18373500. CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY simplified abstract (Huawei Technologies Co., Ltd.)
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 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 18373500 titled 'CODEBOOK-BASED BEAMFORMING WITH RANDOM FOREST ON CONTENT ADDRESSABLE MEMORY
Simplified Explanation
The abstract describes a patent application for a codebook-based beamforming architecture that utilizes a random forest tree classification machine learning approach to select optimal codewords without the need for channel sensing. A content addressable memory (CAM) is used to implement the codebook matrix, and a photonics-based CAM is employed for high bandwidth and low power consumption.
- Codebook-based beamforming architecture with random forest tree classification
- Utilizes CAM for implementing codebook matrix
- Photonics-based CAM for high bandwidth and low power consumption
Potential Applications
The technology can be applied in:
- Wireless communication systems
- Radar systems
- Satellite communication systems
Problems Solved
- Eliminates the need for channel sensing in beamforming design
- Enables selection of near-optimal codewords with uniform delay
Benefits
- Improved efficiency in beamforming architecture design
- Enhanced performance in communication systems
- Reduced power consumption
Potential Commercial Applications
- Telecommunication companies
- Defense industry
- Satellite communication providers
Possible Prior Art
There may be prior art related to:
- Machine learning-based beamforming techniques
- Codebook optimization in communication systems
Unanswered Questions
How does the random forest tree classification improve beamforming efficiency?
The random forest tree classification machine learning approach helps in selecting near-optimal codewords without the need for channel sensing, thus streamlining the beamforming process.
What advantages does the photonics-based CAM offer over traditional implementations?
The photonics-based CAM provides high bandwidth and low power consumption, making it an efficient choice for implementing the codebook matrix in the 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.
