Neural Network Handover Decisions Using Terminal Mobility Profiles
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Solution Overview
Problem
Existing wireless communication systems face challenges in optimizing mobile terminal handovers due to the trade-off between frequency offset estimation precision for high-speed users and throughput, particularly in heterogeneous UE compositions, leading to sub-optimal cell performance.
Innovation Solution
A neural network-based approach is employed to predict throughput optimization degrees for different cell configurations, allowing for dynamic handover decisions based on terminal mobility profiles, thereby optimizing resource allocation for both high-speed and low-speed users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency offset estimation precision is improved for high-speed users, then data decoding accuracy is improved, but cell throughput is reduced
Solution Approach 1:
The patent applies dynamics by making the cell configuration adaptive rather than static. The network entity dynamically adjusts cell configuration parameters (such as reference signal density, subcarrier spacing, or cyclic prefix length) based on real-time terminal mobility profiles. This allows the system to optimize frequency offset estimation precision for high-speed users while maintaining efficient throughput for low-speed users, resolving the contradiction between measurement precision and productivity.
Solution Approach 2:
The patent applies local quality by tailoring cell configuration parameters to specific terminal mobility characteristics. Different terminals receive customized configurations based on their speed profiles - high-speed terminals get configurations optimized for frequency offset estimation, while low-speed terminals get configurations optimized for throughput. This localized optimization resolves the contradiction by allowing both high precision and high productivity in different contexts within the same cell.
2Reliability
If cell configuration is optimized for high-speed users, then frequency offset compensation is improved, but overall network efficiency is reduced
Solution Approach 1:
The system dynamically adjusts cell configuration based on terminal mobility profiles, switching between configurations optimized for high-speed users (better frequency offset compensation) and low-speed users (better network efficiency). This dynamic adaptation allows the network to maintain high reliability for frequency offset compensation when needed while preserving overall network efficiency through optimized resource allocation for different user types.
Solution Approach 2:
The patent changes physical layer parameters (such as reference signal configuration, subcarrier spacing, or cyclic prefix type) based on terminal mobility characteristics. By adjusting these parameters dynamically, the system improves frequency offset compensation for high-speed users without permanently sacrificing network efficiency, as low-speed users continue to benefit from configurations optimized for throughput.
3Ease of manufacture
If manual cell configuration is used, then implementation is simple, but adaptability to different mobility profiles is poor
Solution Approach 1:
The system implements self-service by automatically determining terminal mobility profiles and selecting appropriate cell configurations without manual intervention. The network entity autonomously monitors terminal behavior, classifies mobility patterns, and adjusts configurations accordingly. This maintains implementation simplicity while dramatically improving adaptability to different mobility profiles.
Solution Approach 2:
The system uses feedback mechanisms to continuously monitor terminal performance and mobility characteristics, then adjusts cell configuration parameters in response. This closed-loop control maintains ease of implementation through automated decision-making while achieving high adaptability to varying mobility profiles, as the system learns and responds to actual terminal behavior patterns.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method enhances cell throughput performance by dynamically allocating resources according to user speed profiles, improving overall network efficiency and reducing errors in data decoding.
Implementation Method 1
predicting, utilizing a trained neural network, for each cell configuration of a plurality of cell configurations related to mobility and throughput balance, a throughput optimization degree for said terminal mobility profile
Data Source
AI summary
There are provided measures for mobile terminal handover decision optimizations. Such measures exemplarily include, e.g. at a network entity of a first network cell providing network access to a terminal, obtaining information indicative of a terminal mobility profile of said terminal, predicting, utilizing a trained neural network, for each cell configuration of a plurality of cell configurations related to mobility and throughput balance, a throughput optimization degree for said terminal mobility profile, selecting, from said plurality of cell configurations, a cell configuration having a highest throughput optimization degree, and deciding, based on said highest throughput optimization degree whether to trigger a handover of said terminal from said first network cell to a second network cell.