Wireless Network Mechanism Orchestration for Latency and Reliability
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Solution Overview
Problem
Current wireless communication systems face challenges in achieving ultra-reliable and low-latency communications, especially in long-distance scenarios, due to high computational resource requirements and delays, which are not efficiently managed by existing proactive and reactive approaches.
Innovation Solution
A method and device for orchestrating the execution of mechanisms in a wireless network by receiving quality of service indicators, determining initial and current orchestration strategies based on latency data, and dynamically activating resources and mechanisms to balance latency, reliability, and efficiency, using a combination of proactive and reactive approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proactive approaches are used to ensure ultra-reliable communication by considering worst-case scenarios, then reliability is improved, but resource cost and latency increase significantly
Solution Approach 1:
The patent implements dynamic adaptation of communication parameters and resource allocation based on real-time channel conditions. The system transitions from static proactive configurations to dynamic reactive adjustments, allowing the network to optimize between reliability and latency by adapting mechanisms such as modulation schemes, coding rates, and resource block allocations according to actual transmission conditions rather than worst-case assumptions.
Solution Approach 2:
The system changes communication parameters dynamically based on observed channel quality and transmission outcomes. By adjusting parameters such as transmit power, modulation order, and coding rate in response to real-time feedback, the system achieves reliable communication without consistently over-provisioning resources for rare worst-case scenarios, thereby reducing average latency and resource consumption.
2Productivity
If reactive approaches are used to activate additional resources adaptively, then resource efficiency is improved, but complexity of resource management increases
Solution Approach 1:
The patent segments resource management into distinct functional modules and layers, separating channel quality assessment, parameter selection, and resource allocation tasks. This modular architecture reduces overall system complexity by allowing each module to operate independently with well-defined interfaces, making the reactive resource management system more manageable and easier to implement while maintaining high resource efficiency.
Solution Approach 2:
The system implements feedback mechanisms where transmission outcomes and channel conditions are continuously monitored and used to adjust resource allocation decisions. This closed-loop control enables automated adaptive resource management without requiring complex manual configuration, as the system self-adjusts based on observed performance metrics, thereby improving resource efficiency while keeping management complexity manageable through automation.
3Reliability
If transmission parameters are adapted based on channel quality prediction, then communication performance is improved, but computational time and resource requirements increase
Solution Approach 1:
The patent pre-calculates and stores optimal parameter configurations for various channel conditions in lookup tables or predefined policy sets. When channel quality is assessed, the system quickly retrieves pre-determined parameter settings rather than performing complex real-time optimization calculations, significantly reducing computational time while maintaining improved communication performance through adaptive parameter selection.
Solution Approach 2:
The system performs channel quality assessment and parameter adaptation only when necessary, rather than continuously optimizing all transmission parameters at full complexity. By applying adaptation selectively based on channel condition thresholds or transmission criticality, the system achieves performance improvements in challenging conditions while minimizing unnecessary computational overhead during favorable conditions, thereby balancing performance gain with computational time consumption.
Data Source
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AI summary
A method for orchestrating the execution of a plurality of mechanisms by one or more nodes in a wireless network during a mechanism orchestration strategy execution time, characterized in that the method comprises the steps of: - receiving (200) at least one target value of a quality of service indicator; - determining (201) an initial mechanism orchestration strategy defining a minimum number of mechanisms and a minimum number of resources per node; - executing (202) said initial mechanism orchestration strategy by said one or more nodes; - receiving (203) end-to-end latency data; - determining (204) a statistical distribution of end-to-end latency from said data; - determining (205) a number of network state classes using said statistical distribution of end-to-end latency;- determine (206) a time threshold and a confidence level associated with each network state class from said end-to-end latency statistical distribution; - determine (207) a current mechanism orchestration strategy; - execute (209) said current mechanism orchestration strategy by said one or more network nodes taking into account the communication status of each node.