Samsung electronics co., ltd. (20240195662). DEVICE AND METHOD FOR CHANNEL ESTIMATION USING SHORT/LONG-TERM MEMORY NETWORK IN MILLIMETER-WAVE COMMUNICATION SYSTEM simplified abstract
Contents
- 1 DEVICE AND METHOD FOR CHANNEL ESTIMATION USING SHORT/LONG-TERM MEMORY NETWORK IN MILLIMETER-WAVE COMMUNICATION SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEVICE AND METHOD FOR CHANNEL ESTIMATION USING SHORT/LONG-TERM MEMORY NETWORK IN MILLIMETER-WAVE COMMUNICATION SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Channel Estimation Technology
- 1.13 Original Abstract Submitted
DEVICE AND METHOD FOR CHANNEL ESTIMATION USING SHORT/LONG-TERM MEMORY NETWORK IN MILLIMETER-WAVE COMMUNICATION SYSTEM
Organization Name
Inventor(s)
Kyeongyeon Kim of Suwon-si (KR)
DEVICE AND METHOD FOR CHANNEL ESTIMATION USING SHORT/LONG-TERM MEMORY NETWORK IN MILLIMETER-WAVE COMMUNICATION SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240195662 titled 'DEVICE AND METHOD FOR CHANNEL ESTIMATION USING SHORT/LONG-TERM MEMORY NETWORK IN MILLIMETER-WAVE COMMUNICATION SYSTEM
Simplified Explanation
This patent application describes a method and device for channel estimation in millimeter-wave communication systems using a short/long-term memory network. The method involves inputting a received pilot signal to the network, extracting a time-varying channel feature vector, estimating a channel model parameter, and ultimately estimating the channel for the received pilot signal.
- Inputting received pilot signal to a short/long-term memory network
- Extracting a time-varying channel feature vector
- Estimating a channel model parameter using a fully connected network
- Estimating the channel for the received pilot signal using the channel model parameter
Key Features and Innovation
- Utilizes a short/long-term memory network for channel estimation
- Extracts time-varying channel features for accurate estimation
- Incorporates a fully connected network for parameter estimation
- Provides an efficient method for channel estimation in mmwave communication systems
Potential Applications
This technology can be applied in:
- 5G and beyond wireless communication systems
- Internet of Things (IoT) devices
- Autonomous vehicles
- Smart city infrastructure
Problems Solved
- Improves accuracy of channel estimation in millimeter-wave communication systems
- Enhances reliability of wireless communication in high-frequency bands
- Enables better performance of IoT devices and autonomous systems
Benefits
- Increased data transmission efficiency
- Enhanced signal reliability in mmwave communication
- Improved performance of wireless networks in challenging environments
Commercial Applications
Title: Advanced Channel Estimation Technology for 5G and Beyond Communication Systems This technology can be utilized in:
- Telecommunication companies for improving network performance
- IoT device manufacturers for enhancing connectivity
- Automotive industry for enabling reliable communication in autonomous vehicles
Prior Art
There are existing methods for channel estimation in wireless communication systems using neural networks and machine learning algorithms. Researchers have explored the use of deep learning for channel estimation in various frequency bands.
Frequently Updated Research
Ongoing research focuses on optimizing the network architecture for more efficient channel estimation in mmwave communication systems. Researchers are also investigating the integration of artificial intelligence for real-time adaptation to changing channel conditions.
Questions about Channel Estimation Technology
What are the key advantages of using a short/long-term memory network for channel estimation?
Using a short/long-term memory network allows for capturing both short-term variations and long-term trends in the channel, leading to more accurate estimation results.
How does the fully connected network contribute to the channel estimation process?
The fully connected network is responsible for estimating the parameters of the channel model, which are crucial for accurately predicting the channel characteristics.
Original Abstract Submitted
a device and a method for channel estimation using a short/long-term memory network in a millimeter-wave (mmwave) communication system are provided. the channel estimation method includes the operations of inputting a received pilot signal of a time slot to a long short-term memory network, extracting a time-varying channel feature embedding vector by estimating a change state of a channel by using the received pilot signal of the time slot as an input in the long short-term memory network, estimating a parameter of a channel model by using the time-varying channel feature embedding vector as an input in a fully connected network, and estimating a channel for the received pilot signal of the time slot, using the parameter of the channel model.
