International business machines corporation (20240129887). DYNAMIC MASSIVE MIMO END DEVICE PAIRING BASED ON PREDICTED AND REAL TIME CONNECTION STATE simplified abstract
Contents
- 1 DYNAMIC MASSIVE MIMO END DEVICE PAIRING BASED ON PREDICTED AND REAL TIME CONNECTION STATE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DYNAMIC MASSIVE MIMO END DEVICE PAIRING BASED ON PREDICTED AND REAL TIME CONNECTION STATE - 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
DYNAMIC MASSIVE MIMO END DEVICE PAIRING BASED ON PREDICTED AND REAL TIME CONNECTION STATE
Organization Name
international business machines corporation
Inventor(s)
Saurabh Agrawal of Bangalore (IN)
Dinesh C. Verma of New Castle NY (US)
Mathews Thomas of Flower Mound TX (US)
Sagar Tayal of Ambala City (IN)
DYNAMIC MASSIVE MIMO END DEVICE PAIRING BASED ON PREDICTED AND REAL TIME CONNECTION STATE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240129887 titled 'DYNAMIC MASSIVE MIMO END DEVICE PAIRING BASED ON PREDICTED AND REAL TIME CONNECTION STATE
Simplified Explanation
The abstract describes a computer-implemented method for grouping devices in a massive multiple-input and multiple-output (MIMO) based cellular network. The method involves determining movement states of end devices, estimating payload requirements, and grouping the end devices based on movement states and payload requirements.
- Determining movement states of end devices in a cell of the massive MIMO-based cellular network.
- Estimating payload requirements of the end devices.
- Grouping the end devices in a group based on the determined movement states and estimated payload requirements.
Potential Applications
This technology could be applied in the optimization of resource allocation in massive MIMO-based cellular networks, improving overall network efficiency and performance.
Problems Solved
1. Efficient grouping of devices based on movement states and payload requirements. 2. Enhanced network performance through optimized resource allocation.
Benefits
1. Improved network efficiency. 2. Enhanced user experience. 3. Better utilization of network resources.
Potential Commercial Applications
Optimizing resource allocation in cellular networks for improved performance and user experience.
Possible Prior Art
Prior art in the field of cellular network optimization and resource allocation algorithms may exist, but specific examples are not provided in this abstract.
Unanswered Questions
How does this method handle dynamic changes in movement states and payload requirements of end devices?
The abstract does not specify how the method adapts to real-time changes in movement states and payload requirements of devices within the network.
What impact does grouping devices based on movement states and payload requirements have on overall network latency?
The abstract does not address the potential impact of grouping devices on network latency and whether it is optimized for low latency communication.
Original Abstract Submitted
a computer-implemented method for grouping devices in a massive multiple-input and multiple-output (mimo)-based cellular network, in accordance with one embodiment, includes determining movement states of end devices in a cell of the massive mimo-based cellular network, estimating payload requirements of the end devices, and grouping the end devices in a group based on the determined movement states and the estimated payload requirements.
