5G Network Architecture for QoE Management via Layered Automation
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Solution Overview
Problem
The complexity of 5G network management and monitoring, particularly with the introduction of self-organizing networks (SON) and small cells, necessitates advanced solutions for optimizing network parameters and ensuring quality of experience (QoE) due to increased network complexity and the need for dynamic policy-management.
Innovation Solution
A network architecture comprising a planning/policies unit, management automation unit, physical parameter and measurement layer, parameter abstraction and performance metric layer, and smart virtual monitoring layer, which determines target parameters, optimizes network performance, and predicts future behaviors based on acquired measurement results and correlations, enhancing QoE and network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If self-organizing networks (SON) and small cells are introduced to optimize network parameters, then network automation and service quality management are improved, but device complexity and difficulty of detecting and measuring network behaviors increase
Solution Approach 1:
The network architecture is segmented into distinct functional layers: physical parameter acquisition layer, behavior determination layer, and validation layer. Each layer handles specific tasks independently, managing complexity through modular organization while maintaining high automation through coordinated operation of these segments.
Solution Approach 2:
The patent introduces intermediary components including a parameter correlation determination unit that mediates between raw network parameters and behavior validation, and a behavior determination unit that acts as an intermediary between measured parameters and QoE validation. These intermediaries simplify the overall system by breaking down complex automation tasks into manageable intermediate steps.
2Reliability
If multiple network parameters are monitored and optimized, then service quality management is improved, but measurement precision and difficulty of detecting and measuring network behaviors worsen
Solution Approach 1:
The patent extracts and focuses on specific target parameters for validation rather than attempting to measure and validate all possible network parameters simultaneously. The validation unit selectively monitors key parameters that directly impact QoE, improving measurement precision by concentrating resources on critical measurements rather than diluting them across all parameters.
Solution Approach 2:
The system dynamically changes measurement parameters based on network conditions and QoE requirements. The validation unit adjusts which parameters are measured and at what precision levels based on current network state, allowing high reliability service quality management while maintaining feasible measurement precision by adapting measurement strategies to actual needs.
3Productivity
If network parameters are optimized dynamically, then productivity and service quality are improved, but loss of information about network behaviors increases
Solution Approach 1:
The patent implements a feedback mechanism where the validation unit continuously monitors network behaviors and compares actual behavior against expected behavior patterns. This feedback loop ensures that information about network behaviors is preserved and used to guide further optimization decisions, preventing information loss while maintaining high productivity through efficient dynamic parameter adjustment.
Solution Approach 2:
The system performs preliminary determination of parameter correlations and expected behavior patterns before actual network optimization occurs. By pre-establishing the relationships between parameters and expected behaviors, the system prepares information structures that prevent information loss during dynamic optimization, ensuring that productivity gains do not come at the cost of behavioral information.
Data Source
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AI summary
The invention relates to a network architecture, comprising a planning/policies unit configured to determine at least one target parameter of a network and to provide quality of experience (QoE); a management automation unit including a self-organizing-network configured to optimize the at least one target parameter of the network; a physical parameter and measurement layer configured to acquire a plurality of network parameters of the network and a plurality of measurement results of the network from the management automation unit; a parameter abstraction and performance metric layer configured to subsume the plurality of network parameters into a plurality of resources based on correlations among the plurality of network parameters and to determine a performance metric corresponding to each of at least one network parameter in each of the plurality of resources based on the acquired plurality of measurement results; and a smart virtual monitoring layer configured to determine behaviors of the. network based on the determined performance metric and the plurality of resources, to validate the behaviors of the network, which are effected by the optimized at least one target parameter, based on the QoE as references of determining the behaviors of the network, and to transmit the behaviors of the network to the planning/policies unit for enabling the planning/policies unit to determine the at least one target parameter based on the behaviors of the network.