Quantum Annealing Network Connection Control for 5G Load Balancing
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Solution Overview
Problem
Current 5G network connection control systems face challenges in efficiently managing the increased number of connection devices, leading to complex and inefficient rule design, and limited expandability when network changes occur.
Innovation Solution
A network connection control system and method utilizing a QUBO matrix processed by a quantum annealing algorithm to optimize connection configurations between user equipment and base stations, allowing for dynamic load balancing and adaptability to network changes without the need for repeated data collection and model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based manner is used for network connection control in 5G, then the system is simple to implement, but the rule design becomes complicated and inefficient due to increased number of connection devices
Solution Approach 1:
The patent replaces the traditional rule-based mechanical control system with a quantum annealing optimization system. The QUBO matrix formulation and quantum annealing processor substitute the manual rule design and execution mechanism, enabling automatic optimization of network connection configurations without complex human-designed rules.
Solution Approach 2:
The system changes the approach from static rule-based parameters to dynamic optimization parameters. By formulating the load balancing problem as a QUBO matrix with adjustable parameters (connection configurations, load distribution weights), the system can adapt to changing network conditions and device counts without redesigning rules.
2Adaptability or versatility
If deep-learning-based manner is used to deal with MLB problem, then the model can handle complex network scenarios, but the system lacks expandability when network field is replaced or changed
Solution Approach 1:
The quantum annealing optimization system provides a universal framework that can handle different network scenarios and configurations through a single QUBO formulation approach. The same optimization mechanism works across various network topologies and device distributions, eliminating the need for separate deep learning models for each scenario.
Solution Approach 2:
The system performs preliminary formulation of the optimization problem as a QUBO matrix that captures the essential relationships and constraints. This preliminary structuring allows the quantum annealing processor to directly optimize connections without requiring extensive data collection and model training phases that deep learning systems need.
3Quantity of substance
If the number of connection devices increases in 5G network, then the network capacity is improved, but the rule design becomes much more complicated and inefficient
Solution Approach 1:
The system enables self-service optimization where the quantum annealing processor automatically determines optimal connection configurations without human intervention. The QUBO matrix formulation allows the system to self-adapt to any number of devices, eliminating the need for humans to design and maintain complex rules as device counts increase.
Solution Approach 2:
The optimization system provides dynamic adjustment of connection configurations in real-time based on current network conditions and device distribution. Unlike static rules that become obsolete as device counts change, the quantum annealing system continuously adapts to maintain optimal load balancing regardless of network scale.
Data Source
AI summary
A network connection control system and method is provided. The network connection control system includes user equipments, base stations, a server and a processing unit. Each base station has a connection range and is configured to connect the user equipment located within the connection range. Each user equipment transmits a network parameter between it and every base station through the base station connected therewith to the server. The server generates a QUBO matrix according to all the network parameters and outputs the QUBO matrix to the processing unit. The processing unit processes the QUBO matrix based on a quantum annealing algorithm to obtain an optimized connection configuration of the user equipments and base stations. The server receives the optimized connection configuration and controls the connection range of each base station accordingly for making an actual connection configuration of the user equipments and base stations identical with the optimized connection configuration.