5G Scheduler Parallel Grouping MU-MIMO
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently assigning multiple communications devices to a frequency band for simultaneous data transmission, leading to suboptimal operating parameters and reduced throughput.
Innovation Solution
A scheduler uses a heuristic algorithm and parallel computing techniques to generate and evaluate candidate groupings of devices for frequency resource allocation, selecting groups based on channel gains and precoding matrices to maximize communication rates through MU-MIMO technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple communications devices are assigned to a frequency band for simultaneous transmission, then the communication throughput increases, but the difficulty of resource allocation and interference management increases
Solution Approach 1:
The frequency band is segmented into multiple sub-bands or resource blocks, allowing multiple device groups to transmit simultaneously on different frequency segments. This segmentation enables parallel communications while managing interference through frequency separation, thus increasing throughput without overwhelming allocation complexity.
Solution Approach 2:
The system dynamically adjusts device groupings, frequency assignments, and transmission parameters based on real-time channel conditions, device priorities, and interference levels. This dynamic resource allocation optimizes throughput adaptively while managing complexity through algorithmic control rather than static configurations.
2Productivity
If advanced algorithms and parallel computing are used to optimize device grouping, then the frequency resource allocation efficiency improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The system pre-computes channel state information, device compatibility metrics, and potential grouping configurations before actual resource allocation. This preliminary processing organizes data in advance, reducing the computational burden during real-time allocation decisions and improving overall efficiency.
Solution Approach 2:
The system uses simplified models or approximations of complex channel conditions and device characteristics to generate candidate groupings quickly. These simplified representations allow efficient exploration of multiple allocation scenarios without requiring full computational complexity for each evaluation, balancing accuracy with processing requirements.
Data Source
AI summary
Apparatuses, systems, and techniques to select a group devices to utilize a frequency band. In at least one embodiment, a plurality of groupings are generated in parallel and one of the selected groupings is selected to utilize the frequency band.