AI Flow Control for 5G Packet Splitting and Reassembly
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing flow control mechanisms in 5G and future cellular networks face challenges in managing data transfer rates and packet reassembly due to factors like radio environment conditions, buffer overflow, timing differences, and bandwidth mismatches, leading to complications such as reassembly timer expiration, out-of-window delivery, and buffer overflow, which conventional algorithms struggle to address effectively.
Innovation Solution
Implementing artificial intelligence and machine learning to manage flow control by training models with network data, historical conditions, and real-time feedback to optimize packet splitting and carrier selection, ensuring timely and efficient reassembly of packets at user equipment devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional flow control algorithms are used to manage data transfer rates, then basic packet transmission is maintained, but buffer overflow and reassembly failures occur due to timing differences and bandwidth mismatches
Solution Approach 1:
The patent implements feedback mechanisms where the network monitor continuously observes network conditions (bandwidth, latency, packet loss) and feeds this information back to the flow control algorithm. This enables dynamic adjustment of transmission parameters based on actual network state, preventing buffer overflow and reassembly failures while maintaining manageable complexity through automated closed-loop control
Solution Approach 2:
The flow control system transitions from static conventional algorithms to dynamic adaptive control. The system continuously adjusts packet transmission rates, buffer allocation, and reassembly parameters based on real-time network conditions, allowing it to respond to timing differences and bandwidth variations without requiring complex manual configuration
2Adaptability or versatility
If packet splitting is increased to manage flow control, then data transfer adaptability improves, but spectral efficiency decreases due to increased packet overhead
Solution Approach 1:
The system dynamically changes packet transmission parameters (packet size, splitting ratio, transmission interval) based on network conditions and traffic characteristics. By optimizing these parameters in real-time, the system achieves high adaptability to different network scenarios while minimizing packet overhead and maintaining spectral efficiency
Solution Approach 2:
The flow control algorithm applies packet splitting selectively rather than uniformly - only splitting packets when necessary based on buffer status, network conditions, and traffic patterns. This partial action approach maintains adaptability for complex scenarios while avoiding unnecessary packet fragmentation that would reduce spectral efficiency
3Productivity
If real-time flow control optimization is implemented, then user experience improves, but computational resources increase for AI/ML model processing
Solution Approach 1:
The system uses machine learning models to predict optimal flow control parameters in advance based on historical network data and traffic patterns. By pre-computing and caching prediction results, the system can quickly apply optimized parameters without heavy real-time computation, improving data transfer efficiency while limiting additional energy consumption
Solution Approach 2:
The flow control system implements self-optimization where the AI/ML model learns from accumulated network data and automatically improves its predictions over time. This self-learning capability reduces the need for extensive real-time computational resources as the system becomes progressively more efficient without additional energy input
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving a data packet for transmission over a wireless network to a user equipment (UE) device in data communication with one or more network elements over one or more carrier legs of the wireless network, receiving information about current network conditions, retrieving a set of packet splitting rules which define initial boundary conditions for a process of splitting packets into smaller data packets for transmission to the UE device, applying, in a machine learning model, one or more packet splitting rules of the set of packet splitting rules to split the data packet into a set of smaller data packets, based on the information about current network conditions in the wireless network, selecting, in the machine learning model, a flow control arrangement for the data packet, including determining a number of smaller packets to split the data packet, and selecting respective carrier legs of the one or more carrier legs of the wireless network for communication of respective data packets of the number of smaller data packets, and communicating the number of smaller data packets to the UE device. Other embodiments are disclosed.