17925183. USER DETECTION TECHNIQUE, AND METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN WIRELESS COMMUNICATION SYSTEM SUPPORTING MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
- 1 USER DETECTION TECHNIQUE, AND METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN WIRELESS COMMUNICATION SYSTEM SUPPORTING MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USER DETECTION TECHNIQUE, AND METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN WIRELESS COMMUNICATION SYSTEM SUPPORTING MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Original Abstract Submitted
USER DETECTION TECHNIQUE, AND METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN WIRELESS COMMUNICATION SYSTEM SUPPORTING MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT
Organization Name
Inventor(s)
USER DETECTION TECHNIQUE, AND METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN WIRELESS COMMUNICATION SYSTEM SUPPORTING MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT - A simplified explanation of the abstract
This abstract first appeared for US patent application 17925183 titled 'USER DETECTION TECHNIQUE, AND METHOD AND APPARATUS FOR CHANNEL ESTIMATION IN WIRELESS COMMUNICATION SYSTEM SUPPORTING MASSIVE MULTIPLE-INPUT MULTIPLE-OUTPUT
Simplified Explanation
The patent application describes a technique for user detection and channel estimation in a wireless communication system that supports massive multiple-input multiple-output (MIMO).
- The method involves receiving a superimposed signal that includes transmission signals from multiple user equipments (UEs), each with a pilot signal.
- A sample covariance matrix is calculated from the received signal using the number of antennas of a base station and the pilot signal matrix of the UEs.
- A likelihood function is then calculated based on the number of antennas and the received signal, indicating the probability of the received signal.
- A user index set is detected using the calculated likelihood function and sample covariance matrix, indicating which UEs have transmitted signals.
- Finally, channel estimation is performed for the UEs that are transmitting signals based on the detected user index set.
Potential applications of this technology:
- Wireless communication systems that support massive MIMO, such as 5G networks.
- Cellular networks where multiple UEs are transmitting signals simultaneously.
- Internet of Things (IoT) networks with a large number of connected devices.
Problems solved by this technology:
- Efficiently detecting and estimating the channels of multiple UEs in a wireless communication system.
- Handling the challenges of superimposed signals and interference in massive MIMO systems.
- Improving the overall performance and capacity of wireless networks.
Benefits of this technology:
- Improved user detection and channel estimation accuracy.
- Enhanced capacity and throughput in wireless communication systems.
- Better utilization of available spectrum and resources.
- Increased reliability and quality of service for users.
Original Abstract Submitted
Disclosed are a user detection technique, and a method and apparatus for channel estimation in a wireless communication system supporting massive multiple-input multiple-output. The method comprises the steps of: receiving a superimposed signal including a transmission signal of at least one user equipment (UE) from among a plurality of user equipments, wherein each transmission signal includes a pilot signal of a corresponding user equipment; calculating a sample covariance matrix from the received superimposed signal by using the number of antennas of a base station and a pilot signal matrix of the at least one user equipment; calculating a likelihood function indicating the likelihood probability of the received superimposed signal, on the basis of the number of antennas of the base station and the received superimposed signal; detecting a user index set indicating whether or not the plurality of user equipments have transmitted signals, by using the calculated likelihood function and sample covariance matrix; and performing channel estimation of the at least one user equipment that is transmitting the signal, on the basis of the detected user index set.
