Mobile Network Policy Feedback for Privacy-Preserving ML Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mobile communication networks lack an adequate feedback structure for reinforcement learning, particularly in managing state vectors, which hinders the optimization of network behavior and quality-of-service.
Innovation Solution
A method involving a first network element transmitting policy data with an identifier to a second network element, where the second element generates state information and computes a machine-learning output, which is then used to derive actions, while ensuring privacy preservation through partial status vector transmission and model inference, allowing multiple models to be input simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete state information is transmitted for machine-learning optimization, then network optimization accuracy is improved, but privacy security deteriorates due to exposure of sensitive user data
Solution Approach 1:
The patent extracts and transmits only the necessary state information components required for machine-learning optimization while leaving sensitive user data behind in the network element. This selective extraction allows the UE to perform local inference without exposing private information, resolving the contradiction between accuracy and privacy.
Solution Approach 2:
The patent introduces an intermediary mechanism where the network element provides policy data and partial state information to the UE, which then computes the machine-learning output locally. This intermediary approach enables optimization without direct exposure of sensitive data, balancing accuracy requirements with privacy protection.
2Adaptability or versatility
If multiple machine-learning models are deployed for comprehensive optimization, then network optimization capability is improved, but device complexity increases
Solution Approach 1:
The patent enables the UE to autonomously manage multiple machine-learning models locally, performing self-service model selection and execution based on current network conditions. This distributes the complexity management burden from the network to the user equipment, allowing comprehensive optimization without proportionally increasing network-side complexity.
Solution Approach 2:
The patent segments the machine-learning functionality by deploying different models for different optimization tasks (e.g., one model for handover decisions, another for resource allocation). This segmentation allows comprehensive optimization capability while organizing complexity into manageable, task-specific modules that can be independently managed.
Data Source
Figure 1~2
Figure 3
AI summary
The present invention relates to a method for providing communication services, especially for modifying network behavior and/or for optimizing network performance and/or quality-of-service within a mobile communication network, wherein the mobile communication network comprises, or is assigned to or associated with, an access network and wherein the mobile communication network comprises, or is assigned to or associated with, a core network, wherein furthermore a plurality of user equipments are connected, or able to be connected, to the mobile communication network, wherein the access network as well as the core network comprise a plurality of network elements and wherein furthermore the user equipments likewise comprise or are considered as network elements, wherein in order to provide communication services, a plurality of network elements are required to interact with each other, and wherein, in view of such interaction between a first network element and a second network element, a machine-learning approach is used, the machine-learning generally comprising, or using, policy data, wherein, based on pieces of state information as well as based on the policy data, a machine-learning output is able to be computed that allows to derive one or a plurality of actions or action items, wherein the method comprises the following steps: -- in a first step, the first network element transmits, to the second network element, specific policy data together with a policy data identifier information, -- in a second step, the second network element generates specific pieces of state information and computes, based on the specific pieces of state information as well as based on the specific policy data, a specific machine-learning output, wherein the second network element furthermore transmits the specific machine-learning output and the policy data identifier information to the first network element, -- in a third step, the first network element generates or derives, based on the specific machine-learning output, one or a plurality of actions or action items, and transmits the one or the plurality of actions or action items to the second network element.