Dynamic Cell Assignment via Neural Network Load Allocation
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Solution Overview
Problem
Existing carrier aggregation systems in 5G and 6G networks statically assign primary and secondary cells, leading to inefficient data transmission and throughput disruptions due to suboptimal frequency allocation, often using precious low-band spectrum when mid-band or high-band spectrum could be more efficient.
Innovation Solution
A method and system that determine radio condition metrics and congestion metrics for cells in a wireless network, using a neural network and deep learning module to dynamically allocate data loads between primary and secondary cells, optimizing data transmission based on real-time traffic and radio conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static cell assignment is used, then system complexity is reduced, but network throughput deteriorates due to suboptimal frequency allocation
Solution Approach 1:
The patent implements dynamic cell assignment where the network component continuously monitors RF conditions and traffic load metrics, then adjusts primary and secondary cell assignments in real-time based on current network state, transitioning from static to dynamic configuration to optimize throughput
Solution Approach 2:
The system employs feedback mechanisms by monitoring RF condition metrics and traffic load continuously, using this information to dynamically adjust cell assignments, creating a closed-loop control system that adapts to changing network conditions
2Area of stationary object
If low-band spectrum is used for data transmission, then coverage area is improved, but spectrum efficiency deteriorates compared to mid-band or high-band spectrum
Solution Approach 1:
The system dynamically changes frequency band parameters by selecting which cells (operating in different bands) serve as primary or secondary based on RF conditions and traffic load, allowing optimization of the coverage-area-efficiency tradeoff through parameter adjustment
3Productivity
If dynamic cell assignment using machine learning is implemented, then network throughput is improved, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that processes RF condition metrics and traffic load data to determine optimal cell assignments, separating the complex decision-making logic from the network control plane
Data Source
AI summary
Data load allocation in a wireless network is provided herein. The system includes a user equipment (UE) and a cell. The method begins with determining radio condition metrics for a plurality of cells used for communication between the plurality of cells and the UE. A congestion metric is also determined for each cell of the plurality of cells. The radio condition metrics and the congestion metric for each cell of the plurality of cells is input to a neural network and a deep learning module. Based on the radio condition metrics and the congestion metric for each of the plurality of cells and the output from at least one of the neural network and the deep learning module, a first fraction of a data load is allocated to a first cell of the plurality of cells. A scheduler then schedules the first fraction of the data load for transmission.