Dynamic Spectrum Allocation for Low-Latency Stateful Services
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Solution Overview
Problem
Wireless networks face challenges in providing high-quality, low-latency stateful services across different network locations, particularly for latency-sensitive applications like autonomous driving and gaming, due to varying performance requirements and limitations in dynamic spectrum allocation and resource reallocation.
Innovation Solution
The implementation of dynamic spectrum allocation and state shifting, where a service optimization controller manages the allocation of radio access technologies and spectrum frequencies based on performance requirements, prioritizing higher frequency bands for latency-sensitive services and utilizing Multi-Access Edge Computing facilities to localize service execution and maintain low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If dynamic spectrum allocation is used to prioritize higher frequency bands for latency-sensitive services, then latency is reduced, but spectrum utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic spectrum allocation that adapts spectrum assignment in real-time based on service type, user equipment location, and network conditions. The service optimization controller dynamically adjusts spectrum allocation between latency-sensitive and latency-insensitive services, transitioning from static to dynamic resource management to simultaneously optimize latency performance and spectrum utilization efficiency.
Solution Approach 2:
The patent applies different spectrum allocation strategies to different services and locations. Higher frequency bands are allocated to latency-sensitive services while lower frequency bands serve latency-insensitive services. The system also implements location-aware allocation where spectrum resources are optimized based on user equipment position and mobility patterns, ensuring local optimization of both latency and utilization.
2Loss of time
If service state is shifted to Multi-Access Edge Computing facilities for low-latency access, then access latency is reduced, but device complexity increases
Solution Approach 1:
The patent segments service functionality between centralized cloud infrastructure and distributed Multi-Access Edge Computing facilities. By dividing service state management across multiple edge locations, the system reduces access latency for local users while maintaining centralized coordination. The service optimization controller manages state transitions between edge facilities without requiring complex changes to user equipment.
Solution Approach 2:
The service optimization controller acts as an intermediary that manages service state shifting between Multi-Access Edge Computing facilities. This intermediary handles the complexity of state management, location tracking, and coordination, shielding user equipment from complexity while enabling low-latency access through intelligent state placement at edge locations.
3Reliability
If resources are dynamically reallocated based on performance requirements, then service quality is improved, but control complexity increases
Solution Approach 1:
The patent implements a feedback-driven resource allocation system where the service optimization controller continuously monitors service performance, user equipment location, and network conditions. Based on this feedback, the controller dynamically adjusts spectrum allocation and service state placement to maintain high service quality. The feedback mechanism enables automatic adaptation without requiring complex manual control configurations.
Solution Approach 2:
The system enables self-service resource optimization where the service optimization controller automatically manages spectrum allocation and service state management based on monitored conditions. The system self-adjusts resource distribution according to service requirements and network state without external intervention, improving service quality while keeping control mechanisms manageable through automation.
Data Source
AI summary
Provided are systems and methods for performing dynamic spectrum allocation and state shifting in order to provide high quality stateful services to user equipment (“UE”) that access the stateful services from different network locations. The dynamic spectrum allocation and state shifting may include tracking mobility of a UE accessing a stateful service using a first allocation of spectrum from a first Radio Access Network (“RAN”), predicting continued stateful service access via a second RAN, determining latency requirements of the stateful service, selecting a second allocation of spectrum at the second RAN with a frequency range that provides a first amount of latency, transferring the stateful service state to a Multi-Access Edge Computing (“MEC”) location that provides a second amount of latency for services accessed via the second RAN such that the first and second amounts of latency satisfy the performance requirements of the stateful service.