Adaptive Spectrum Control for Dynamic Network Slice Bandwidth Allocation
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Solution Overview
Problem
Current network slicing methods rely on static resource allocation plans, which fail to adapt to changing demands and varying service requirements, leading to underutilization of resources during low traffic periods and potential congestion during peak usage.
Innovation Solution
A method that involves receiving input from network consumers, extracting consumer intent, mapping it to Quality of Service (QoS) attributes, and assigning a consumer priority indicator for dynamic bandwidth allocation. This method generates and manages network slices, selects radio frequency bands, and allocates bandwidth adaptively based on real-time usage and priority indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static resource allocation plans are used for network slicing, then network configuration is simple and stable, but resource utilization is inefficient and cannot adapt to changing demands
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring network traffic patterns and automatically adjusting bandwidth assignments for network slices based on real-time demand. The system transitions from static predetermined allocation plans to a dynamic mechanism where resource allocation adapts to changing traffic conditions, enabling efficient utilization during peak hours and preventing waste during low-demand periods.
Solution Approach 2:
The system incorporates feedback loops that monitor network performance metrics and traffic patterns, using this information to automatically adjust resource allocation decisions. The feedback mechanism enables the network to learn from actual usage patterns and modify bandwidth assignments accordingly, resolving the contradiction between adaptability and complexity through automated feedback-driven optimization.
2Productivity
If fixed bandwidth allocation is assigned to each network slice, then resource allocation is straightforward and stable, but resources are underutilized during low traffic and congested during peak usage
Solution Approach 1:
The patent applies dynamic bandwidth adjustment where network slices receive different bandwidth allocations based on real-time traffic conditions and priority levels. During peak usage periods, high-priority slices receive increased bandwidth to maintain performance, while low-priority slices receive reduced allocation. During low-traffic periods, the system redistributes resources to maximize overall utilization efficiency.
Solution Approach 2:
The system changes the bandwidth parameter dynamically based on network conditions and consumer priority indicators. Instead of fixed bandwidth assignments, the system adjusts the bandwidth parameter in response to monitored traffic patterns, enabling the network to optimize both resource utilization efficiency and maintain reliability through prioritized service levels.
3Adaptability or versatility
If static service chains are preconfigured for each network slice, then deployment is simple and fast, but service optimization for specific QoS requirements is compromised
Solution Approach 1:
The patent segments service chains into modular components that can be dynamically assembled based on consumer requirements. Instead of configuring complete static service chains for each slice during deployment, the system uses standardized service function segments that can be automatically selected and assembled to meet specific QoS requirements, maintaining ease of deployment while enabling optimization.
Solution Approach 2:
The system implements dynamic service chain configuration where the service path and functions are automatically adjusted based on monitored network conditions and consumer QoS requirements. The service chain transitions from static preconfiguration to a dynamic assembly that adapts to changing demands, enabling optimization for specific requirements without complicating the initial deployment process.
Data Source
AI summary
Methods and systems for adaptive spectrum allocation are provided. An example method includes receiving an input from a network consumer of a network, extracting a consumer intent from the input data and mapping the consumer intent with one or more predetermined QoS attributes, assigning a consumer priority indicator to the network consumer based on the QoS attributes, generating a network slice and assign the network slice to the network consumer, selecting a radio frequency band, assigning the band to the network slice, allocating a first allotment of the band to the network slice, provisioning network service to the network consumer for at least one UE connected to the network slice to use the allocated allotment of the band, monitoring continuously real-time bandwidth usage by the network slice, and adjusting, automatically and adaptively, a total bandwidth allocated to the network slice during network