Machine-Learning RAN Hardware Allocation Across Standards
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional static allocations of hardware components in telecommunications networks fail to adapt to changing device demands and technological shifts, leading to poor network performance and connectivity issues.
Innovation Solution
Implementing machine learning techniques to dynamically allocate hardware resources based on device distances, system capacity, and electrical power, using a system that generates commands to configure hardware components intelligently and adapt to changing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static allocations of hardware components are used in telecommunications networks, then hardware components can be shared amongst different network standards, but the system cannot adapt to changing device demands and technological shifts, leading to poor network performance
Solution Approach 1:
The patent implements dynamic hardware resource allocation by continuously monitoring network traffic patterns and device requirements, then adjusting hardware resource distribution in real-time. The system transitions from static to dynamic allocation mechanisms, allowing the network to adapt to changing device demands and technological shifts while maintaining reliable network performance through automated resource reconfiguration.
2Productivity
If hardware components are shared amongst different network standards using static allocations, then hardware utilization is simplified, but the system fails to respond to changing network conditions and device requirements
Solution Approach 1:
The patent employs feedback mechanisms where the system continuously monitors network traffic patterns, device requirements, and hardware utilization metrics. This feedback information is used to automatically adjust hardware resource allocations, ensuring the system responds effectively to changing network conditions while optimizing hardware utilization efficiency through data-driven decision-making.
Solution Approach 2:
The system dynamically changes allocation parameters such as hardware resource distribution ratios, priority levels, and capacity settings based on real-time network conditions and device requirements. This parameter adjustment enables the system to maintain high hardware utilization efficiency while adapting to evolving network standards and device demands.
3Reliability
If machine learning techniques are used to dynamically allocate hardware resources, then network performance is enhanced through real-time optimization, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically monitors, analyzes, and adjusts hardware resource allocations using embedded machine learning algorithms. This autonomous operation enhances network performance through real-time optimization while minimizing the need for external manual intervention, effectively managing system complexity through automated self-adjustment capabilities.
Data Source
AI summary
Systems and methods for dynamic hardware allocation for telecommunications networks. The system accesses one or more records indicative of network demand at a radio access network (RAN) node, wherein the RAN node is capable of supporting at least two network standards and comprises one or more hardware components for cell site infrastructure shared between the at least two network standards. The system may then extract, from the records, metrics indicative of network demand for each network standard and input the metrics into a machine learning model to determine a distance and directionality of each device from the node. Based on the distance, the system identifies an allocation of hardware for each of the at least two standards and generates one or more commands for enabling real-time modification of at least one hardware component to implement the allocation.