Soft Preamble Assignment for Low-Collision Message 1 Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 4G/5G networks face inefficiencies in preamble allocation due to random selection methods, leading to collisions and underutilization of preambles, especially with the introduction of advanced ML capabilities in 5G Advanced and 6G networks, where UE activity and mobility are not adequately considered.
Innovation Solution
A soft preamble framework utilizing a machine learning engine to actively assign and dynamically update preambles based on UE activity and mobility statistics, ensuring efficient allocation and minimizing collisions by learning user behavior and adapting preamble selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If random preamble selection method is used, then implementation complexity is low, but preamble collision rate increases and resource utilization decreases
Solution Approach 1:
The system implements feedback loops where the network monitors UE activity statistics and mobility patterns, then dynamically adjusts preamble assignments. The machine learning engine continuously receives performance data and refines allocation decisions, creating a closed-loop system that adapts to changing conditions and resolves the contradiction between simple implementation and high reliability.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing UE activity statistics and mobility patterns before random access occurs. The machine learning engine proactively generates optimized preamble assignments based on historical data, so when Message 1 transmission is needed, the UE already has a tailored preamble assigned, eliminating collisions before they happen.
2Ease of operation
If random preamble selection method is used, then system operation is simple, but preamble resource utilization efficiency deteriorates
Solution Approach 1:
The machine learning engine performs self-service by automatically monitoring network conditions, analyzing UE behavior patterns, and dynamically reassigning preambles without manual intervention. The system serves itself by continuously optimizing resource allocation based on real-time statistics, maintaining ease of operation while dramatically improving resource utilization efficiency.
Solution Approach 2:
The system transitions from static random assignment to dynamic adaptive assignment. Preamble allocations are no longer fixed but continuously adjusted based on UE activity statistics and mobility patterns. This dynamic approach allows the system to maintain operational simplicity while maximizing resource utilization by assigning preambles based on actual network conditions and predicted UE behavior.
3Reliability
If machine learning-based soft preamble assignment is implemented, then preamble collision rate decreases, but system complexity increases
Solution Approach 1:
The machine learning engine acts as an intermediary layer between the network and UEs for preamble allocation. Instead of direct complex interactions between network controllers and multiple UEs, the ML engine mediates by processing UE activity statistics and mobility patterns centrally, then providing optimized preamble assignments. This intermediary approach manages system complexity while maintaining high reliability through intelligent, data-driven decisions.
4Productivity
If dynamic preamble reassignment is performed, then resource allocation efficiency improves, but signaling overhead increases
Solution Approach 1:
The system applies partial reassignment rather than complete reassignment for all UEs. The machine learning engine identifies only those UEs whose preamble assignments should be changed based on activity statistics and mobility patterns, performing selective updates. This partial action approach maintains high resource allocation efficiency while minimizing signaling overhead by avoiding unnecessary reassignment messages for UEs that don't require changes.
Data Source
AI summary
A method may include registering with a communication network, and transmitting user equipment activity information to a machine learning engine of the communication network. The method may further include receiving an assigned soft preamble from the machine learning engine based on the user equipment activity information. In addition, the method may include performing message 1 transmission using the assigned soft preamble based on a preamble load. Another method may include receiving user equipment activity information from a network element, and assigning a same soft preamble to a plurality of user equipment based on the user equipment activity information, and based on a map of a network describing preamble load on every preamble in a serving cell and neighboring cells. Additionally, updated user equipment activity information may be received, and the soft preamble may be dynamically updated. The updated soft preamble may then be assigned to the user equipment.