Huawei technologies co., ltd. (20240137083). CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY simplified abstract
Contents
- 1 CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
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 20240137083 titled 'CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY
Simplified Explanation
The codebook-based beamforming architecture described in the patent application utilizes a random forest tree classification machine learning approach to select an optimal codeword without the need for channel sensing. This approach involves representing the random forest tree as a matrix and using a content addressable memory (CAM) to implement the codebook matrix-based beamforming architecture. A photonics-based CAM is also employed to take advantage of the high bandwidth and low power consumption of optical components.
- Random forest tree classification machine learning approach
- Codebook matrix-based beamforming architecture
- Content addressable memory (CAM) implementation
- Photonics-based CAM utilization
Potential Applications
The technology can be applied in wireless communication systems, radar systems, and satellite communication systems.
Problems Solved
Eliminates the need for channel sensing in beamforming architectures, reduces delay in selecting optimal codewords, and improves overall system efficiency.
Benefits
Enhanced performance in beamforming, increased system reliability, and reduced power consumption.
Potential Commercial Applications
Potential commercial applications include 5G networks, IoT devices, and smart city infrastructure.
Possible Prior Art
Prior art may include traditional beamforming techniques, channel sensing methods, and codebook-based beamforming architectures without machine learning components.
Unanswered Questions
How does the random forest tree classification improve beamforming performance compared to traditional methods?
The patent application does not provide a detailed comparison between the random forest tree classification approach and traditional beamforming techniques.
What specific parameters are used to train the random forest tree in the classification process?
The patent application does not specify the exact parameters or training process for the random forest tree classification machine learning approach.
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.
