AI-Based Handover Neural Network for 5G Wireless Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing handover procedures in wireless communication systems, particularly in 5G networks, are not optimized for individual situations of user equipment (UE) or base stations (BS), leading to inefficiencies and potential service disruptions due to factors like path loss, shadowing, and high-speed movements.
Innovation Solution
The implementation of an artificial intelligence (AI)-based handover method, which utilizes a neural network to determine the optimal target cell for handover based on measurement results from the UE, thereby adapting to individual situations and improving communication performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional handover procedures are used, then the handover process is simple and standardized, but it cannot adapt to individual situations of UE or BS leading to service disruptions
Solution Approach 1:
The neural network model enables the UE to autonomously determine handover decisions by processing measurement results and identifying target cells based on learned patterns from historical data, eliminating the need for complex network-controlled handover procedures and enabling adaptation to individual UE situations
Solution Approach 2:
The system changes the handover decision-making parameters from standardized network-controlled thresholds to AI-processed measurement results that adapt to individual UE characteristics, BS conditions, and environmental factors, allowing dynamic adjustment of handover criteria
2Reliability
If AI-based handover with neural network is implemented, then adaptability to individual situations improves, but configuration and management complexity increases
Solution Approach 1:
The complex neural network model configuration and management tasks are extracted from the UE and centralized at the network side (BS/OAM), where the model can be trained, updated, and validated without burdening the UE with complex configuration management
Solution Approach 2:
The network side acts as an intermediary that receives measurement results from the UE, processes them through the neural network model, and returns simplified handover decisions or target cell information to the UE, shielding the UE from model complexity
3Ease of manufacture
If conventional handover methods are used, then the system is easy to implement, but it causes service disruptions due to path loss, shadowing, and high-speed movements
Solution Approach 1:
The neural network model is pre-trained with extensive historical handover data and measurement results before deployment, enabling it to anticipate optimal handover decisions in advance for various scenarios including path loss, shadowing, and high-speed movement conditions
Data Source
AI summary
The present disclosure relates to a communication technique for fusing, with an IoT technology, a 5G communication system for supporting a higher a data transmission rate than a 4G system, and a system therefor. According to various embodiments of the present disclosure, a method performed by a base station of a serving cell in a wireless communication system may comprise the steps of: transmitting configuration information for an artificial intelligence (AI)-based handover to a terminal; receiving, from the terminal, a handover request to a target cell according to the AI-based handover; and transmitting, to the terminal, a configuration message for access to the target cell, in response to the handover request, wherein the target cell is identified on the basis of a neural network (NN) configured for the AI-based handover and a measurement result of the terminal.