Problems Solved: - Eliminates the need for channel sensing - Allows for near-optimal codeword selection - Reduces delay in beamforming design
Benefits: - Improved efficiency in beamforming design - Enhanced performance in communication systems - Reduced power consumption
Commercial Applications: Title: Advanced Beamforming Technology for Communication Systems This technology can be applied in various commercial sectors such as telecommunications, defense, and satellite communications. It can improve the efficiency and performance of communication systems, leading to better signal quality and reduced power consumption.
Questions about Beamforming Technology: 1. How does the random forest tree classification approach improve beamforming design? The random forest tree classification approach helps in selecting near-optimal codewords without the need for a channel sensing step, leading to more efficient beamforming design.
2. What are the advantages of using a photonics-based CAM in implementing the codebook matrix-based beamforming architecture? A photonics-based CAM offers high bandwidth and low power consumption, making it ideal for implementing the codebook matrix-based beamforming architecture.
Original Abstract Submitted
a codebook-based beamforming architecture utilizes a random forest tree classification machine learning approach in order to circumvent the channel sensing step when designing a beamforming architecture. the random forest tree may be represented by a matrix. a content addressable memory (cam) may be used to implement the codebook matrix based beamforming architecture using a random forest classifier allowing a near optimal codeword to be selected with a uniform delay. a photonics-based cam may be used to exploit the high bandwidth and the low power consumption of optical components.
