AI Base Station and Beam Prediction for Low-Latency Handover
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Solution Overview
Problem
Existing millimeter wave communication systems face challenges with high propagation attenuation, power consumption, and complexity in beam management, particularly in high-speed scenarios where inter-BS handovers and beam switches occur frequently, leading to increased latency and overhead in traditional beam scanning and RRC re-establishment processes.
Innovation Solution
Employing an AI-based method to predict radio link failures (RLF) or handovers by analyzing radio link conditions, using deep learning to model complex radio environments and predict target base stations and beams, allowing for reduced candidate beam measurements and optimized handover processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beam scanning and RRC re-establishment processes are used in high-speed scenarios, then handover reliability is maintained, but handover latency and signaling overhead increase significantly
Solution Approach 1:
The patent performs candidate beam measurement and target base station prediction in advance before handover is actually needed. By using AI models to predict potential target base stations and pre-measure candidate beams, the system prepares handover information beforehand, eliminating the need for time-consuming beam scanning and RRC re-establishment processes during actual handover execution in high-speed scenarios
Solution Approach 2:
The patent uses AI models to create predictive copies of handover information, including target base station identification and candidate beam characteristics. These predicted handover parameters serve as pre-prepared copies that can be directly applied during handover execution, avoiding the need to perform complete beam scanning and re-establishment procedures from scratch
2Loss of time
If AI-based prediction is used to reduce candidate beam measurements, then handover latency and overhead are reduced, but measurement precision may be compromised
Solution Approach 1:
The patent introduces AI models as intermediary components that process radio link condition information and generate predictions about target base stations and candidate beams. These AI intermediaries translate complex measurement data into actionable predictions, reducing the number of physical beam measurements needed while maintaining accurate identification of optimal handover targets through learned patterns from training data
3Adaptability or versatility
If deep learning models are deployed for radio environment modeling, then adaptability to environmental fluctuations is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent performs extensive model training and environmental pattern learning in advance during offline phases. By pre-training deep learning models with historical radio environment data, the system captures environmental characteristics and fluctuations beforehand, allowing the deployed model to make accurate predictions with minimal real-time computational overhead, thus reducing online complexity while maintaining high adaptability
Solution Approach 2:
The deep learning models are designed to learn and adapt to radio environment characteristics autonomously from training data without requiring continuous manual intervention or complex real-time configuration. The models self-adjust to environmental fluctuations by processing input features and generating predictions independently, reducing the need for complex external control systems while maintaining high adaptability to changing conditions
Data Source
AI summary
The present disclosure relates to base station and beam joint prediction and handover assisted by artificial intelligence. There is provided a method for radio communication, comprising: predetermining that a radio link failure (RLF) or handover will occur by using an artificial intelligence (AI) model, wherein, the predetermination is based at least on radio link condition information related to a user equipment (UE), and the radio link condition information comprises at least information reflecting condition of a radio link between the UE and a serving base station (SBS).