Service-Aware Wireless Resource Scheduling via Dynamic Weights
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Solution Overview
Problem
Conventional scheduling methods in wireless communication networks are inflexible and inadequate in dynamically assigning radio resources, leading to inefficiencies and inability to schedule all requesting user equipment, especially in fully loaded networks, despite efforts to maximize user and operator value.
Innovation Solution
A service-aware scheduling method that maps service performance to user and operator values, using a scheduling weight function to prioritize resource allocation based on the relative value increase, considering factors like throughput, delay margin, and spectral efficiency, to optimize resource usage across different service types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional scheduling methods are used to allocate radio resources, then the scheduling process is simple and straightforward, but the system cannot dynamically adapt to changing network conditions and user requirements, leading to inefficient resource utilization
Solution Approach 1:
The patent implements dynamic scheduling weights that are continuously adjusted based on real-time network conditions, user equipment status, and service requirements. The scheduler dynamically modifies allocation decisions rather than using fixed rules, allowing the system to adapt to changing conditions while maintaining manageable complexity through structured weight adjustment mechanisms.
Solution Approach 2:
The patent changes the scheduling parameters by introducing multiple adjustable weights (throughput weight, delay weight, fairness weight) that can be modified based on network conditions. By varying these parameters dynamically, the scheduler achieves adaptability without requiring a completely complex restructuring of the scheduling mechanism.
2Productivity
If radio resources are allocated to maximize user value, then user satisfaction improves, but the complexity of determining and optimizing value increases significantly
Solution Approach 1:
The patent transforms the complex value optimization problem into a parameter adjustment problem by using scheduling weights that represent different value dimensions (throughput, delay, fairness). The scheduler adjusts these weights to achieve optimal throughput without requiring complex value calculation models, thereby improving productivity while controlling complexity.
Solution Approach 2:
The patent introduces scheduling weights as intermediary parameters that mediate between raw network metrics and final scheduling decisions. These weights serve as intermediate variables that simplify the optimization process by converting complex value assessments into adjustable parameters that directly influence resource allocation.
3Productivity
If the network schedules resources for all requesting user equipment, then user coverage is maximized, but resource exhaustion occurs and system performance degrades
Solution Approach 1:
The patent uses dynamic weight adjustment to balance resource utilization and service quality. By modifying the fairness weight and other parameters based on current network load and user conditions, the scheduler maintains consistent service quality across different users while efficiently utilizing available resources, preventing both exhaustion and degradation.
Solution Approach 2:
The patent implements feedback mechanisms where the scheduler continuously monitors network conditions, user equipment status, and service quality metrics. This feedback information is used to dynamically adjust scheduling weights and allocation decisions, ensuring that resource utilization remains efficient while service quality consistency is maintained across the network.
Data Source
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AI summary
The invention relates to a method for service aware scheduling of wireless resources in a wireless network. According to the inventive method, a service request for service delivery of a service type is received (51). A performance to value relationship for the service type is determined (52) and approximated (53) with a scheduling weight function associated with the service type. Previous performance for service delivery related to this service request is evaluated and approximated with a performance estimate (54). A scheduling weight is determined (55) by introducing the performance estimate in the scheduling weight function. Resource scheduling is performed (58) following a step of comparing (56) scheduling weights for on-going service requests. The invention also includes a network node and a system for service aware scheduling of wireless resources in a wireless network.